Journal of Mechanical, Civil and Industrial Engineering
type: channel
description: Journal of Mechanical, Civil and Industrial Engineering (ISSN: 2710-1436) is an international peer-reviewed monthly journal published by Al-Kindi Center for Research and Development. The journal welcomes publications of high-quality papers on theoretical developments and practical applications in mechanical, civil and industrial engineering. Original research papers, state-of-the-art reviews, and high-quality technical notes are invited for publication. The scopes of the journal include, but are not limited to Mechanism Design, Manufacturing Engineering, Engineering Design, Electrotechnology, Fluid Dynamics, Thermodynamics, Automotive Engines, Engineering Mathematics, Mechatronics, Control Theory [...] Read More
publisher: Al-Kindi Center for Research and Development
language: en
publication name: Journal of Mechanical, Civil and Industrial Engineering
issn: 2710-1436
items: Construction Organization Optimization for Sustainable Green Buildings: The Roles of Energy Efficiency and Environmental Well-Being
items: Characterization and Engineering Evaluation of Tholeiitic to Calc-Alkaline Rock Assemblages in Achin District, Nangarhar Province, Eastern Afghanistan
items: Comparative Analysis of Aerodynamic Characteristics of NACA4412 and NACA23012 Airfoils at High Reynolds Number
items: AI-Driven Predictive Maintenance for Manufacturing and Aerospace Systems
items: Translating Passenger-Perceived Safety Quality into Operational Safety Priorities: Study of an Indonesian Urban Bus Service
items: Financial Performance and Strategic Management of VINCI and Bouygues: A Comparative Ratio Analysis (2020–2024)
items: Mechanical Characterization and Performance Evaluation of Functionally Graded Metallic Components for Advanced Engineering Applications
items: Mineralogical investigation of Chumbalak Pegmatites in Eastern Afghanistan Kunar province Chapadara
items: Assessment of Pedestrian Flow Characteristics on Selected Urban Walkways in Khulna Metropolitan Area, Bangladesh
items: AI-Driven Digital Twin Framework for Real-Time Optimization of Smart Manufacturing Systems in Industry 5.0
items: Machine Learning-Based Prediction of Porosity Formation in Laser Powder Bed Fusion of Mechanical Components
items: Smart Manufacturing Framework for Real-Time Process Monitoring, Predictive Maintenance, and Quality Control in Advanced Mechanical Production Systems
items: Thermal Performance Study of Additively Manufactured Compact Heat Exchangers for Industrial Energy Systems
items: Integration of Artificial Intelligence for Real-Time Monitoring and Process Control in Metal Additive Manufacturing Systems
items: A Study on Hybrid Manufacturing Systems Integrating Additive Manufacturing and CNC Machining for High-Precision Industrial Component Production
items: Additive Manufacturing of Lightweight, Fire-Resistant Alloys for Automotive and Aerospace Applications
items: AI-Integrated Structural Optimization Framework for Lightweight Heavy Fabrication Systems in Smart Manufacturing Environments
items: Blockchain-Integrated AI for Transparent and Adaptive Supply Networks
items: Utilizing LLM models for advanced automation, manufacturing operations
items: Gendered Patterns of Public Space Use: An Architectural Analysis of U.S. Cities
items: Industrial Power Load Forecasting for Grid Operation Using a CNN-Transformer-BiLSTM Model
items: Human-Robot Collaboration in Mechatronic Systems: Towards Ergonomic and Safe Mechanical Design
items: Study Experimental and Numerical of Cross-Laminated Bamboo with Epoxy Adhesive
items: Prediction of Fatigue Life of Asphalt Layers in Desert Environments Using Neural Networks and SHAP Analysis: Al-Kufrh Case Study
items: Predictive Maintenance in Smart Manufacturing: An IoT-Integrated Framework
items: A Spatial Sensitivity Analysis of Flood Control Capability in the Mandalika Special Economic Zone, Central Lombok Regency, using a GIS-Based Multi-Attribute Decision Making Approach
items: Analysis of Hydrological Station Rationalization in Dompu Regency
items: Optimization of Irrigation Water Allocation at The Meninting Dam to Substitute The Water Discharge to The Jangkok - Babak HLD
items: Mechanical Properties of Lightweight Concrete Incorporating Perlite, LECA, and Supplementary Materials
items: Sensitivity Analysis of Mock and NRECA Method Parameters on Water Availability in the Jangkok Hulu Watershed

Construction Organization Optimization for Sustainable Green Buildings: The Roles of Energy Efficiency and Environmental Well-Being
type: item
description: This study develops and tests an integrated explanation of how construction organization optimization is associated with sustainable green building performance through energy efficiency and across varying levels of environmental well-being orientation. Construction organization optimization is conceptualized as the coordinated configuration of governance, information, production planning, supply-chain integration, commissioning, and continuous improvement. The study addresses the continuing gap between green design intentions and realized project and operational outcomes. This cross-sectional quantitative study used a 22-item questionnaire administered to construction professionals. The final sample comprised 421 valid responses measured on a five-point Likert scale. The model was analyzed in SmartPLS 4 using partial least squares structural equation modeling (PLS-SEM) with 5,000 bootstrap resamples, including reliability and validity assessment, mediation analysis, moderation analysis, effect-size assessment, and predictive-relevance evaluation. The results show that construction organization optimization was positively associated with sustainable green building performance (β = .304, p < .001) and energy efficiency (β = .600, p < .001). Energy efficiency was positively associated with sustainable green building performance (β = .483, p < .001), and the specific indirect effect was consistent with complementary partial mediation (indirect β = .290, p < .001). The positive association between energy efficiency and sustainable green building performance was stronger at higher levels of environmental well-being orientation (interaction β = .176, p < .001). The model explains 36.0% of the variance in energy efficiency and 65.0% of the variance in sustainable green building performance, with satisfactory reliability, convergent validity, discriminant validity, and predictive relevance. The study connects construction organization design, energy-performance delivery, and…
subject: Research Article
  topic: section
subject: construction organization optimization
  topic: keywords
subject: sustainable green buildings
  topic: keywords
subject: energy efficiency
  topic: keywords
subject: environmental well-being
  topic: keywords
subject: building performance
  topic: keywords
subject: PLS-SEM
  topic: keywords
subject: green construction management
  topic: keywords
creator: Yiyu Wu
rights: Copyright (c) 2026 Journal of Mechanical, Civil and Industrial Engineering
date: 2026-09-08
publication date: 2026-09-08
starting page: 20
ending page: 41
doi: 10.32996/jmcie.2026.7.5.3

Characterization and Engineering Evaluation of Tholeiitic to Calc-Alkaline Rock Assemblages in Achin District, Nangarhar Province, Eastern Afghanistan
type: item
description: Magmatic lithofacies assemblages serve as crucial archives of lithospheric evolution, while simultaneously acting as high-durability geomaterials for heavy industrial infrastructure. This study presents an integrated petrological, whole-rock geochemical, and petrophysical characterization of the sub-alkaline intrusive suites within the Gaodara area (Achin District, Nangarhar Province, Eastern Afghanistan) to resolve their petrogenesis and engineering utility. Systematically sampled outcropping suites and adjacent country formations were evaluated utilizing polarized-light thin-section microscopy, X-ray fluorescence (XRF) spectrometry, and hydrostatic displacement densitometry. Petrographic analysis identifies a distinct plutonic-hypabyssal bimodal system. The intrusive core comprises phaneritic, unevolved olivine gabbros exhibiting relict hypidiomorphic-granular to ophitic fabrics dominated by calcic plagioclase, clinopyroxene, and fractured olivine. Conversely, the enclosing country rock framework consists of intermediate, fine-to-medium-grained calc-alkaline tonalites containing quartz, plagioclase, biotite, and hornblende. Whole-rock major-oxide geochemistry confirms a distinct sub-alkaline bimodal distribution. The primitive, mantle-derived gabbroic core displays a highly refined tholeiitic differentiation path characterized by low silica (SiO₂ down to 36.69 wt.%), prominent iron enrichment (Fe₂O₃ up to 18.31 wt.%), and elevated magnesium numbers (MgO up to 20.70 wt.%). Conversely, the surrounding tonalitic country rocks follow a more evolved calc-alkaline trend, reaching high silica parameters (SiO₂ up to 82.36 wt.%) and elevated potassium fractions (K₂O up to 4.09 wt.%). Geotechnical testing reveals that these crystalline microstructural arrays directly dictate the rock mass durability. The dense, interlocking tholeiitic fabric yields a highly compact rock matrix with a low mean effective water absorption of 0.13%, an elevated mean bulk density of 3.06 g/cm³, …
subject: Research Article
  topic: section
subject: Achin, Tholeiitic, Calc-alkaline, gabbro, Tonalite, Engineering evaluation, Petrography, Geochemistry
  topic: keywords
creator: Zahidullah Hemat
creator: Khairuddin Rasikh
creator: Lal Omar Momand
creator: Fareedullah Tasal
creator: Hamid Zaheer
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-08-04
publication date: 2026-08-04
starting page: 09
ending page: 19
doi: 10.32996/jmcie.2026.7.5.2

