Asian Journal of Civil Engineering最新文献

{"title":"Machine learning-based prediction of the unconfined compressive strength of expansive soils stabilized with hydrated lime-activated rice husk ash","authors":"Bayram Ateş,&nbsp;Mohammad Azim Eirgash,&nbsp;Yang Zhao","doi":"10.1007/s42107-026-01689-5","DOIUrl":"10.1007/s42107-026-01689-5","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate prediction of the unconfined compressive strength (UCS) of stabilized expansive soils is vital for reliable geotechnical design. This study proposes a Slime Mould Algorithm (SMA), an optimized machine learning framework to predict the UCS of expansive soils treated with hydrated-lime-activated rice husk ash (HARHA). A database of 121 laboratory-tested samples was developed using seven input parameters: HARHA content, liquid limit, plastic limit, plasticity index, optimum moisture content, clay activity, and maximum dry density. Several machine learning models were evaluated in both conventional and SMA-optimized forms using R², RMSE, MAE, and MSE. SMA optimization significantly improved model performance, increasing R² by 3–7% and reducing RMSE by up to 25%. The optimized models achieved R² values above 0.98, indicating excellent predictive accuracy. Compared with existing empirical and standalone models, the proposed framework showed substantial performance gains, up to 83% over SVR, 53% over ANN, and 15% over RF. Sensitivity analysis revealed that HARHA content and maximum dry density positively influence UCS, while clay activity and plasticity-related parameters have negative effects. Overall, the proposed approach offers a robust and sustainable alternative to conventional empirical methods, reducing reliance on extensive laboratory testing.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 7","pages":"3681 - 3701"},"PeriodicalIF":0.0,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386001","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":"Explainable AI based ML models for predicting the flexural strength of basalt fiber reinforced concrete using SHAP, LIME, PDP","authors":"Md. Imtiyaz Hussain,&nbsp;B. D. V. Chandra Mohan Rao","doi":"10.1007/s42107-026-01670-2","DOIUrl":"10.1007/s42107-026-01670-2","url":null,"abstract":"<div>\u0000 \u0000 <p>Basalt fiber reinforced concrete (BFRC) is gaining recognition as a promising building material in the recent times because of its improved durability and mechanical properties over conventional concrete. This study examines the flexural strength of BFRC and analyzes the impact of important factors such as curing conditions, w/c ratio, and fiber volume fraction. Experimental results have shown combining basalt fibers into concrete can significantly increase the flexural strength of concrete, thereby enhancing its load bearing capacity and improves its crack resistance. These results prove that BFRC is a suitable material for structures requiring high tensile and flexural performance. To complement the experimental investigation, several machine learning classifiers such as Gradient Boosting, and eXtreme Gradient Boost, K-Nearest Neighbors, Support Vector Regression, Decision Tree, Random Forest, are used to forecast the flexural strength of BFRC. The XGBoost model demonstrated excellent accuracy, achieving R² value of 0.972 for training data, and 0.936 for testing data respectively. In the error analysis, RMSE value was 0.391 during the training, while it was 0.539 during testing, indicating strong generalization capability. This study indicates that the use of machine learning techniques can serve as reliable predictive tools to improve BFRC performance and promote their use in real-world construction practices.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 6","pages":"3323 - 3340"},"PeriodicalIF":0.0,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147959107","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":"Structural and performance optimization of GGBS–fly ash–CNT-based M40 concrete U-drains under IRC loadings using FEM and multi-objective optimization","authors":"Girish Chandra Gandhi,&nbsp;Payal Mehta,&nbsp;Ankit Sodha","doi":"10.1007/s42107-026-01671-1","DOIUrl":"10.1007/s42107-026-01671-1","url":null,"abstract":"<div>\u0000 \u0000 <p>This study presents an integrated experimental, numerical, and optimization-based framework for enhancing the structural and durability performance of precast M40 concrete U drains incorporating supplementary cementitious materials (GGBS and fly ash) and multi-walled carbon nanotubes (CNTs). Ten concrete mixes were developed, including control, SCM-based, and CNT-modified