Asian Journal of Civil Engineering最新文献

{"title":"Data-driven modeling of mechanical properties in polyethylene fiber-based engineered cementitious composites (PE-ECC)","authors":"Md Mehedi Hasan,&nbsp;Md Nasir Uddin,&nbsp;Md Sagar Akon,&nbsp;Abba Bashir,&nbsp;Borhan Uddin Rabbani","doi":"10.1007/s42107-026-01724-5","DOIUrl":"10.1007/s42107-026-01724-5","url":null,"abstract":"<div><p>Polyethylene fiber-based engineered cementitious composites (PE-ECC) offer exceptional compressive strength (CS), tensile strength (TS), ductility, crack control, and durability properties. Their mechanical performance depends on highly nonlinear interactions among the matrix composition, fiber geometry, and curing conditions. Accurately capturing these interactions using conventional experimental approaches is time-consuming and costly. This study developed a comprehensive and interpretable machine learning (ML) framework to predict the CS and TS of PE-ECC. An experimental database was constructed from published experimental studies incorporating the mixture proportions, fiber characteristics, and curing parameters. Six ML models (e.g., DT, RF, SVR, XGBoost, LightGBM, and CatBoost) were trained and optimized using systematic data preprocessing, outlier removal, feature standardization, and grid-search-based hyperparameter tuning. The model performance was rigorously evaluated using (e.g., R², Adj. R², MAE, RMSE, MSE, and SMAPE) supported by fluctuation, distribution, residual, and radar-based multi-metric analyses. Among the evaluated models, XGBoost demonstrated superior predictive accuracy and stability, achieving testing R² values of 0.93 and 0.90 for CS and TS, respectively. XGBoost proved to be the most effective method for this task, minimizing the prediction errors while capturing the nonlinear micromechanical interactions of PE-ECC more effectively than other ensemble and individual learners. SHapley Additive exPlanations (SHAP) was employed to ensure model interpretability and identify governing parameters. The results indicate that CS is primarily controlled by matrix-related variables, particularly the water-to-binder ratio (W/B), whereas TS is dominated by fiber-related parameters such as fiber length (L_f) and the GGBS-to-binder ratio (GS/B). To facilitate practical implementation, an interactive graphical user interface (GUI) was developed, enabling the rapid and reliable prediction of the mechanical properties of PE-ECC without coding or extensive laboratory testing. The proposed framework provides a robust, interpretable, and application-oriented tool for accelerating PE-ECC mix design, reducing experimental effort, and supporting the development of durable and sustainable cementitious composites.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 9","pages":"4303 - 4334"},"PeriodicalIF":0.0,"publicationDate":"2026-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148807445","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 seismic performance of LRB and HDRB systems in reinforced concrete buildings","authors":"Arcan Yanik,&nbsp;Demet Okuroğlu","doi":"10.1007/s42107-026-01722-7","DOIUrl":"10.1007/s42107-026-01722-7","url":null,"abstract":"<div><p>Among elastomeric seismic isolation devices, lead rubber bearings (LRB) and high damping rubber bearings (HDRB) are commonly preferred due to their reliable energy dissipation mechanisms. This study investigates and compares the seismic performance of LRB and HDRB isolation systems applied to a seven-story reinforced concrete building. A numerical model of the structure was developed in SAP2000 and analyzed through nonlinear time-history analysis. The seismic responses of fixed-base, LRB-isolated, and HDRB-isolated models were evaluated using the 1999 Kocaeli and 1992 Landers earthquake records scaled to the target design spectrum. To investigate the influence of structural capacity, two material strength scenarios were considered: one representing newly designed structures and the other representing existing buildings with reduced material strength. The results indicate that both LRB and HDRB systems effectively improve structural performance, while HDRB exhibits relatively better performance in reducing force and acceleration demands due to its higher inherent damping capacity. In both material strength scenarios, the isolated models demonstrated consistent reductions in seismic demand, confirming that the effectiveness of base isolation is governed primarily by isolator characteristics rather than structural material strength alone. These findings highlight the applicability of elastomeric isolation systems for both newly designed and existing reinforced concrete buildings subjected to seismic loading.