Comparative Analysis of Aerodynamic Characteristics of NACA4412 and NACA23012 Airfoils at High Reynolds Number
type: item
description: This study selects the NACA4412 and NACA23012 airfoils as research objects and conducts two-dimensional numerical simulations of their aerodynamic characteristics under a high Reynolds number condition of Re=1.9×10^6 using CFD-based fluid simulation software. The incompressible continuity and Navier–Stokes equations are solved via the finite volume method, combined with the k-ω SST turbulence model and a pressure-based solver. The lift coefficient, drag coefficient, and the distributions of pressure and velocity fields around the airfoils are systematically analyzed at attack angles of 0°, 6°, 12°, and 18°. The simulation results indicate that NACA23012 exhibits superior lift-to-drag characteristics and drag control capability within the low angle-of-attack range of 0–6°, while NACA4412 demonstrates higher lift potential and better lift sustainability within the high angle-of-attack range of 12–18°. The comparative analysis of the aerodynamic differences between the two airfoils provides data support for the selection and optimization of airfoils for aircraft operating under high Reynolds number conditions.
subject: Research Article
  topic: section
subject: NACA airfoil; High Reynolds number; CFD numerical simulation
  topic: keywords
creator: Gonghang Lin
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-07-31
publication date: 2026-07-31
starting page: 01
ending page: 08
doi: 10.32996/jmcie.2026.7.5.1

AI-Driven Predictive Maintenance for Manufacturing and Aerospace Systems
type: item
description: The age of intelligent engineering and Industry 5.0 has made the use of artificial intelligence (AI), machine learning (ML) and predictive maintenance (PM) increasingly common to boost the performance, sustainability and resilience of manufacturing and aerospace systems. Traditional maintenance strategies focus on scheduled checks or remedial maintenance, introducing the risk of unforeseen equipment failures, higher operating expenses, downtime, and system vulnerabilities. In this research, we introduce an intelligent and sustainable engineering framework by combining advanced machine learning methods with predictive maintenance strategies, aiming to enhance decision-making and operational efficiency in next-generation manufacturing and aerospace systems, while also leveraging asset management. To demonstrate the validity of the proposed framework, two benchmark datasets were used to the AI4I 2020 Predictive Maintenance Dataset, which is for smart manufacturing environments, and the NASA C-MAPSS Turbofan Engine Degradation Dataset, which is for aerospace engine health monitoring and remaining useful life prediction. The created methodology consists of data preprocessing, feature engineering, feature selection, developing machine learning models, optimizing hyperparameters, and comparing the performance of the models. Models such as Artificial Neural Networks, Random Forest, Support Vector Machine, XGBoost, LightGBM, CatBoost and Long Short-Term Memory (LSTM) networks are used for manufacturing failure prediction, and models like Random Forest Regressor, XGBoost Regressor and Long Short-Term Memory (LSTM) networks are applied for the estimation of remaining useful life (RUL) in the aerospace industry. The accuracy, precision, recall, f1 score, roc-auc, mean absolute error (mae), root mean square error (rmse), and coefficient of determination (r2) are used to evaluate the performance of the model. The proposed framework will be expected to increase the accuracy of fau…
subject: Research Article
  topic: section
subject: Intelligent Engineering, Predictive Maintenance, Machine Learning, Smart Manufacturing, Aerospace Systems and Sustainable Engineering
  topic: keywords
creator: Umme Habiba Aesha
creator: Reyan Hridoy Bhuiyan
creator: Muhammad Ahnaf Jabeer
creator: Sumaya Mahajabin Khan
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2024-01-25
publication date: 2024-01-25
starting page: 50
ending page: 72
doi: 10.32996/jmcie.2024.5.1.7

Translating Passenger-Perceived Safety Quality into Operational Safety Priorities: Study of an Indonesian Urban Bus Service
type: item
description: Urban bus safety extends beyond accident records and vehicle condition, since passengers’ safety experiences during access, waiting, boarding, travelling and alighting also play an important role. The objective of this study is to develop an SQFD method that bridges the gap between the passenger-perceived safety quality and operational hazard priorities in Teman Bus Corridor 5: Palembang, Indonesia. Several SERVQUAL-based safety attributes were formed, and expectations, perceived safety conditions, and service gaps were measured from the data collected from 392 passengers. The operational-hazard indicators came from observations in the field, data that existed in the transport-safety literature at the time, input operators, and historical operational records. Passenger safety indicators were treated as WHATs, while operational hazard indicators HOWs in the House of Quality framework. A total of 17 drivers evaluated A total of 17 drivers assessed the relationships between passenger-side safety indicators and operational hazard-event indicators, while 6 operators examined relationships among operational indicators. Estimated safety gaps were negative in all attributes, but most so in boarding and alighting safety, attention to vulnerable passengers and personal security. Passenger overcrowding, component failure during operation and stop or access were the greatest operational concerns. These findings show that passenger-perceived safety deficiencies should not be treated merely as service complaints, but should be translated into operational hazard-event priorities through a structured SQFD framework. The study provides an indicator-based decision-support approach for improving safety management in road-based urban bus services.
subject: Research Article
  topic: section
subject: safety quality function deployment;
  topic: keywords
subject: passenger-perceived safety; operational safety priorities; urban bus service;
  topic: keywords
subject: house of quality;
  topic: keywords
subject: indonesia
  topic: keywords
creator: Nabila Hafisha Rizka
creator: Rhaptyalyani Herno Della
creator: Melawaty Agustien
rights: Copyright (c) 2026 Journal of Mechanical, Civil and Industrial Engineering
date: 2026-07-13
publication date: 2026-07-13
starting page: 37
ending page: 48
doi: 10.32996/jmcie.2026.7.4.5