mixes with CNT dosages ranging from 0.01% to 0.15%. Mechanical and durability properties were evaluated in accordance with relevant Indian Standards (IS), while finite element modelling (FEM) of six U-drain geometries was performed in ANSYS under IRC Class A, AA, and 70R vehicular loadings. Experimental results indicated that the maximum 28-day compressive strength (65.5 MPa) occurred at 0.03% CNT due to improved matrix densification. However, FEM simulations revealed that structural performance is governed by a combined stress–deformation–durability response rather than strength alone. A multi-objective optimization approach incorporating FEM stress, deformation, rapid chloride permeability (RCPT), and water permeability was employed to compute a composite performance index (PI). The results consistently identified 0.05% CNT (Mix M4-3) as the optimal dosage across all load classes, offering the best trade-off between stiffness, durability, and structural safety. Statistical analyses confirmed the significant influence of CNT dosage and SCM incorporation. The proposed framework provides practical design guidance for durable and structurally efficient precast U-drain systems subjected to varying traffic loads.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 6","pages":"3341 - 3360"},"PeriodicalIF":0.0,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147959108","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-driven arithmetic optimization algorithm for multi-objective time–cost–quality–safety optimization","authors":"Mani Bhushan,&nbsp;Jitendra Kumar,&nbsp;Aditya Kumar,&nbsp;Pankaj Kumar,&nbsp;Sumit Suman,&nbsp;Keshav Kumar,&nbsp;Upendra R. Darla","doi":"10.1007/s42107-026-01693-9","DOIUrl":"10.1007/s42107-026-01693-9","url":null,"abstract":"<div>\u0000 \u0000 <p>Time, cost, quality, and safety (TCQS) are critical and interdependent objectives in modern construction projects, where increasing complexity necessitates advanced multi-objective optimization techniques. This study proposes an Opposition-Enhanced Arithmetic Optimization Algorithm (OE-AOA) that integrates a simple opposition-based learning (OBL) mechanism to enhance the exploratory capability of the conventional Arithmetic Optimization Algorithm (AOA). By incorporating opposition during both population initialization and iterative updating, the proposed method effectively expands the search space, mitigates premature convergence, and improves solution diversity. A real-world 13-activity building project with multiple execution modes is modeled to capture discrete time–cost trade-offs, quality performance levels, and safety risk ratings, resulting in a complex nonlinear multi-objective optimization problem. The performance of the proposed OE-AOA is evaluated against the classical AOA, Latin Hypercube Sampling (LHS)-based NSGA-III, and the Adaptive Opposition Slime Mold Algorithm (AOSMA). The results demonstrate that OE-AOA generates a denser and more uniformly distributed Pareto front, achieving superior performance in project duration, total cost, overall quality, and cumulative safety risk. Furthermore, the proposed approach exhibits higher convergence accuracy, improved stability, and greater robustness across multiple simulation runs. Overall, the findings confirm that OE-AOA provides a reliable, computationally efficient, and high-performing framework for integrated TCQS optimization, supporting decision-makers in achieving balanced, safe, cost-effective, and high-quality construction project outcomes.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 8","pages":"3703 - 3714"},"PeriodicalIF":0.0,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458791","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":"Impact of a displacement-based design procedure in the hysteretic steel dampers-based seismic retrofitting of mid-rise regular reinforced concrete framed buildings","authors":"Cristian O. Bay,&nbsp;Gustavo L. Palazzo,&nbsp;José Luis Almazán,&nbsp;Jesús D. Villalba-Morales","doi":"10.1007/s42107-026-01676-w","DOIUrl":"10.1007/s42107-026-01676-w","url":null,"abstract":"<div>\u0000 \u0000 <p>Many existing mid-rise reinforced concrete (RC) buildings in high seismic hazard regions require retrofitting to improve seismic performance under current criteria. Passive energy dissipation systems are a practical option to enhance response, but their design often relies on computationally demanding nonlinear analyses. This study examines a displacement-based design (DBD) procedure for retrofitting RC buildings in Mendoza, Argentina, using hysteretic steel dampers. The method idealizes the retrofitted