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 9","pages":"4275 - 4283"},"PeriodicalIF":0.0,"publicationDate":"2026-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148807373","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":"Artificial intelligence for multi-objective optimization of time, cost, quality, and safety risks in construction projects","authors":"Vishnu Lakkamraju,&nbsp;Gyana Ranjana Panigrahi,&nbsp;Ramesh Chandra Sahoo,&nbsp;Tushar Savale,&nbsp;Tarun Madan Kanade,&nbsp;Abdul Habib Zaray","doi":"10.1007/s42107-026-01720-9","DOIUrl":"10.1007/s42107-026-01720-9","url":null,"abstract":"<div><p>Modern construction project planning increasingly requires the simultaneous optimization of multiple conflicting objectives, particularly time, cost, quality, and safety risk. This study presents an artificial intelligence-based multi-objective optimization framework for addressing the construction time–cost–quality–safety risk trade-off (TCQSRT) problem. A 37-activity project is considered as a case study, where each activity is associated with multiple execution modes characterized by different combinations of duration, cost, quality, and safety risk, resulting in a large-scale combinatorial optimization problem. To effectively solve this complex problem, an enhanced Aquila Optimizer (OBL–AO) integrated with opposition-based learning is proposed. The Aquila Optimizer, inspired by the hunting behavior of Aquila eagles, provides a balanced exploration and exploitation mechanism, while opposition-based learning improves population diversity and accelerates convergence by simultaneously evaluating candidate solutions and their opposite solutions. The proposed model simultaneously minimizes project duration, total cost, and safety risk while maximizing quality, generating a diverse set of Pareto-optimal solutions for decision-making. The performance of the proposed OBL–AO approach is evaluated using standard multi-objective metrics, including Spacing (Sp), Mean Ideal Distance (MID), and Hypervolume (HV). The results indicate that OBL–AO achieves a Sp value of 0.647, reflecting a competitive distribution of solutions across the Pareto front. In terms of convergence, it attains a MID value of 2.01, which is lower than NDSII-TLBO (2.21) and close to OBL-TLBO (1.97), demonstrating strong proximity to the ideal solution set. Most notably, OBL–AO achieves the highest HV value of 0.79 compared to OBL-TLBO (0.77) and NDSII-TLBO (0.74), indicating superior overall performance in terms of convergence and diversity balance. Overall, comparative analysis confirms that the proposed OBL–AO framework generates a more robust and well-distributed Pareto front than benchmark methods. These results demonstrate its effectiveness in producing high-quality trade-off solutions for the TCQSRT problem and highlight its potential as a reliable decision-support tool for improving sustainability, safety, and efficiency in modern construction project management.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 9","pages":"4261 - 4274"},"PeriodicalIF":0.0,"publicationDate":"2026-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148807364","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":"Composite brick from waste foundry sand and polymeric waste: a sustainable alternative to conventional bricks","authors":"Praveen Kumar Pandey,&nbsp;M. V. Raju","doi":"10.1007/s42107-026-01718-3","DOIUrl":"10.1007/s42107-026-01718-3","url":null,"abstract":"<div><p>The increasing demand for bricks and cement in the construction industry has resulted in significant depletion of natural resources, particularly water and soil, leading to severe environmental degradation. While the dry-pressed and stiff mud process indeed uses less water, the dominant conventional practices in India and many developing countries still involve the soft mud process, which requires large quantities of water. Hence, conventional brick manufacturing is highly resource-intensive, relying heavily on soil extraction and a large volume of water, thereby raising sustainability concerns. In response, this study introduces a novel approach by repurposing two underutilised industry byproducts, waste foundry sand (WFS) and polymeric thermoplastic waste (PTW), which consists of mixed post-consumer plastic, including Polyethene terephthalate (PET), High-density polyethene (HDPE), Low-density polyethene (LDPE), Polypropylene (PP), and multilayer plastic (MLP) as disposed plastic waste. To develop a high-performance composite brick. This approach is distinctive in combining WFS with thermoplastic waste, optimising material proportions, and utilising advanced manufacturing techniques to reduce dependency on virgin raw materials. Composite