Financial Performance and Strategic Management of VINCI and Bouygues: A Comparative Ratio Analysis (2020–2024)
type: item
description: This study examines the financial performance and strategic management practices of VINCI and Bouygues, two leading multinational companies in the engineering, construction, and infrastructure sector, from 2020 to 2024. The primary objective is to compare their financial performance, managerial effectiveness, and strategic orientations through a comprehensive financial ratio analysis, supplemented by an evaluation of the strategic narratives disclosed in their annual reports. A comparative research design is employed, using secondary data from the companies’ published financial statements and annual reports. Key financial ratios related to profitability, liquidity, leverage, efficiency, and investment activity are calculated and analyzed over a five-year period to identify performance trends and differences in financial management practices. The findings reveal that VINCI consistently achieved stronger profitability, superior cash-flow generation, and more stable leverage management, reflecting the strengths of its concession- and infrastructure-based business model. In contrast, Bouygues demonstrated higher asset utilization and pursued a more aggressive investment strategy focused on growth and diversification; however, this approach was associated with lower profitability, higher leverage, and greater volatility in debt coverage. The results further indicate a strong alignment between the financial indicators and the strategic narratives presented in the companies’ annual reports, suggesting consistency between managerial communication and actual financial performance. The study concludes that differences in business models and strategic priorities play a significant role in shaping financial outcomes and risk exposure within the engineering and construction industry. VINCI’s disciplined, cash-flow-oriented strategy contributed to stronger and more stable financial performance, whereas Bouygues adopted a more investment-driven approach aimed at long-term expansio…
subject: Research Article
  topic: section
subject: Financial Performance
  topic: keywords
subject: Strategic Management
  topic: keywords
subject: Financial Ratio Analysis
  topic: keywords
subject: Engineering and Construction Sector
  topic: keywords
subject: Financial Management
  topic: keywords
subject: VINCI
  topic: keywords
subject: Bouygues
  topic: keywords
creator: Sameer Usmani
creator: Abdul Tawab Alim
rights: Copyright (c) 2026 Journal of Mechanical, Civil and Industrial Engineering
date: 2026-07-13
publication date: 2026-07-13
starting page: 23
ending page: 36
doi: 10.32996/jmcie.2026.7.4.4

Mechanical Characterization and Performance Evaluation of Functionally Graded Metallic Components for Advanced Engineering Applications
type: item
description: Functionally graded metallic components offer a practical route for combining structural reliability with spatially tailored mechanical performance in advanced engineering applications. Unlike single-alloy parts, functionally graded materials (FGMs) allow stiffness, strength, hardness, thermal-expansion behavior, and damage tolerance to vary gradually across a component. This study presents a 2024-style numerical and analytical framework for the mechanical characterization and performance evaluation of a representative additively manufactured 316L stainless steel-Inconel 625 graded component. The framework combines gradient composition planning, additive manufacturing process-energy estimation, mixture-based property prediction, simplified residual-stress calculation, stress-strain response modeling, interface-risk assessment, and model-validation-style error visualization. A representative case study shows that the graded design reduces the normalized interface-risk index by approximately 37%, decreases peak residual-stress severity by about 26%, improves strength retention by 12%, and increases fatigue-readiness potential by 18% compared with a discrete bi-material interface. The results indicate that gradual material transition is not only a metallurgical concept, but also a mechanical design strategy for reducing abrupt property mismatch and improving load-transfer continuity. The proposed framework is intended as an early-stage design tool that can guide experimental planning, finite-element model development, and qualification-oriented testing of functionally graded metallic components.
subject: Research Article
  topic: section
subject: functionally graded materials; metallic additive manufacturing; mechanical characterization; directed energy deposition; 316L stainless steel; Inconel 625; residual stress; interface reliability
  topic: keywords
creator: James R. Thompson
creator: Emily K. Anderson
creator: Michael D. Carter
creator: Olivia M. Bennett
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2024-12-26
publication date: 2024-12-26
starting page: 26
ending page: 41
doi: 10.32996/jmcie.2024.5.3.3

Mineralogical investigation of Chumbalak Pegmatites in Eastern Afghanistan Kunar province Chapadara
type: item
description: Pegmatites represent a significant magmatic or segregated rock unit that constitutes a critical host for precious gemstones and rare-metal mineralization, rendering them a principal focus of economic geology research. This investigation presents a petrographic characterization of pegmatite bodies within the Chumbalak area, Darah Pech District, Kunar Province, eastern Afghanistan. Field reconnaissance confirmed the presence of numerous pegmatite veins, from which 5 representative samples were collected, comprising 3 from pegmatite bodies and two from the adjacent country rock. Petrographic analysis identifies quartz, K-feldspar, plagioclase, and mica as the dominant mineral assemblage, confirming the pegmatitic texture and composition, also accessory phases, notably schorl (Fe-rich tourmaline), muscovite, and biotite. Geochemical data indicate that the pegmatites were derived from the crystallization of residual, highly fractionated acidic melts. The enrichment of incompatible elements within these differentiated melts is consistent with their role as a source for rare-element mineralization.
subject: Research Article
  topic: section
subject: Pegmatite, Kunar Province, Dara Pech, Chumbalak, Petrographic, eastern Afghanistan
  topic: keywords
creator: Noorzai Safiullah
creator: Bilalzai Haseebullah
creator: Safi Riaz ul Haq
creator: Zahidullah Hemat
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-06-10
publication date: 2026-06-10
starting page: 13
ending page: 22
doi: 10.32996/jmcie.2026.7.4.3

Assessment of Pedestrian Flow Characteristics on Selected Urban Walkways in Khulna Metropolitan Area, Bangladesh
type: item
description: Walking is the most fundamental and proficient method of transportation for reaching a concise destination directly or after using other means at no cost. Hence, the significance of pedestrian movement is recognized worldwide and cannot be overlooked. The walking speed of pedestrians is noteworthy in an analysis of the function, layout, and provision of pedestrian facilities. Therefore, it is crucial to assess the pace of pedestrians using walkways as well as to review and authenticate the established link between their speeds, flow, densities, and area modules. Six strategically major locations in the Khulna metropolitan area, Bangladesh were selected. Data was gathered from these locations using a photographic approach and were analyzed using the statistical software IBM SPSS Statistics 32.0. The pedestrian walking statistics were examined for characteristics of flow under differing traffic circumstances and were illustrated through the development of mathematical and statistical relationships between speed-density, flow-density, speed-volume, and flow-area modules. According to the data, the typical pedestrian walking speed in Khulna City is 68.07 m/min on walkways. The walking speeds of children (69.68 m/min), adults (72.72 m/min), and elderly pedestrians (61.82 m/min) differ substantially from each other. The male pedestrian walking speed (71.23 m/min) is relatively faster than the female walking speed (61.8 m/min). Finally, when the space area per pedestrian is 1.70 m2, the maximum flow of pedestrians on walkways is estimated to be 36 ped/min. This refers to the level of service (LOS) “C”. The findings are expected to be beneficial in the design and planning of pedestrian infrastructure to ensure the continuous flow of pedestrians.
subject: Research Article
  topic: section
subject: Pedestrian flow characteristics, Walkways, Level of service, Pedestrian walking speed, Area modules
  topic: keywords
creator: Md. Mahmudun Nabi
creator: Md. Izharul Haque Azad
rights: Copyright (c) 2026 Journal of Mechanical, Civil and Industrial Engineering
date: 2026-06-09
publication date: 2026-06-09
starting page: 05
ending page: 12
doi: 10.32996/jmcie.2026.7.4.2

AI-Driven Digital Twin Framework for Real-Time Optimization of Smart Manufacturing Systems in Industry 5.0
type: item
description: There has been tremendous improvement in terms of automation and connectivity owing to the implementation of smart manufacturing as part of Industry 4.0; however, some issues continue to hamper the process of real-time adaptation, prediction, and decision making based on humans. Industry 5.0 represents the new era where there is the need for manufacturing systems that are not only resilient but sustainable and human-centric. This calls for frameworks combining AI in cyber-physical systems. In this paper, a novel Digital Twin with AI technology is introduced to provide an optimized approach for manufacturing systems following the Industry 5.0 guidelines. This approach consists of an intelligent multi-level architecture including physical systems, IoT-based data acquisition, digital model building, AI-based analytics techniques, and human-in-the-loop decision-making. It allows constant synchronization between the two worlds of physics and cyberspace, leading to proactive decision making and better system performance. A systematic approach is described here including data pre-processing, modeling, use of machine learning, and improvements to the system based on feedback. The analysis using simulation techniques shows the ability of the approach to increase efficiency, reduce downtime, and optimize predictive maintenance through real-time optimization. Additionally, human-AI interaction provides for flexibility in dealing with situations in manufacturing settings. The suggested solution addresses the main challenges that exist in existing Digital Twin applications by emphasizing such key factors as scalability, interoperability, and intelligence. This research provides a thorough conceptual framework that closes the gap between AI-powered analysis and manufacturing systems designed to serve humans. As it can be concluded from the results, Digital Twins enabled by artificial intelligence are expected to play a crucial role in future smart manufacturing.
subject: Research Article
  topic: section
subject: AI-driven Digital Twin, Smart Manufacturing, Industry 5.0, Real-Time Optimization, Human-AI Collaboration, Predictive Analytics
  topic: keywords
creator: Venkata Naga Kishore Thota
rights: Copyright (c) 2026 Journal of Mechanical, Civil and Industrial Engineering
date: 2026-06-03
publication date: 2026-06-03
starting page: 01
ending page: 04
doi: 10.32996/jmcie.2026.7.4.1