structure (existing frame and dampers acting in parallel) as an equivalent single-degree-of-freedom system. Equivalent damping is estimated through a closed-form formulation intended to capture the interaction between the original frame and the added devices. Three mid-rise regular RC buildings representative of the Cuyo region are considered. Retrofit solutions are assessed through nonlinear time-history analyses using far-field and near-field ground motions scaled by spectral matching. The results indicate that the procedure can achieve substantial reductions in global displacement demand and interstory drifts for the studied cases, and it tends to produce a more uniform drift distribution along the height at the design level. As expected, the retrofit increases base shear demand relative to the original structures. For the analyzed buildings and record set, peak drift estimates from the proposed procedure show close agreement with nonlinear dynamic results. The findings suggest that the approach may provide an efficient preliminary design tool for regular mid-rise RC frames with hysteretic dampers; however, its applicability should be verified for other structural configurations, irregularities, and broader ground-motion characteristics.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 7","pages":"3435 - 3458"},"PeriodicalIF":0.0,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386064","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":"Fragility-based seismic performance assessment of steel braced and moment resisting frames","authors":"Bapugouda B. Biradar,&nbsp;Anoop I. Shirkol,&nbsp;Bush Rc,&nbsp;Rohit Vyas","doi":"10.1007/s42107-026-01677-9","DOIUrl":"10.1007/s42107-026-01677-9","url":null,"abstract":"<div>\u0000 \u0000 <p>The study assesses the fragility assessment of Concentrically Braced Frames (CBF), Eccentrically Braced Frames (EBF), and Special Moment Resisting Frames (SMRF) using Nonlinear Static Pushover Analysis (NSPA). A comparative evaluation of Performance-Based Plastic Design (PBPD) and Force-Based Design (FBD) is conducted using fragility curves developed through the Capacity Spectrum Method (CSM) to quantify exceedance probabilities across various damage states. Results indicate that PBPD significantly reduces exceedance probabilities in the Complete damage state, with reductions of 9.2% (3-storey), 6.6% (6-storey), 9.0% (9-storey), and 10.0% (12-storey) in CBF; 23.0%, 22.6%, 16.7%, and 11.3% in EBF; and 25.9%, 23.6%, 6.0%, and 0.0% in SMRF, respectively. Additionally, PBPD reduces structural weight, achieving reductions of 15.17% in SMRF, 7.24% in EBF, and 3.51% in CBF. Furthermore, PBPD enhances drift control, decreasing yield drift variations by up to 45% in CBF and 35% in EBF, ensuring more controlled inelastic deformations. Overall, PBPD enhances seismic resilience, optimizes material efficiency, and minimizes damage risks compared to FBD.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 7","pages":"3459 - 3475"},"PeriodicalIF":0.0,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386019","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":"Utilizing artificial intelligence for solving many-objective construction time–cost–quality–safety risk trade-off optimization problems","authors":"Vishnu Lakkamraju,&nbsp;Naveen Tewari,&nbsp;Gyana Ranjana Panigrahi,&nbsp;Suvendu Kumar Nayak,&nbsp;Akanksha Kulkarni,&nbsp;Mohammad Zaki Mortazavi","doi":"10.1007/s42107-026-01683-x","DOIUrl":"10.1007/s42107-026-01683-x","url":null,"abstract":"<div><p>Construction project planning inherently involves complex decision-making due to the presence of multiple conflicting objectives, including project duration, cost efficiency, quality performance, and safety risk. This study presents an artificial intelligence (AI)-based optimization framework for solving many-objective construction time–cost–quality–safety risk trade-off optimization problems (TCQSRTP) using a 37-activity project as a case study. Each activity is modeled with multiple execution modes, characterized by distinct durations, costs, quality levels, and safety risk indices, thereby forming a large-scale combinatorial optimization problem. To efficiently explore the search space and enhance solution quality, an improved metaheuristic algorithm based on the Opposition-Based Learning Teaching–Learning-Based Optimization (OBL–TLBO) algorithm is proposed. The TLBO algorithm is a parameter-less optimization technique that updates candidate solutions through teacher and learner phases, ensuring a balance between exploration and exploitation. To further improve convergence speed and