Asian Journal of Civil Engineering最新文献

{"title":"Hybrid machine learning and multi-objective water strider optimization for seismic performance enhancement of light steel keel frame–wall systems","authors":"Rabah Ismail","doi":"10.1007/s42107-026-01672-0","DOIUrl":"10.1007/s42107-026-01672-0","url":null,"abstract":"<div>\u0000 \u0000 <p>In the last few years, considerable development has taken place in the practices of civil engineering through the application of artificial intelligence-based methodologies. The accuracy of the results obtained has improved, the computation costs are reduced, and the application of the methodologies has resulted in sustainability as well as optimization of the complex civil engineering systems. The present research work has proposed a novel hybrid computational framework wherein the application of Machine Learning (ML) as well as Multi-Objective Water Strider Optimization (MOWSO) has resulted in improved seismic behavior of Light Steel Keel Frame-Wall systems. The artificial neural networks are trained in the present research work to accurately simulate the nonlinear seismic behavior of Light Steel Keel Frame-Wall systems. The accuracy of the results obtained is reflected by the value of R^2 being greater than 0.98. The results obtained in the present research work have resulted in a reduction of inter-story drift by 35%, improvement of energy dissipation capacity by 28%, as well as a reduction of materials by 20%. The results obtained in the present research work are accurate enough to satisfy the code requirements, reduce computation time by more than 90%, as well as robust performance. The proposed approach establishes an efficient, adaptive, and sustainable paradigm for next-generation seismic design of lightweight steel systems.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 7","pages":"3383 - 3398"},"PeriodicalIF":0.0,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modified adaptive weight multi-objective GWO algorithm for construction time-cost trade-off optimization problems","authors":"Dileep Kumar M,&nbsp;Rathinakumar V,&nbsp;Padma Nilesh Mishra,&nbsp;Bikash Ranjan Bag,&nbsp;T. C. Manjunath,&nbsp;Nageswara Rao Lakkimsetty","doi":"10.1007/s42107-026-01648-0","DOIUrl":"10.1007/s42107-026-01648-0","url":null,"abstract":"<div>\u0000 \u0000 <p>Time–Cost Trade-off Problems (TCTPs) are central to construction project planning, requiring decision-makers to identify optimal execution strategies that balance project duration with financial expenditure. Due to their combinatorial and multi-objective nature, TCTPs often demand advanced optimization techniques capable of simultaneously ensuring solution quality, diversity, and computational efficiency. This study proposes a Modified Adaptive Weight Multi-objective Grey Wolf Optimizer (MAWA-GWO) to address these challenges in an 18-activity construction project characterized by multiple modes and nonlinear time–cost relationships. The MAWA-GWO introduces an adaptive weighting mechanism that dynamically tunes leader influence and search intensity, thereby strengthening the algorithm’s exploration and exploitation capabilities throughout the optimization process. The performance of the proposed approach is benchmarked against two widely used metaheuristic-based multi-objective optimization Methods: the Multi-Objective Genetic Algorithm (MOGA) and the Random Key Variant for Time–Cost Optimization (RKV-TCO). Experimental results show that MAWA-GWO consistently outperforms both MOGA and RKV-TCO in terms of Pareto-front convergence, diversity, and dominance ratio. The algorithm produces a more comprehensive set of efficient time–cost trade-off alternatives for the 18-activity project, providing clearer insights for construction managers when evaluating scheduling and budgeting decisions. Overall, the findings highlight the superior robustness and optimization capability of MAWA-GWO, establishing it as an effective tool for complex construction management applications involving multi-objective decision environments.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 6","pages":"2895 - 2905"},"PeriodicalIF":0.0,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147959081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning–based seismic resilience optimization and rapid assessment of reinforced cement concrete bridge","authors":"Ujwala S. Ghodeswar,&nbsp;Pranita S. Bhandari,&nbsp;Yogesh S. Lanjewar,&nbsp;Shradhesh Marve,&nbsp;Pranali Dandekar,&nbsp;Lowlesh N. Yadav,&nbsp;Latika Pinjarkar,&nbsp;Nikita Hatwar","doi":"10.1007/s42107-026-01685-9","DOIUrl":"10.1007/s42107-026-01685-9","url":null,"abstract":"<div><p>After an earthquake, resilient transportation planners must estimate reinforced cement concrete bridge structural safety, recovery time, economic losses, and long-term sustainability under tight time and data restrictions. Traditional seismic assessment frameworks use nonlinear time-history analysis, simplified fragility curves, and unconnected loss or life-cycle models, which increase computational cost, fragment uncertainty treatment, resilience, cost, and environmental performance optimization. Current machine learning methods are faster but disregard system-level interconnections, recovery dynamics, and sustainability implications, restricting their application for policy-level decision-making. To address these limits, this study provides a unified machine learning–based prediction and optimization method for rapid RCC bridge seismic resistance, post-earthquake losses, and sustainability metrics assessment. The six-block