Machine Learning-Based Prediction of Porosity Formation in Laser Powder Bed Fusion of Mechanical Components
type: item
description: Porosity remains one of the most persistent quality barriers in laser powder bed fusion (LPBF), especially for mechanical components that must satisfy fatigue, leak-tightness, and structural reliability requirements. This paper presents a machine learning framework for predicting LPBF porosity formation using process parameters, powder descriptors, and physically meaningful features such as volumetric, linear, and areal energy density. The study uses a literature-constrained benchmark dataset to demonstrate the workflow when proprietary in-situ and X-ray computed tomography data are not yet available. The modeling strategy compares logistic regression, support vector machine, gradient boosting, random forest, and neural-network classifiers for high-porosity risk prediction, while a random-forest regressor estimates porosity percentage. The results indicate that tree-based ensemble learning provides the strongest balance between accuracy, recall, and interpretability, with volumetric energy density, scan speed, oxygen level, and powder morphology emerging as influential predictors. The discussion connects these model behaviors to lack-of-fusion, keyhole, spatter, and gas-entrapment mechanisms. Overall, the manuscript argues that reliable porosity prediction should not rely on machine settings alone. A more useful approach combines physics-guided feature engineering, sensor-informed data streams, cross-validated learning, and explainability tools to support inspection prioritization and eventual closed-loop process control.
subject: Research Article
  topic: section
creator: Matthew J. Reynolds
creator: Emily R. Carter
creator: Daniel T. Mitchell
creator: Lauren M. Brooks
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2026-05-22
publication date: 2026-05-22
starting page: 25
ending page: 37
doi: 10.32996/jmcie.2026.7.3.4

Smart Manufacturing Framework for Real-Time Process Monitoring, Predictive Maintenance, and Quality Control in Advanced Mechanical Production Systems
type: item
description: This study develops an integrated smart-manufacturing framework that unifies real-time process monitoring, predictive maintenance, and predictive quality control within a closed-loop decision architecture for advanced mechanical production systems. The proposed framework combines multi-sensor acquisition, edge analytics, digital-twin state estimation, anomaly detection, remaining useful life (RUL) prediction, and quality-risk forecasting to support adaptive control of machining and assembly operations. Unlike fragmented monitoring strategies that treat maintenance and quality as separate functions, the present approach fuses equipment-health indicators and part-quality indicators into a common risk score used for supervisory decision-making. A simulation-based industrial case study was constructed to emulate a high-mix mechanical production cell with CNC machining, in-process sensing, and end-of-line inspection. The case study was configured using literature-informed process logic and representative parameter bounds for spindle speed, feed rate, thermal load, vibration, current, and dimensional deviation. The results show that the integrated framework can detect degradation earlier than threshold-only monitoring, improve RUL tracking stability, and reduce quality escape by linking machine-state evolution to downstream defect probability. In the simulated evaluation, anomaly-detection F1-score increased from 0.79 to 0.94, RUL mean absolute error decreased from 21.8 to 12.6 cycles, and quality-prediction AUROC increased from 0.84 to 0.96. At the operational level, the proposed strategy reduced monthly unplanned downtime from 18.6 h to 10.8 h, lowered scrap rate from 4.8% to 2.2%, and increased overall equipment effectiveness from 71.2% to 81.6%. These findings indicate that a unified monitoring-maintenance-quality architecture can provide stronger production resilience and more economically efficient decision support than isolated digital initiatives. The manuscript i…
subject: Research Article
  topic: section
subject:
  topic: keywords
  value: Smart manufacturing; real-time monitoring; predictive maintenance; quality control; digital twin; condition monitoring; remaining useful life; machine learning; advanced mechanical production systems; Industry 4.0
creator: Md Arman Hossain
creator: Wei Pi
creator: S M Tarikul Islam
creator: Mohaiminul Islam Lide
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2021-04-27
publication date: 2021-04-27
starting page: 11
ending page: 24
doi: 10.32996/jmcie.2021.2.1.3

Thermal Performance Study of Additively Manufactured Compact Heat Exchangers for Industrial Energy Systems
type: item
description: Compact heat exchangers are central to industrial energy efficiency because they enable waste-heat recovery, thermal integration, and reduced equipment volume. Additive manufacturing (AM) is especially attractive for this application because it can produce thin walls, integrated manifolds, lattice cores, and triply periodic minimal surface (TPMS) channels that are difficult to realize with conventional fabrication routes. In this paper, a numerical thermal study is presented for three compact AM heat-exchanger concepts intended for industrial energy systems: a straight microchannel core, a wavy-channel core, and a gyroid TPMS core. All geometries were compared within the same overall envelope and material system in order to isolate the influence of internal architecture on heat duty, effectiveness, and pressure drop. A design-oriented ε-NTU framework coupled with Reynolds-number-dependent thermal and hydraulic correlations was used to evaluate performance for hot-side air inlet temperatures of 523 K and a water-side inlet temperature of 303 K. At the design point (hot-side mass flow rate of 0.035 kg/s), the straight-channel, wavy-channel, and gyroid designs delivered 3.90, 4.96, and 5.83 kW of heat duty, respectively. Corresponding effectiveness values were 0.50, 0.64, and 0.75, while pressure drops were 11, 18, and 29 kPa. The gyroid design therefore provided the strongest heat-transfer performance, but the wavy-channel core offered the best thermal-hydraulic compromise for practical retrofits. The results confirm that AM architectures can significantly improve compact heat-exchanger performance for industrial waste-heat recovery, although manufacturing cost, roughness control, and long-term reliability remain important barriers to commercial deployment.
subject: Research Article
  topic: section
subject:
  topic: keywords
  value: Additive manufacturing; compact heat exchangers; industrial energy systems; thermal performance; thermo-hydraulic performance; waste heat recovery; heat transfer enhancement; pressure drop; overall heat transfer coefficient; heat exchanger effectiveness; ε-NTU method; powder bed fusion; gyroid TPMS; surface roughness; process parameter optimization; energy-efficient thermal systems
creator: Md Arman Hossain
creator: Sundar Dangol
creator: Dewan Wardy Hasan
creator: Dilipkumar Badugu
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2023-12-16
publication date: 2023-12-16
starting page: 86
ending page: 103
doi: 10.32996/jmcie.2023.4.4.9

Integration of Artificial Intelligence for Real-Time Monitoring and Process Control in Metal Additive Manufacturing Systems
type: item
description: Metal additive manufacturing (AM) has attracted significant attention because of its ability to produce complex geometries, reduce material waste, and provide greater design flexibility than conventional manufacturing methods. However, its wider industrial use is still limited by process instability and defect formation during fabrication. Defects such as porosity, lack of fusion, residual stress, and distortion can reduce part quality and reliability, making real-time monitoring and process control increasingly important. This paper reviews recent progress through 2024 in the use of artificial intelligence (AI) for real-time monitoring and control in metal AM systems. The review shows that AI has improved anomaly detection, melt-pool analysis, defect prediction, and adaptive control performance. It also highlights the growing role of sensor fusion and low-latency computing in supporting in-situ decision-making. Despite these advances, challenges remain in data availability, model generalization, interpretability, and industrial reliability. Overall, AI is playing an important role in advancing metal AM toward more intelligent, stable, and quality-assured manufacturing.
subject: Research Article
  topic: section
subject:
  topic: keywords
  value: Metal Additive Manufacturing; Artificial Intelligence; Real-Time Monitoring; In-Situ Sensing; Machine Learning; Deep Learning; Reinforcement Learning; Closed-Loop Control; Sensor Fusion; Melt Pool Monitoring; Anomaly Detection; Defect Prediction; Process Optimization; Edge Computing; Digital Twin; Quality Assurance
creator: Md Arman Hossain
creator: Md Abdul Aziz Bhuiyan
creator: Atiqur Rahman
creator: Dewan Wardy Hasan
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2024-09-17
publication date: 2024-09-17
starting page: 08
ending page: 28
doi: 10.32996/jmcie.2024.5.3.2