population diversity, opposition-based learning (OBL) is incorporated, enabling simultaneous consideration of candidate solutions and their corresponding opposite solutions within the search space. This mechanism increases the probability of locating regions closer to the global optimum and helps avoid premature convergence. The proposed framework integrates the Critical Path Method (CPM) for accurate project scheduling and adopts a Pareto-based multi-objective optimization approach to identify non-dominated solutions. The model simultaneously minimizes project duration, total cost, and safety risk while maximizing overall quality performance. The results demonstrate that the proposed OBL–TLBO approach produces a well-distributed and diverse Pareto front, offering a wide range of optimal trade-off solutions for decision-makers. Comparative analysis reveals that the proposed method outperforms conventional algorithms in terms of convergence speed, solution diversity, and robustness. The findings highlight the effectiveness of integrating opposition-based learning with TLBO as a powerful decision-support tool for enhancing sustainability, safety, and overall performance in complex construction project planning.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 7","pages":"3565 - 3575"},"PeriodicalIF":0.0,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386041","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":"Parametric analysis and optimized shear capacity prediction of stiffened steel plate girders using hybrid machine learning algorithms","authors":"Baljeet Yadav,&nbsp;S. S. Mishra","doi":"10.1007/s42107-026-01690-y","DOIUrl":"10.1007/s42107-026-01690-y","url":null,"abstract":"<div>\u0000 \u0000 <p>Structural engineers face a hard time accurately predicting the level of shear resistance reinforced steel plate girders. The reason for this is the complicated network of nonlinear interactions that exists between the qualities of the material, the shape of the girder, and the behavior of the post-buckling tension field. Basler's and Cardiff's tension field theory is the basis for much of the empirical formulas used in long-standing design standards like IS 800:2007, AISC 360–16, and Eurocode3. People often don't think about how flexible and strong the flanges are after they bend. This paper presents a Hybrid ML framework that can predict shear resistance close to accurate and lets for in-depth parametric analysis. A dataset from 314 experiments and using numerical simulations. Thirteen non-dimensional measures were included to show how these structures work in conjunction careful feature engineering. These metrics include web slenderness (dw/tw), flange stiffness (bf/2tf), and moment of inertia ratios of flanges and web. The study looked at nine different regression methods, including Random Forest, XGBoost, CatBoost, AdaBoost, Decision Tree, Multilayer Perceptron, Artificial Neural Networks, LightGBM, and Gradient Boosting Machines. Eight different ways were tried by changing the models' hyperparameters to see how well they could predict. Besides Grid Search, Successive Halving, and Random Search there were other advanced bio-inspired metaheuristics such as Genetic Algorithm, Particle Swarm Optimization, Artificial Gorilla Troops Optimizer, Ant Colony Optimization, and Colliding Bodies Optimization were used to find the best solution. A new Stacking Ensemble Regressor was developed using a Linear Regression meta-learner. It incorporates the best optimized basic learners, like GA-tuned RF and AGTO-tuned CatBoost. This ensemble has a Root Mean Squared Error (RMSE) of 24.65 kN and a coefficient of determination (R^2) of about 0.994. The model was much ahead of the standalone models and the analytical models made by Cardiff and Hoglund. SHAP (SHapley Additive exPlanations) and PDP were used in a study on interpretability to confirm the results of theoretical mechanics. The most important factors recognised were web slenderness and web thickness. Lastly, a Python Graphical User Interface (GUI) was developed to connect complicated computational models to real-world design uses.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 8","pages":"4095 - 4135"},"PeriodicalIF":0.