bricks resulted in a specific gravity of 2.48 and a finess modulus of 2.133. The developed brick achieved a water absorption of 0.69 for poly sand brick (PSB) and 0.92 for perforated leg brick, and an impressive compressive strength of 4.98 N/mm². These results demonstrate that PTW-WFS composite bricks offer a balance of adequate strength and reduced water absorption, providing a cost-effective and eco-friendly solution for construction applications. This composite brick not only addresses pressing environmental challenges but also aligns with several United Nations Sustainable Development Goals (SDGs), thus contributing to sustainable construction without compromising the structural integrity of performance.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 8","pages":"4137 - 4157"},"PeriodicalIF":0.0,"publicationDate":"2026-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458842","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":"An opposition-based multi-objective Grey Wolf optimizer for time–cost–quality trade-off optimization in subcontractor selection","authors":"Rakesh Gupta,&nbsp;Miguel Villagómez-Galindo,&nbsp;Gohar Ali,&nbsp;T. C. Manjunath,&nbsp;K. Ravibabu,&nbsp;Surajit Murasingh","doi":"10.1007/s42107-026-01715-6","DOIUrl":"10.1007/s42107-026-01715-6","url":null,"abstract":"<div><p>Subcontractor selection in construction projects involves complex decision-making due to the conflicting nature of multiple performance criteria, particularly time, cost, and quality (TCQ). This study proposes a novel Opposition-Based Grey Wolf Optimizer (OBL-GWO) to effectively solve the multi-objective TCQS trade-off optimization problem in subcontractor selection. The proposed approach integrates opposition-based learning (OBL) into the standard Grey Wolf Optimizer (GWO) to enhance population diversity, accelerate convergence, and improve exploration–exploitation balance. A discrete 20-activity construction project is considered as a case study, where each activity can be executed by alternative subcontractors with varying performance levels. The optimization framework evaluates project duration and total cost using the Critical Path Method (CPM), while quality are incorporated as additional objective functions within a multi-objective optimization setting. The performance of the proposed OBL-GWO is benchmarked against Discrete Particle Swarm Optimization (DPSO) and NDSII-GWO algorithm. To ensure a robust comparative assessment, widely accepted performance metrics, including Hypervolume (HV) and Spread (SP), are employed to evaluate the convergence and diversity of the obtained Pareto fronts. The results demonstrate that OBL-GWO consistently achieves superior performance in terms of solution quality, convergence speed, and distribution uniformity compared to the competing algorithms. Specifically, the proposed method provides a well-distributed Pareto front with higher HV values and lower SP values, indicating better diversity and proximity to the true Pareto-optimal front. The findings confirm that incorporating opposition-based learning significantly enhances the optimization capability of GWO for complex multi-objective construction problems. This study contributes a robust and efficient decision-support tool for subcontractor selection, enabling project managers to achieve balanced and sustainable trade-offs among time, cost and quality objectives.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 9","pages":"4227 - 4237"},"PeriodicalIF":0.0,"publicationDate":"2026-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148807388","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":"Deep learning-based structural crack detection framework for structural health monitoring of reinforced concrete buildings","authors":"Siddharth Arora","doi":"10.1007/s42107-026-01713-8","DOIUrl":"10.1007/s42107-026-01713-8","url":null,"abstract":"<div><p>Structural health monitoring (SHM) is an important activity in the safety and long life of reinforced concrete buildings and bridges. Structural cracks should be detected early to avoid further degeneration and reduce the expenses incurred on maintenance. Conventional approaches to crack inspection are mainly based on the manual visual evaluation that may turn out to be time-consuming, labor intensive, and prone to subjective error. New technologies in artificial intelligence and computer vision give hope of automating the process of structural inspection. This paper proposes a deep learning-based model of automated crack detection on reinforced concrete surfaces through convolutional neural network (CNN). The Concrete Crack Images Dataset (40,000 labeled images divided equally into crack and non-crack