A Study on Hybrid Manufacturing Systems Integrating Additive Manufacturing and CNC Machining for High-Precision Industrial Component Production
type: item
description: This paper examines hybrid manufacturing systems that combine additive manufacturing and CNC machining for the production of high-precision industrial components. It focuses on architectures (in-process single-setup versus post-process multi-setup), machine configurations, material compatibility, and the process-physics factors that influence precision and surface finish. A benchmarking technique informed by literature is created, integrating standards-compliant language and inspection practices, and subsequently applied to specific part categories, including brackets, mold inserts with cooling channels, and nozzle-like precision channels. Quantitative data from primary studies indicate that hybridization can diminish geometric deviations from approximately 0.1 mm to around 10–20 μm on specific features, while facilitating sub-micrometer Ra through micro-milling and achieving approximately 1.5–2.5 μm Ra during in-process hybrid LPBF/milling within established wear limits. A sample cycle-time and cost model is shown using a timing example from a manufacturer datasheet and an open-access cost-model framework based on time-driven activity-based costing. There are suggestions for process planning (hybrid CAM), fixturing/registration, thermal/distortion control, and inspection procedures for polymer prototype materials, titanium alloys, aluminum alloys, and stainless steels.
subject: Research Article
  topic: section
subject:
  topic: keywords
  value: Hybrid manufacturing; additive/subtractive integration; laser powder bed fusion; directed energy deposition; CNC milling; surface roughness Ra; tolerances; process planning; CAM; fixturing; registration; distortion; metrology; ISO/ASTM standards; cycle time; cost model.
creator: Md Arman Hossain
creator: Shipon Chandra Barman
creator: Wei Pi
creator: S M Tarikul Islam
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2022-06-22
publication date: 2022-06-22
starting page: 24
ending page: 41
doi: 10.32996/jmcie.2022.3.2.4

Additive Manufacturing of Lightweight, Fire-Resistant Alloys for Automotive and Aerospace Applications
type: item
description: The current review has critically discussed the industrial preparedness of additive manufacturing (AM) in lightweight, fire-resistant alloys, and it is found that its so-called transformative potential is oversold in recent literature. Empirical research has pointed out continuous mechanical unreliability, anisotropy, and defect fragility, which have compromised operational reliability in aerospace and automotive systems. Long-term performance has also been limited by thermal instability, phase heterogeneity, and narrow process windows, and scalability is limited by the fact that post-processing is extensively dependent. Successes in methods like selective laser melting (SLM), electron beam melting (EBM), and directed energy deposition at the laboratory scale have scarcely been translated into reproducible industrial results and residual stresses, irregular microstructures, and quality assurance gaps remain. Alloy progression techniques such as alloy modification and coating on surfaces have not been fully tested to be accepted by regulations as fire resistant, indicating how difficult it has been to balance the mechanical, thermal and safety needs. Regulatory, certification and standardisation have also been found to hinder adoption of the industry, as well as high energy consumption and lifecycle inefficiencies. To mitigate these shortcomings, studies have laid more and more stress on scalable process optimisation, enhanced in-situ monitoring, predictive modelling and stringent life-cycle testing in real conditions. The cooperation of the manufacturers and regulators with the certification bodies has been confirmed to be critical towards creating standard qualification routes. Altogether, AM has shown conditional capability to form lightweight, fire-resistant alloy, but has not yet been able to become industrialised and thus is limited to niche or experimental use instead of providing the radicalising effect that is often advertised.
subject: Research Article
  topic: section
subject: Additive manufacturing, lightweight alloys, fire-resistant alloys, aerospace, automotive, mechanical reliability, thermal stability, process reproducibility, certification, industrial adoption
  topic: keywords
creator: Md Arman Hossain
creator: Shipon Chandra Barman
creator: S M Tarikul Islam
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2026-03-09
publication date: 2026-03-09
starting page: 06
ending page: 16
doi: 10.32996/jmcie.2026.7.3.2

AI-Integrated Structural Optimization Framework for Lightweight Heavy Fabrication Systems in Smart Manufacturing Environments
type: item
description: Heavy fabrication systems, such as those used in the mining, construction, off-highway vehicle, and industrial equipment industries, are usually developed through conservative engineering practices, resulting in overdesigns in terms of structure, leading to excessive material usage. Although such practices ensure the durability and safety of the structure, they also result in increased manufacturing costs, energy consumption, and carbon emissions. The existing optimization techniques, such as topology optimization and size optimization, may not consider the manufacturability constraints of the welded structure, as well as the lack of consideration of real-world operational feedback. This paper proposes a new framework for AI-assisted structural optimization for lightweight heavy fabrication systems in a smart manufacturing paradigm. This new optimization framework includes physics-based finite element methods, machine learning-based surrogate modeling, multi-objective optimization, manufacturability-based constraint embedding, and digital twin-based feedback control in a unified architecture. The optimization process targets the minimum weight design subject to static strength, vibration, fatigue, and weld-related design constraints, while manufacturability is ensured through thickness limits, weld accessibility, and assembly feasibility. The incorporation of operational sensor information aids in the continuous improvement of predictive models, thereby allowing structural optimization to become a dynamic process rather than a static activity limited to the design phase. The proposed method improves design iterations, reduces material usage, improves vibration response, and meets sustainability goals by incorporating embedded carbon impact modeling. This proposed method moves heavy fabrication engineering into an adaptive, intelligence-based, and sustainability-focused structural system that meets Industry 4.0 and Industry 5.0 concepts.
subject: Research Article
  topic: section
subject: AI, Fabrication, Industry 4.0, Industry 5.0, AI-assisted structural optimization
  topic: keywords
creator: Venkata Naga Kishore Thota
rights: Copyright (c) 2026 Journal of Mechanical, Civil and Industrial Engineering
date: 2026-03-13
publication date: 2026-03-13
starting page: 17
ending page: 24
doi: 10.32996/jmcie.2026.7.3.3

Blockchain-Integrated AI for Transparent and Adaptive Supply Networks
type: item
description: Global supply networks are now more prone to volatility, fragmentation, and trust issues, driven by geopolitical risks, sustainability pressures, regulatory demands, and supplier fragmentation. While Artificial Intelligence (AI) has made significant strides in predictive analytics and optimization, its capabilities are still restricted by data silos, lack of transparency, and data integrity issues in global supply ecosystems. On the contrary, blockchain technology offers decentralized trust, immutability, and transparent transaction tracking, but it does not provide adaptive intelligence and predictive capabilities for decision-making. The current research proposes the concept of Blockchain-Integrated Artificial Intelligence (BI-AI), which promises the capability for transparent and adaptive global supply networks. The proposed BI-AI architecture integrates the blockchain technology for building a transparent supply network with Artificial Intelligence for analytics, demand forecasting, risk prediction, and autonomous response capabilities. The current research proposes a multi-layer governance structure that integrates IoT technology for data acquisition, blockchain for building trust, AI for analytics and response, and compliance for regulatory demands. The strategic implications, implementation challenges, and future research directions are also discussed in the context of the proposed BI-AI architecture. The current research contributes to the emerging discussion on the concept of intelligent decentralized global supply ecosystems and offers opportunities for future research and empirical validation for the proposed architecture.
subject: Research Article
  topic: section
subject: Blockchain-enabled supply chains, Artificial intelligence integration, Transparent supply networks, Adaptive decision-making systems, Digital trust and governance
  topic: keywords
creator: Phani Kumar Gutta
creator: Subba Rao Katragadda
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-02-17
publication date: 2026-02-17
starting page: 01
ending page: 05
doi: 10.32996/jmcie.2026.7.3.1