analytical process ensures data continuity. First, a SeisBridge Diffusion Surrogate (SB-DS) imitates nonlinear seismic response utilizing graph-based structural representations and conditional diffusion models to reliably and uncertainty-awarely estimate engineering demand parameters at low computational Second, topology-aware message transmission utilizing a Graph Fragility and Damage Inference Network (G-FraDIN) translates these demands into component- and system-level damage state probabilities, surpassing standalone component fragility models. Third, for risk assessment, a Recovery–Loss Multi-Task Emulator with Conformal Uncertainty (ReLo-ConfMT) predicts repair time, direct and indirect losses, and functionality trajectories with statistically calibrated prediction interval Fourth, the hybrid ML–LCA HyLiSE-Bridge life-cycle carbon and energy estimates include seismic damage and repair trajectories. Fifth, a Resilience–Loss–Sustainability Multi-Objective Bayesian Optimizer (ReLoS-MOBO) discovers Pareto-optimal bridge designs and retrofits that balance resilience, economic risk, and environmental performance The Scenario-Consistent Policy Evaluator with Explainable Learning (SCOPE-XL) finds robust design solutions under stakeholder requirements and openly interprets key design drivers. The proposed paradigm decreases computational effort by orders of magnitude, improves prediction accuracy and uncertainty consistency, and offers resilience–sustainability trade-offs. Its integrated and interpretable nature aids emergency response planning, infrastructure investment, and sustainable bridge earthquake designs.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 7","pages":"3593 - 3609"},"PeriodicalIF":0.0,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative analysis of response of BI building with varying post yield stiffness ratio under NF and FF ground motion","authors":"Jitendra Gudainiyan","doi":"10.1007/s42107-026-01684-w","DOIUrl":"10.1007/s42107-026-01684-w","url":null,"abstract":"<div>\u0000 \u0000 <p>This study aims to examine the performance a base-isolated (BI) building with varying post-yield stiffness ratio (PYSR) to near-field (NF) and far-field (FF) ground motion in scaled and unscaled forms. The PYSR was varied as 0.05, 0.10, 0.15, 0.20, and 0.20. The ground motions were scaled to Zone IV as per IS 1893: 2016 (Part 1). Time history analysis was performed for various cases of the building and responses- storey displacement, IDR, storey acceleration and base shear were determined. It was found in the study that Lower PYSR (0.05) provides maximum isolation efficiency with minimum base shear and IDR and higher energy dissipation, but results in greater displacement demand. Higher PYSR values (0.20–0.25) increase force transmission with only marginal improvement in acceleration control, showing diminishing benefits beyond PYSR 0.15–0.20. NF ground motions produced higher force, and displacement demands due to pulse-type effects, making them critical for displacement-based design, whereas FF motions exhibited smoother responses and greater sensitivity to PYSR variation. An optimal PYSR range of 0.10–0.15 was found for NF ground motions while 0.10–0.20 was found to be suitable for FF ground motions, ensuring a balanced displacement control, force reduction, and energy dissipation.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 7","pages":"3577 - 3591"},"PeriodicalIF":0.0,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of soft storeys, facade components, and shear walls on the seismic behavior of high-rise RC buildings","authors":"M. S. Ujwal,&nbsp;N. C. Sanjay Shekar,&nbsp;Arya Prathap,&nbsp;C. B. Ranjan Gowda,&nbsp;N. Vibhashree,&nbsp;M. Karthik,&nbsp;G. Shiva Kumar","doi":"10.1007/s42107-026-01662-2","DOIUrl":"10.1007/s42107-026-01662-2","url":null,"abstract":"<div>\u0000 \u0000 <p>Seismic performance of reinforced concrete (RC) framed buildings is strongly influenced by architectural and structural features such as façade elements, soft storeys, and shear walls. These components interact in complex ways to modify stiffness distribution, force transfer mechanisms, and deformation demand, particularly in high seismic zones. This study presents a comprehensive comparative evaluation of the seismic behavior of RC framed buildings incorporating façade elements, vertical stiffness irregularities due to soft storeys, and shear wall systems. Eight three-dimensional 15-storey RC building models were developed and analysed using ETABS under gravity, wind, and seismic loading in accordance with IS 1893 provisions. Linear dynamic response spectrum analysis and nonlinear time-history analysis were employed to assess key performance indicators, including storey displacement, inter-storey drift, story shear, base reactions, and dynamic characteristics. In addition, a machine learning (ML) framework was developed using a dataset generated from numerical simulations to predict structural response parameters using Decision Tree, Random Forest, and Multi-Layer Perceptron (MLP) regressors. The results indicate that shear wall–equipped models exhibit significant reductions in roof displacement and inter-storey drift by approximately 60–70%, along with an increase in base shear capacity of 120–140% compared to frame-only configurations. Soft storeys were found to amplify deformation demand in the absence of shear walls, while their influence was effectively suppressed in shear wall–dominated systems. Base reaction heat map analysis revealed pronounced load concentration at shear wall locations, highlighting important foundation design implications. Among the ML models, the Decision Tree and Random Forest regressors demonstrated superior predictive accuracy (R² up to 0.95), whereas the MLP showed comparatively lower robustness. The study provides integrated performance-based insights for seismic design, retrofit decision-making, and data-driven structural response prediction of RC buildings in earthquake-prone regions.