categories) is proposed to train and evaluate the offered model. Before training, images have been processed by resizing, normalization, and dividing into a set of data to guarantee uniformity of inputs in the model and credible performance assessment. CNN architecture includes several layers of convolutional and pooling which extract features hierarchically and dense which classify features in binary. The experimental results indicate the proposed framework has the high detection performance with the overall accuracy of about 99, and large precision, recall, and F1-score indicators. The findings reveal that the model demonstrates strong performance in detecting crack patterns under controlled experimental conditions. The suggested solution can aid the automated structural inspection and smart infrastructure monitoring systems, which will help in making structural health monitoring more efficient and scalable in the modern civil engineering practice.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 9","pages":"4215 - 4225"},"PeriodicalIF":0.0,"publicationDate":"2026-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148807475","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":"Hybrid experimental–explainable machine learning framework for predicting subgrade strength of lime-treated clay soils","authors":"Prashant Tiwari,&nbsp;Smita Tung","doi":"10.1007/s42107-026-01688-6","DOIUrl":"10.1007/s42107-026-01688-6","url":null,"abstract":"<div><p>The stabilization of weak subgrade soils using lime is widely practiced in geotechnical engineering; however, accurate prediction of strength parameters such as the California bearing ratio (CBR) remains challenging due to complex soil–lime interactions. This study develops a hybrid experimental–machine learning framework for predicting the CBR of hydrated lime-treated low-plasticity clay (CL) soils by integrating laboratory investigation with explainable artificial intelligence (XAI). Laboratory results showed that CBR increased from 4.2% in untreated soil to 21.5% at 10% lime content, confirming strength enhancement through cation exchange, flocculation, and pozzolanic reactions. To improve model robustness, the dataset was expanded to 300 samples using controlled Gaussian-based statistical sampling while preserving statistical characteristics. Ten input variables were used to train seven machine learning models, including MLR, SVR, ANN, DTR, RFR, XGBoost, and CatBoost. Among these, CatBoost achieved the highest predictive accuracy (R² = 0.98, RMSE = 1.08, MAE = 0.87). SHAP analysis and partial dependence plots identified lime content, maximum dry density, and curing period as dominant factors governing CBR. The proposed framework offers a reliable and interpretable tool for subgrade strength prediction, reducing reliance on extensive laboratory testing and supporting data-driven pavement design.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 7","pages":"3663 - 3680"},"PeriodicalIF":0.0,"publicationDate":"2026-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386101","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 prediction of seismic response parameters of reinforced concrete buildings under different site and ground motion conditions","authors":"Hudson Gnanadurai,&nbsp;Revathy Jayaseelan,&nbsp;Gajalakshmi Pandulu","doi":"10.1007/s42107-026-01712-9","DOIUrl":"10.1007/s42107-026-01712-9","url":null,"abstract":"<div><p>Predicting seismic response parameters is vital for designing reinforced concrete structures, but traditional approaches can become computationally intensive when multiple seismic scenarios are evaluated. This study presents a unified machine learning framework to predict storey-level seismic responses of a 10-storey RC special moment-resisting frame building. It explicitly integrates seismic hazard features, site conditions, and response variability. A comprehensive dataset of around 1300 samples was generated using ETABS-based nonlinear analyses, considering seismic zones, soil types, and ground motion intensity measures such as peak ground acceleration, velocity, and displacement as inputs. The key response parameters analysed include storey displacement, inter-storey drift ratio, storey shear, and overturning moment. Three machine learning models, viz., Random Forest, Support Vector Regression, and Extreme Gradient Boosting, were developed and optimised via cross-validation. Among these methods, XGBoost performed the best, with test R² scores of 0.9936 for displacement, 0.9955 for inter-storey drift ratio, 0.9973 for storey shear, and 0.9941 for overturning moment. It reduced prediction errors by approximately 30% to 90% compared to RF and SVR, with the greatest improvement observed in storey shear prediction, resulting in a 21% increase in R² over SVR. These findings demonstrate that the proposed framework effectively models complex nonlinear seismic behaviour while significantly reducing computational costs. This method provides a practical, efficient tool for rapid seismic performance assessments and supports data-driven structural design aligned with Indian seismic code standards.