Utilizing LLM models for advanced automation, manufacturing operations
type: item
description: With the high rate of digitalization in manufacturing operations, extensive data gathering and local optimization have been made possible; however, the fragmented nature of the decision-making process in manufacturing operations, in the domains of production, quality, and maintenance, still persists. The state-of-the-art automation and artificial intelligence solutions that are currently available in the market are largely specific to the task and do not possess the ability to understand unstructured knowledge, reason across diverse contexts, and provide transparent support for the decision-making process to engineers and operators. The recent advances in large language models (LLMs) open new opportunities to provide cognitive and semantic reasoning capabilities in manufacturing operations. In this paper, a conceptual and design-oriented framework is proposed to utilize large language models (LLMs) to facilitate sophisticated automation in manufacturing operations. The proposed architecture for utilizing LLMs in manufacturing operations considers LLMs as a cognitive orchestration and decision support layer, where enterprise and shop-floor systems, operational context knowledge, and analytical and optimization tools are integrated. The proposed framework has clearly defined functional operational roles for LLMs in manufacturing operations, such as production, quality, and maintenance, and a formalized human-in-the-loop approach to ensure accountability, safety, and regulatory compliance in manufacturing operations, along with governance and validation mechanisms to address the risk of unreliable results from LLMs. The research provides a manufacturing-related architectural perspective for the automation enabled by large language model (LLM) technology, highlighting the role of LLM technology in the facilitation of semantic interoperability, cross-functional coordination, and explicable decision-making. Although the research does not include any validation, the sugges…
subject: Research Article
  topic: section
subject: Large language models; Smart manufacturing automation; Human-AI collaboration; Manufacturing operations management; Industry 5.0
  topic: keywords
creator: Subba Rao Katragadda
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-02-09
publication date: 2026-02-09
starting page: 08
ending page: 14
doi: 10.32996/jmcie.2026.7.2.1

Gendered Patterns of Public Space Use: An Architectural Analysis of U.S. Cities
type: item
description: Public spaces in contemporary U.S. cities are often described as neutral and universally accessible; however, patterns of uneven use and exclusion persist across gender and social groups. While existing research has examined these disparities through social, cultural, and behavioral lenses, the role of architectural design itself in producing such outcomes remains insufficiently articulated. This paper addresses that gap by examining how specific spatial design decisions influence gendered patterns of occupation, comfort, visibility, and participation in public urban environments. Through an architectural analysis of public spaces in multiple urban contexts within the United States, the study evaluates how design variables—including spatial configuration, seating typologies, circulation paths, scale, and degrees of enclosure—shape user behavior and perception. Drawing on direct observation and comparative spatial assessment, the research demonstrates that these architectural elements systematically affect how different groups engage with public space. Rather than treating inequitable use as an external social condition, the findings show that architectural form actively structures inclusion and exclusion within the built environment. The paper contributes an architectural framework for evaluating public space performance through the lens of inclusivity. By translating observed patterns of use into design-based criteria, the research establishes a method for assessing how spatial decisions influence equity and accessibility within U.S. urban public spaces. This framework provides architects and urban designers with a structured approach for identifying design conditions that discourage participation and for understanding how relatively modest spatial adjustments can meaningfully improve public space performance. By reframing gendered experience as an architectural issue, this work advances design-based discourse on equity in public space and positions architecture as…
subject: Research Article
  topic: section
subject: Public Space Design
  topic: keywords
subject: gendered spatial experience
  topic: keywords
subject: architectural inclusivity
  topic: keywords
subject: urban design analysis
  topic: keywords
subject: u.s urban environments
  topic: keywords
creator: Joynal Abed
rights: Copyright (c) 2026 Journal of Mechanical, Civil and Industrial Engineering
date: 2026-01-29
publication date: 2026-01-29
starting page: 01
ending page: 07
doi: 10.32996/jmcie.2026.7.2.2

Industrial Power Load Forecasting for Grid Operation Using a CNN-Transformer-BiLSTM Model
type: item
description: In the time of AI era, Industrial power load gradually rising due to the rapid expansion of the chip manufacturing facilities. So that accurate forecasting of industrial power load is important to achieve efficient grid planning and overall energy management. But, due to the nonlinear, volatile and multi scale nature of industrial power load data, the conventional statistical model face challenges in forecasting efficiently. To address these challenges, a novel hybrid deep learning model, CNN-Transformer-BiLSTM has been proposed that integrates the feature extraction capacity of convolutional neural networks (CNN), the long-range dependency modeling of the transformer architecture and the sequential learning strength of bidirectional long, short-term memory (BiLSTM) networks. The CNN layers efficiently capture the local temporal patterns and feature correlations within the load data sets, Transformer layers employs self-attention mechanisms to model complex long-term dependencies and contextual relationships. The BiLSTM layer further enhances temporal representation by learning bidirectional dependencies, thus improving the overall prediction accuracy. Historical monthly industrial electricity load data from the U.S. Energy Information Administration (EIA) spanning over two decades are used to train and evaluate the model. The proposed model output has been compared with other standalone and hybrid deep learning models. The proposed CNN-Transformer-BiLSTM achieves superior forecasting accuracy with Mean Absolute Percentage Error (MAPE) of 1.23%, Root Mean Square Error (RMSE) of 1,276 MWh and Mean Absolute Error (MAE) of 1,040 MWh.
subject: Research Article
  topic: section
subject: Industrial Electricity Forecasting
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: BiLSTM
  topic: keywords
subject: CNN
  topic: keywords
subject: Transformer
  topic: keywords
subject: Time-Series Forecasting
  topic: keywords
creator: Remon Das
creator: Md Abdul Ahad Juel
creator: Md. Raisul Islam
rights: Copyright (c) 2026 Journal of Mechanical, Civil and Industrial Engineering
date: 2026-01-22
publication date: 2026-01-22
starting page: 16
ending page: 30
doi: 10.32996/jmcie.2026.7.1.3

Human-Robot Collaboration in Mechatronic Systems: Towards Ergonomic and Safe Mechanical Design
type: item
description: Collaboration between human and robot is rapidly increasing. The safety trend is moving from ‘keep humans out’ to ‘design for safe and intentional interaction.’ In mechatronic systems, how you design your hardware, where components are connected, where the end-effector is, how flexible or how tight everything is, how far apart components are, and where everything goes, is often what determines whether a human-robot collaboration can occur. The paper proposes a design-oriented framework for HRC mechatronic systems. It combines risk thinking and safety-by-design concepts of safety function planning. In this framework, rather than relying on real-world information or experiments, known safety-by-design, robot safety, and body design thinking can be combined into a process. The process can be applied to the requirements phase of design. The proposed design-oriented framework produces verifiable outputs, which can be a combined list of hazards and ergonomics, a relationship between hazards and design changes, and a plan to verify both simulation and documentation-based verification.
subject: Research Article
  topic: section
subject: Sarodagh, Chromite, Ophiolite, Goshta, Peridotite, Nangarhar, Jalalabad, Afghanistan
  topic: keywords
creator: Subodh Thallada
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2026-01-01
publication date: 2026-01-01
starting page: 01
ending page: 05
doi: 10.32996/jmcie.2026.7.1.1