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 6","pages":"3177 - 3201"},"PeriodicalIF":0.0,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147959163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolution and performance of outrigger systems in tall buildings: insights into their mechanics and design","authors":"Saurabh Kasliwal,&nbsp;Santosh Sanap,&nbsp;Sandip Karale,&nbsp;Yogesh Gawande,&nbsp;Rohit Pote,&nbsp;Harshali Gaikwad,&nbsp;Ankita Gaikwad","doi":"10.1007/s42107-026-01674-y","DOIUrl":"10.1007/s42107-026-01674-y","url":null,"abstract":"<div>\u0000 \u0000 <p>The outrigger and belt truss system has emerged as one of the defining innovations in the structural design of tall buildings. By linking the central core with perimeter columns, it not only restrains lateral drift but also redistributes overturning moments and enhances resilience under wind and earthquake loading. This review follows the journey of outrigger technology from its early theoretical models and first single-level applications to the wide spectrum of multi-outrigger, damped, and hybrid solutions now available. Evidence from comparative studies shows that single outriggers typically reduce lateral drift by about 30 to 40%, while multiple levels of outriggers can achieve reductions of 40 to 60% along with 20 to 25% savings in material use compared to conventional frames. The introduction of damping devices has further advanced the system, raising effective damping by five to ten times and improving both seismic response and occupant comfort in slender towers. Hybrid systems, which combine outriggers with belt trusses, diagrids, or dampers, are proving equally valuable by balancing architectural flexibility with structural efficiency, in some cases lowering steel demand by 10 to 15%. Recent research emphasizes computational optimization, performance-based design, and sustainability, supported by codified provisions such as IS 16700:2023. Looking ahead, the field is moving toward smart outriggers with adaptive damping, AI-driven placement strategies, and integration into multi-hazard resilience frameworks. Collectively, these advances show how outriggers have evolved from auxiliary stiffeners into multifunctional systems that underpin the safety, serviceability, and sustainability of today’s super tall buildings.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 7","pages":"3413 - 3434"},"PeriodicalIF":0.0,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sustainable multi-objective scheduling of groundwater treatment plant construction using NSGA-III–TOPSIS","authors":"Bijay Kumar Singh,&nbsp;Jasvir Singh","doi":"10.1007/s42107-026-01666-y","DOIUrl":"10.1007/s42107-026-01666-y","url":null,"abstract":"<div>\u0000 \u0000 <p>The construction of groundwater pollution treatment plants presents a complex scheduling challenge due to the need to balance timely project delivery, cost-efficiency, carbon footprint reduction, and environmental risk mitigation. Traditional scheduling models often overlook these multidimensional sustainability objectives. This study proposes a novel hybrid optimization–decision-making framework integrating non-dominated sorting genetic algorithm III (NSGA-III) with the technique for order of preference by similarity to ideal solution (TOPSIS) to generate and rank Pareto-optimal construction schedules. A real-world case study of an 18-activity, 5 MLD groundwater treatment plant in North India is analyzed, considering three execution modes per activity—fast-track (FTM), balanced (BEM), and eco-sustainable (ESEM). Each mode is characterized by distinct durations, costs, carbon emissions, and risk levels. The NSGA-III algorithm efficiently explores the solution space, producing diverse Pareto fronts, while TOPSIS aids in selecting the most balanced schedule. Results demonstrate that the proposed framework outperforms existing methods (e.g., NSGA-II, MOPSO, MOACO) in terms of IGD, HV, and coverage metrics. Strong trade-offs and correlations are observed among the four objectives, highlighting the need for integrated decision-making in sustainable infrastructure development. This study offers a replicable, optimization-driven approach for policy-makers and practitioners to schedule treatment plant construction projects under sustainability constraints.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 6","pages":"3241 - 3262"},"PeriodicalIF":0.0,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147959067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Gaussian Process Regression–based framework for optimizing green wall plant selection and indoor air quality performance in sustainable buildings","authors":"Zain Nader Maghaireh,&nbsp;Hamza Moh’d AlAkash,&nbsp;Ayman Abu Hamdiya,&nbsp;Mohammad Ababnah","doi":"10.1007/s42107-026-01665-z","DOIUrl":"10.1007/s42107-026-01665-z","url":null,"abstract":"<div>\u0000 \u0000 <p>The paper proposes a framework for the optimization of green wall plant selection and indoor air quality performance based on Gaussian Processes Regression and applicable in the context of Jordan’s Mediterranean climate for sustainable buildings. The research combines building simulation tools such as Energy Plus and CONTAM with machine learning modeling for the assessment of pollutant reduction, temperature stability, and humidity control in spaces with modular green walls. The results show that the inclusion of vegetation systems in buildings reduces critical pollutants such as CO₂ by 28%, VOCs by 33%, and PM₂.