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458772","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":"Predictive modeling of waste glass cementless mortar performance through interpretable machine learning approaches","authors":"K. G. Shwetha,&nbsp;B. B. Das","doi":"10.1007/s42107-026-01675-x","DOIUrl":"10.1007/s42107-026-01675-x","url":null,"abstract":"<div>\u0000 \u0000 <p>The manufacturing of construction material which is sustainable in nature plays an important role in reducing the environmental impact related to Portland cement production and also promoting the principles of circular economy. The present study investigates a waste-glass-based cementless mortar, where waste glass powder played the role of primary binder component to avoid conventional usage of cement. The performance of the sustainable material was efficiently and optimally predicted employing the interpretable machine learning (ML) models. The key performance indicators namely compressive strength, flexural strength, and drying shrinkage were predicted by developing Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) ML models utilizing the experimental data. The evaluation of the models was rigorously conducted through train–test splitting and five-fold cross-validation, with the use of metrics such as the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). Even though both ML models predicted with high accuracy, XGBoost models was observed to be marginally superior in terms of generalization performance for all the output parameters. Among the developed models, XGBoost demonstrated superior predictive capability with R² values of 0.996, 0.998, and 0.999 for compressive strength, flexural strength, and drying shrinkage, respectively, along with significantly reduced RMSE values. The enhancement in transparency and physical interpretability was obtained employing SHapley Additive exPlanations (SHAP) for studying the effect of input variables individually. The results focus on the usefulness of interpretable ML models as dependable decision-support tools for the design and optimization of low-carbon, waste-derived cementless mortar systems. The suggested framework offers a developed sustaianable material, maintaining transparency and minimizes the efforts and time spent on experimental work.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 6","pages":"3361 - 3381"},"PeriodicalIF":0.0,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147959106","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":"Comprehensive adaptive penalty-based non-dominated sorting genetic algorithm III for multi-objective trade-off optimization of construction scheduling using mivan formwork technology in high-rise buildings","authors":"Atole Tanaji Vaman,&nbsp;Manoj Sharma","doi":"10.1007/s42107-026-01664-0","DOIUrl":"10.1007/s42107-026-01664-0","url":null,"abstract":"<div>\u0000 \u0000 <p>This study presents a Comprehensive Adaptive Penalty Scheme integrated with the Non-Dominated Sorting Genetic Algorithm III (CAPS–NSGA-III) for multi-objective optimization of construction scheduling in high-rise residential projects executed using Mivan aluminum formwork technology. Moving beyond conventional time–cost trade-off models, the proposed framework simultaneously minimizes five conflicting objectives: total project duration, construction cost, defect index (quality), accident risk (safety), and carbon dioxide (CO₂) emissions (environmental impact). The scheduling problem is formulated as a multi-mode activity model with strict precedence, mode exclusivity, and feasibility constraints. To enhance convergence and feasibility preservation, a dynamic adaptive penalty mechanism is employed that adjusts constraint severity based on generation progress and population feasibility ratio. The framework is validated using a real G + 15 residential project in Meerut, India, consisting of 21 activities with three execution modes (Normal, Fast-Track, and Eco-Friendly). Results demonstrate that the proposed CAPS–NSGA-III significantly outperforms the standard NSGA-III, achieving a feasibility ratio of 98.2%, improved hypervolume (0.885), and faster convergence. Seventeen feasible Pareto-optimal schedules were generated, revealing explicit trade-offs among sustainability, safety, quality, and economic performance. A post-optimization decision-support process integrating Analytic Hierarchy Process-based weights with the Technique for Order Preference by Similarity to Ideal Solution identified an eco-balanced schedule that reduced carbon emissions (123.76 t) and accident risk (0.21%) with only marginal schedule extension. The proposed framework demonstrates strong scalability, robustness, and practical applicability for sustainable and safety-oriented planning of high-rise construction projects.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 6","pages":"3203 - 3224"},"PeriodicalIF":0.0,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147959165","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}