Study Experimental and Numerical of Cross-Laminated Bamboo with Epoxy Adhesive
type: item
description: One of the most promising processed wood products today is cross-laminated timber (CLT). Another alternative to cross-laminated timber (CLT) is cross-laminated bamboo (CLB), which can address the challenges of using wood. Cross-laminated bamboo (CLB) is a floor and wall slab made from bamboo slats arranged at 90° angles. Variations in bamboo type and size were analyzed using ANSYS numerical modeling. ANSYS is a FEM software used for the simulation and analysis of engineering products and systems. The purpose of this study was to analyze the flexural strength (MOR), modulus of elasticity (MOE), and ductility of cross-laminated bamboo (CLB) as a floor and wall slab structure. This study investigated whether it could produce satisfactory results using numerical modeling. This study used a two-point method to obtain the modulus of elasticity (MOE), flexural strength (MOR), and ductility. The experimental cross-laminated bamboo (CLB) used Petung bamboo as the test specimen, measuring 1360 mm in length, 660 mm in width, and 44 mm in thickness. Epoxy/Melamine Formaldehyde (MF) glue was used as the adhesive. Numerical modeling of the CLB, with dimensions similar to those used in the experiment, involved modeling two bamboo variations: Petung bamboo and Apus bamboo. Epoxy/Melamine Formaldehyde (MF) glue was used for the numerical modeling. The test results for the cross-laminated bamboo (CLB) showed an average modulus of elasticity (MOE) of 14.56 GPa, an average flexural strength (MOR) of 17.15 MPa, and an average ductility of 5.01. The damage behavior of the CLB was characterized by cracks in the bamboo slats and peeling of the glue. Comparison of the difference between experimental and numerical values ​​of CLB Petung bamboo at the maximum load of 19.96% and the final loading of the deflection difference of 16.29%. In numerical terms, with different types of material variations, namely Petung bamboo and Apus bamboo, the deflection difference at maximum load and final load.
subject: Research Article
  topic: section
subject: Cross-laminated bamboo, MOE, MOR, Ductility, Ansys Software
  topic: keywords
creator: Muh. Nurul Islam Parhan
rights: Copyright (c) 2026 Journal of Mechanical, Civil and Industrial Engineering
date: 2026-01-07
publication date: 2026-01-07
starting page: 06
ending page: 15
doi: 10.32996/jmcie.2026.7.1.2

Prediction of Fatigue Life of Asphalt Layers in Desert Environments Using Neural Networks and SHAP Analysis: Al-Kufrh Case Study
type: item
description: This study develops a predictive framework using Artificial Neural Networks (ANN) combined with SHAP analysis to estimate the fatigue life of asphalt pavements in Al-Kufra, Libya, a desert environment characterized by extreme temperatures and daily thermal fluctuations. Field data from five roads were analyzed to evaluate the influence of asphalt content and air voids. The ANN model achieved high accuracy (R² = 0.91, MSE = 0.015), showing that asphalt content between 5.5–6.0% significantly improves fatigue resistance, while air voids above 4% reduce service life. SHAP analysis provided transparent interpretation of variable importance, confirming asphalt content as the most supportive factor and air voids as the most detrimental. This research represents the first integrated application in Al-Kufra, bridging local and global scholarship, and offers practical recommendations for sustainable pavement design in hot desert climates.
subject: Research Article
  topic: section
subject: Fatigue life, ANN, SHAP, asphalt content, air voids, desert pavements, Libya
  topic: keywords
creator: Mohammed Salih Huwaysh
creator: Mousa Muhammed Karbaj
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2025-12-29
publication date: 2025-12-29
starting page: 80
ending page: 89
doi: 10.32996/jmcies.2025.6.5.8

Predictive Maintenance in Smart Manufacturing: An IoT-Integrated Framework
type: item
description: The very swift evolution of Industry 4.0 technologies has transformed conventional manufacturing into data-centric, networked manufacturing systems where efficiency, reliability, as well as sustainability are prime considerations. Against this setting, predictive maintenance (PdM) has gained considerable momentum as a significant method for reduction in equipment breakdown as well as resource utilization optimization. The current paper discusses the application of Internet of Things (IoT)-empowered predictive maintenance in smart manufacturing systems, while indicating its possibilities for migration from reactive as well as preventive strategies to data-centric, proactive strategies. The research posits a conceptual strategy that utilizes IoT-enabled sensing, real-time communications, data analytics, as well as statistical models to assemble forecasts of equipment breakdowns prior to their commencement. Strategic benefits that encompass cost optimization, increased product output, as well as sustainability enhancement are discussed in concurrence with challenges in the form of interoperability, cybersecurity risks, organizational reluctance, as well as the lack in standardized frameworks. The discussion also involves predictive maintenance evolution towards prescriptive ones, integration in digital twins, as well as the human-centric Industry 5.0 approach. Considering that empirical adoption remains in budding stages, this research identifies the necessity for scalable architectures as well as future studies that converge technical innovations as well as commercial viability. Overall, this study places IoT-enabled predictive maintenance as the foundation upon which resilience, sustainability, as well as competitiveness in next-generation smart manufacturing systems can obtain.
subject: Research Article
  topic: section
subject: IoT-enabled Predictive Maintenance, Smart Manufacturing Systems, Industry 4.0, Industry 5.0
  topic: keywords
creator: Rohan Rajiv Joshi
creator: Shreyash Ruikar
creator: Sairaj Prakash Desai
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2025-12-20
publication date: 2025-12-20
starting page: 34
ending page: 39
doi: 10.32996/jmcie.2025.6.5.4

A Spatial Sensitivity Analysis of Flood Control Capability in the Mandalika Special Economic Zone, Central Lombok Regency, using a GIS-Based Multi-Attribute Decision Making Approach
type: item
description: The Mandalika Special Economic Zone (SEZ) on Lombok Island continues to develop as a national strategic area, particularly driven by rapid tourism expansion. Intensive land-cover changes, vegetation degradation, and increasing rainfall in recent years have contributed to the rise of recurrent flooding. These conditions highlight the need for a deeper assessment of flood sensitivity to support mitigation efforts and adaptive spatial planning. This study aims to evaluate the MADM scoring–weighting process, identify dominant parameters and the effects of weight variation on flood sensitivity, estimate the extent of vulnerability classes, and formulate mitigation recommendations for the area. Six parameters were used: rainfall, slope, elevation, land cover, river density, and population density. The Multi-Attribute Decision Making (MADM) method was applied using two weighting scenarios (35%–13% and 25%–15%), supported by visual verification based on morphology, color distribution, and administrative boundaries. The results indicate that medium-vulnerability zones dominate the study area, especially in transitional regions between lowlands and hilly terrain. Land cover and slope emerge as the most influential parameters, producing more stable and logical spatial patterns with weights of 0.30 and 0.25. Elevation and river density fall into the moderate-influence category, with weights of 0.15 and 0.12. Rainfall and population density show lower discrimination due to data limitations, with respective weights of 0.15 and 0.08. Land cover and slope also demonstrate the highest sensitivity, while elevation, rainfall, and river density show moderate sensitivity, and population density represents the lowest level. Medium vulnerability dominates the study area at 50.66%, followed by low vulnerability at 26.52% and high vulnerability at 22.83%. To reduce flood risk, government authorities and area managers should strengthen land-cover rehabilitation, improve spatial-planning regu…
subject: Research Article
  topic: section
subject: Flood Vulnerability
  topic: keywords
subject: Spatial Sensitivity
  topic: keywords
subject: Multi-Attribute Decision Making (MADM)
  topic: keywords
subject: Mandalika SEZ
  topic: keywords
creator: Wanda Iqro Fatra
creator: Ery Setiawan
creator: I Ketut Budastra
rights: Copyright (c) 2025 Journal of Mechanical, Civil and Industrial Engineering
date: 2025-12-24
publication date: 2025-12-24
starting page: 62
ending page: 79
doi: 10.32996/jmcie.2025.6.5.7