₅ by 39% compared to baseline values. High accuracy in the prediction results was obtained by the GPR model with R² and RMSE values of 0.94 and 0.12, respectively, which demonstrates its capability in modeling nonlinear interactions and uncertainty in the built environment. Moreover, the results show that plant species and air change rate (ACH) are the most significant factors affecting indoor air quality performance, which validates the capability of the AI-driven framework for plant selection optimization in the built environment. This research demonstrates the dual benefits of incorporating green walls in buildings in terms of environmental and health benefits and promotes the importance of considering the integration of vegetation systems in buildings for the development of sustainable buildings and spaces in support of the United Nations’ Sustainable Development Goals 11: Sustainable Cities and Communities.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 6","pages":"3225 - 3240"},"PeriodicalIF":0.0,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147959164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-informed neural network for estimating the crack number of fiber-reinforced cementitious composites at high strain rates","authors":"Thien Phuc Nguyen,&nbsp;Tan Duy Phan","doi":"10.1007/s42107-026-01660-4","DOIUrl":"10.1007/s42107-026-01660-4","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate prediction of the crack number (<span>(:{N}_{cr})</span>) in fiber-reinforced cementitious composites (FRCCs) is essential because it captures how damage develops to ensure the safe design for structures made by FRCCs. Existing empirical and purely data-driven models often exhibit limited generalization capability due to heterogeneous material compositions and insufficient incorporation of fracture mechanics principles. This study proposes a physics-informed neural network (PINN) framework that integrates fracture-related physical constraints directly into the loss function to enhance prediction accuracy and robustness. A dataset comprising 268 experimental samples collected from published literature was used for model development and validation. By embedding known physical relationships during training, the proposed PINN improves generalization under limited data conditions and reduces physically inconsistent predictions. The PINN model achieves strong predictive performance (R² = 0.898 for training and 0.739 for testing). The PINN model exhibited 4.907% and 12.140% improvement in R² for training and testing datasets, respectively in comparison to the NN model. Moreover, partial dependence plot (PDP) analysis identifies fiber index, gauge length, and strain rate as the most influential parameters governing crack number of FRCCs.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 6","pages":"3135 - 3150"},"PeriodicalIF":0.0,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147959092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Durability and corrosion resistance of silica fume modified RCC under citric acid exposure through experimental analysis and multi objective optimization","authors":"Abhishek Mishra,&nbsp;Mukesh Pandey,&nbsp;Rakesh Gupta","doi":"10.1007/s42107-026-01667-x","DOIUrl":"10.1007/s42107-026-01667-x","url":null,"abstract":"<div>\u0000 \u0000 <p>Reinforced cement concrete (RCC) structures operating in agro-industrial and food-processing environments are highly vulnerable to organic acid attack, particularly from citric acid, which induces calcium leaching, matrix destabilization, and premature steel corrosion. This study experimentally investigates the durability and corrosion resistance of silica fume–modified RCC exposed to citric acid and identifies an optimal mix through multi-objective optimization. Six RCC mixes were prepared by partially replacing Ordinary Portland Cement with silica fume at 0–15% (by weight) and designed for M30 grade concrete. After 28 days of water curing, specimens were immersed in citric acid solutions of varying concentrations (2–10%) for up to 56 days. Mechanical performance, durability, and corrosion behavior were evaluated using compressive, split tensile and flexural strength tests, sorptivity, water absorption, ultrasonic pulse velocity, rapid chloride penetration, half-cell potential, steel weight loss, and pore solution pH measurements. Microstructural and chemical degradation mechanisms were examined through SEM, EDS, XRD, and FTIR analyses. Results reveal that silica fume significantly enhances matrix densification, reduces permeability, preserves alkalinity, and mitigates corrosion under acidic exposure. The mix containing 10% silica fume exhibited the best overall performance, achieving a compressive strength of 46.0 MPa, minimal concrete and steel weight loss, low chloride permeability, and superior electrochemical stability. A hybrid NSGA-III–TOPSIS optimization framework confirmed this mix as the optimal trade-off solution. The findings provide practical guidelines for designing durable RCC for organic acid–rich environments.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 6","pages":"3263 - 3283"},"PeriodicalIF":0.0,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147959109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}