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 9","pages":"4199 - 4214"},"PeriodicalIF":0.0,"publicationDate":"2026-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148807474","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":"Thermophysical performance of compressed red earth bricks incorporating argan nut shell fibers for sustainable construction applications","authors":"Karima Ouaazizi,&nbsp;Abderrahim Samaouali,&nbsp;Randa Bakari,&nbsp;Fatima Kanibou,&nbsp;Asmae Arbaoui","doi":"10.1007/s42107-026-01717-4","DOIUrl":"10.1007/s42107-026-01717-4","url":null,"abstract":"<div><p>This study investigates the chemical composition, microstructure, and thermophysical performance of compressed red earth bricks reinforced with argan nut shell fibers at varying contents (0–30 wt%). The objective is to assess the suitability of this natural reinforcement for improving the thermal efficiency of sustainable earthen construction materials. Scanning Electron Microscopy revealed uniform fiber dispersion and increased porosity at higher fiber contents, while XRF and EDX analyses confirmed the incorporation of organic components through increased carbon content and reduced mineral oxides (Si, Al, Ca, Fe). Thermophysical parameters—thermal conductivity, volumetric and specific heat capacity, thermal diffusivity, and thermal effusivity—were measured between 20 ° C and 50 ° C. Results show a progressive reduction in thermal conductivity (up to 20%) and diffusivity (26%) with increasing fiber content, indicating an enhanced insulation effect. Statistical analyses (Type III ANOVA and Tukey HSD) confirmed that both fiber content and temperature significantly influenced all measured parameters. These findings highlight the potential of argan shell fibers as sustainable additives for thermally efficient, low-impact earthen construction materials.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 9","pages":"4239 - 4259"},"PeriodicalIF":0.0,"publicationDate":"2026-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148807476","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":"Uncertainty-aware and adaptively sampled kriging-assisted Bayesian finite element model (FEM) updating","authors":"Ertuğrul Türker Uzun","doi":"10.1007/s42107-026-01711-w","DOIUrl":"10.1007/s42107-026-01711-w","url":null,"abstract":"<div><p>This study evaluates the impact of experimental design strategies on surrogate-assisted Bayesian FEM updating using stochastic modal parameters. A unified Bayesian FEM updating framework is developed, focusing on structural parameters inferred using modal frequencies and mode shapes treated as stochastic quantities. Modal data is obtained from repeated ambient and earthquake recordings and consolidated using a three-layer stochastic framework. The use of consensus modal parameters derived from multiple measurement runs enables a robust and uncertainty-aware representation of modal characteristics, which enhances the stability and reliability of the Bayesian updating process. Two experimental design strategies are implemented: a space-filling Latin Hypercube Sampling (LHS)-based design and an adaptive Expected Improvement (EI)-driven sampling strategy that iteratively refines the surrogate model. The results show that EI-based adaptive sampling concentrates training points in regions associated with low model-data mismatch and high posterior probability, leading to improved surrogate accuracy within the posterior region, more accurate posterior frequency predictions, and tighter posterior parameter distributions with fewer finite element model evaluations. The study demonstrates that posterior-aware adaptive experimental design significantly improves the efficiency and reliability of surrogate-assisted Bayesian FEM updating, outperforming conventional space-filling designs when stochastic modal data are employed. The findings emphasize the importance of local surrogate accuracy within the posterior region and provide guidance for uncertainty-aware structural model calibration and future reliability-oriented applications.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 8","pages":"4035 - 4053"},"PeriodicalIF":0.0,"publicationDate":"2026-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458866","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}