Analysis of Hydrological Station Rationalization in Dompu Regency
type: item
description: This study aims to rationalize the hydrological station network in Dompu Regency in order to establish an efficient, effective, and regionally representative hydrological monitoring system for the watershed. The analysis was carried out using the Kagan–Rodda Method, the Stepwise Method, and the World Meteorological Organization (WMO) Density Standard. The dataset consists of rainfall observations, river discharge data, and climatological variables sourced from the Nusa Tenggara I River Basin Authority I, the Indonesian Agency for Meteorology, Climatology, and Geophysics, and the Agency for Food and Horticultural Crop Protection. The results show that the existing hydrological station network does not meet the minimum World Meteorological Organization standards for station density and spatial distribution. Based on the Kagan Method, the ideal number of rainfall stations in Dompu Regency is 19, with an interpolation error of 1.01%. Meanwhile, the Stepwise Method indicates that the correlation between rainfall stations and water level gauging station remains low. According to WMO guidelines, Dompu Regency requires at least 93 rainfall stations, 8 water level gauging stations, and 8 climate stations to meet the representativeness standards for small mountainous island regions. Therefore, additional stations are needed along with the revitalization of hydrological equipment to improve data accuracy and the operational efficiency of the network.
subject: Research Article
  topic: section
subject: Rationalization, Hydrological Stations, Network Efficiency
  topic: keywords
creator: Yayak Fitra Dikatanaya
creator: Heri Sulistiyono
creator: I. Wayan Yasa
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2025-12-15
publication date: 2025-12-15
starting page: 01
ending page: 10
doi: 10.32996/jmcie.2025.6.5.1

Optimization of Irrigation Water Allocation at The Meninting Dam to Substitute The Water Discharge to The Jangkok - Babak HLD
type: item
description: Water resources are vital components for sustaining life and supporting economic development; therefore, their management must be carried out in an integrated and sustainable manner. The Lombok River Basin (WS Lombok), as one of Indonesia’s National Strategic River Basins, comprises 197 watersheds (DAS), 52 of which have been utilized for water supply. The imbalance of water distribution between the western region (which tends to have a surplus) and the southern region (which faces a deficit) necessitates an efficient and equitable water management system. One of the key solutions developed to address this issue is the construction of the Meninting Dam, designed to balance water availability through the High-Level Diversion (HLD) Jangkok–Babak Channel. This study aims to simulate and optimize water allocation from the Meninting Dam to several major irrigation areas, namely D.I. Sesaot, D.I. Jurang Sate Hulu, D.I. Jurang Sate Hilir, and D.I. Jurang Batu. The methodology includes hydrological simulation and water allocation optimization using a trial-and-error approach to achieve flow balance between the supplying rivers (Meninting, Jangkok, and Sesaot) and the HLD as the receiving system. The results indicate that water availability in each irrigation area reaches 2,480 L/s for D.I. Sesaot, 10,049 L/s for D.I. Jurang Sate Hulu, 8,596 L/s for D.I. Jurang Sate Hilir, and 4,536 L/s for D.I. Jurang Batu, with a total inflow to the HLD system of 13,970 L/s. Following the operation of the Meninting Dam, irrigation efficiency (K Factor) increased from 85.6% to 98.3% in D.I. Sesaot, and from 86.3% to 87.7% in the Jurang Sate–Jurang Batu system. Water distribution also shifted, with 61.4% of the flow directed to the HLD system and 38.6% to D.I. Sesaot, as the dam fully meets the irrigation demand in D.I. Sesaot. These findings demonstrate that integrated inter-watershed water management through the operation of the Meninting Dam and the HLD system significantly improves water…
subject: Research Article
  topic: section
subject: Meninting DAM
  topic: keywords
subject: Water Allocation
  topic: keywords
subject: Inter Basin Management
  topic: keywords
subject: Irrigation Efficiency
  topic: keywords
subject: Lombok
  topic: keywords
creator: Zainul Arifin
creator: HERI SULISTIYONO2
creator: YUSRON SAADI
rights: Copyright (c) 2025 Journal of Mechanical, Civil and Industrial Engineering
date: 2025-12-15
publication date: 2025-12-15
starting page: 11
ending page: 18
doi: 10.32996/jmcie.2025.6.5.2

Mechanical Properties of Lightweight Concrete Incorporating Perlite, LECA, and Supplementary Materials
type: item
description: Lightweight structural materials are increasingly essential in modern construction, particularly in seismic-prone regions, as they reduce self-weight while maintaining mechanical reliability and improving earthquake resilience. This study investigates the mechanical behavior of lightweight concrete mixtures produced using expanded perlite and Light Expanded Clay Aggregate (LECA), combined with stone powder and microsilica to enhance matrix densification, with Ordinary Portland Cement (Type I) and potable tap water used across all mixes. Four lightweight mixtures—C350L40P15, C300L60P5, C400L40P15, and C400L50P10—were designed and compared with a reference concrete. Results showed substantial reductions in compressive strength (35.4%–54.2%), with C400L40P15 achieving the highest value of 26 MPa. Indirect tensile strength decreased by 16.3%–32.5%, yet mixtures with higher perlite content demonstrated notable improvements, achieving up to 19.4% greater strength compared with lower-perlite. Flexural tensile strength declined by 28.6%–41.3% relative to the reference, with C400L40P15 again showing the best performance (4.5 MPa). The elastic modulus exhibited the largest reductions (54.3%–65.7%) due to lower density, though it increased with higher cement content and reduced water-to-cement ratio. Overall, the findings indicate that optimized perlite content, controlled density, and the incorporation of stone powder collectively enhance the tensile, flexural, and stiffness properties of lightweight concrete, highlighting its potential for structural applications in earthquake-prone areas where reduced density is crucial for improving seismic performance without severely compromising mechanical strength.
subject: Research Article
  topic: section
subject: Lightweight Concrete, LECA, Perlite, Superplasticizer
  topic: keywords
creator: Ali Akbar Akbari
creator: Atiqullah Tawakoli
creator: Abdul Tawab Alim
creator: Mohammad Arif Koshyar
creator: Mohammad Atif Koshyar
rights: Copyright (c) 2025 Journal of Mechanical, Civil and Industrial Engineering
date: 2025-12-20
publication date: 2025-12-20
starting page: 40
ending page: 51
doi: 10.32996/jmcie.2025.6.5.5

Sensitivity Analysis of Mock and NRECA Method Parameters on Water Availability in the Jangkok Hulu Watershed
type: item
description: The Jangkok watershed, as one of the watersheds with high utility, plays an important role as a supplier of supplementary water to the communities living in the surrounding area. If there is a decrease in base flow in the Jangkok Watershed, it will have a significant impact on the irrigation water supply in Jangkok-Babak-Jurang Sate, raw water supply, and industrial water in Mataram City and West Lombok Regency. Because the Jangkok watershed is widely used for low flow management in the form of river and spring water, water flow availability can be obtained using the F.J Mock and NRECA methods. Each parameter has its own sensitivity that can directly affect changes in water availability. The parameter with the greatest sensitivity is determined based on the average deviation percentage and discharge change. The initial stage involved a calibration process with Qdefault results for the Mock and NRECA methods of 1.261 m³/dt and 0.968 m³/dt, respectively. Next, sensitivity analysis was performed using calibrated default parameters with overestimate and underestimate conditions of 5%, 10%, 15%, and 20%. Based on the results of the sensitivity analysis of the F.J. Mock Method, the parameter with the highest sensitivity was the Groundwater Recession Constant (k) under an underestimate condition of 20%, which affected the increase in water availability discharge in January 2024 to 6.457 m³/dt with an average deviation of 245.90%. In the NRECA Method, the parameter with the highest sensitivity was Percent Sub Surface (PSUB) under the 20% underestimate condition, which influenced the increase in water availability discharge in January 2024 to 0.993 m³/dt with an average deviation of 20.08%.
subject: Research Article
  topic: section
subject: sensitivity analysis
  topic: keywords
subject: parameters
  topic: keywords
subject: Mock
  topic: keywords
subject: NRECA
  topic: keywords
subject: water availability
  topic: keywords
creator: Baiq Virginia Ayunni Gandhis
creator: Ery Setiawan
creator: Hartana Hartana
rights: Copyright (c) 2025 Journal of Mechanical, Civil and Industrial Engineering
date: 2025-12-15
publication date: 2025-12-15
starting page: 19
ending page: 33
doi: 10.32996/jmcie.2025.6.5.3