Asian Journal of Civil Engineering最新文献

{"title":"Multi-objective optimization of high-performance concrete with SBR, silica fume, and fibers using NSGA-III: comprehensive evaluation of fresh, mechanical, durability, and microstructural properties","authors":"Anirudh Sharma,&nbsp;Ram Vilas Meena","doi":"10.1007/s42107-025-01328-5","DOIUrl":"10.1007/s42107-025-01328-5","url":null,"abstract":"<div><p>This study investigates the multi-objective optimization of high-performance concrete (HPC) incorporating styrene-butadiene rubber (SBR), silica fume, and fibers (glass and polypropylene) to enhance its fresh, mechanical, durability, and microstructural properties. A systematic experimental program was conducted to evaluate the effects of fiber reinforcement on workability, strength, durability, and microstructure. The study employs NSGA-III (non-dominated sorting genetic algorithm III) to optimize the mix design for maximum strength, minimum permeability, and cost efficiency. The results show that the addition of 1% glass fiber and 3% SBR in HPC led to a 20% increase in compressive strength (107.7 MPa), a 50% reduction in permeability, and improved acid and freeze-thaw resistance compared to conventional concrete. Microstructural analysis (SEM, TGA, and XRD) confirmed improved interfacial transition zone (ITZ) density, reduced porosity, and enhanced hydration product formation. The cost-performance analysis indicates that glass fiber-reinforced HPC offers superior durability and mechanical properties, making it an ideal choice for high-rise buildings, bridges, marine structures, and pavements. This study demonstrates that NSGA-III-based optimization effectively balances strength, durability, and cost, providing a sustainable and high-performance concrete solution for modern infrastructure. Future research should focus on hybrid fiber combinations and machine learning-based mix design optimization to further enhance HPC performance.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 6","pages":"2547 - 2567"},"PeriodicalIF":0.0,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073572","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":"Application of artificial neural network for prediction of fracture energy of concrete","authors":"Sudhanshu Pathak,&nbsp;Sachin Mane,&nbsp;Smita Pataskar,&nbsp;Gaurang Vemawala,&nbsp;Sandeep Shiyekar,&nbsp;Sandeep Sarnobat","doi":"10.1007/s42107-025-01333-8","DOIUrl":"10.1007/s42107-025-01333-8","url":null,"abstract":"<div><p>The analysis of fracture parameters of concrete drawing the researchers attention and getting popular day by day. Every concrete structure undergoes crack formation, initiation and propagation phase, to understand the kind and severity of crack study the fracture mechanics is very much needed. Fracture energy (G_<i>f</i>) is one the main characteristic amongst numerous fracture parameters. The different parameters such as water to cement (w/c) ratio, compressive strength (<i>fc</i>), diameter of aggregates, testing age of specimens etc. play essential role in understanding the G_<i>f</i>. In present work, G_<i>f</i> of concrete is measured by replacing cement with nano TiO₂ (NT) at 1, 2, 3, and 4% of the concrete mix, as well as fly ash (FA) and ground granulated blast furnace slag (GGBS) at 10, 20, 30, and 40%. The G_<i>f</i> was investigated using the size effect technique (SEM), and the notched beams were subjected to a three-point bend test. According to the experimental results, the NT4FA40 mix had the maximum G_<i>f</i>, while mixtures including FA out performed than GGBS mixes. Furthermore, an attempt was made to anticipate G_<i>f</i> using the soft computing method in light of the current necessity. The G_<i>f</i> is predicted using an ANN. The literature database, which includes 193 fracture tests, was gathered from earlier research in addition to the data from the current experimental investigation. Furthermore, the formula proposed by Bažant and Becq-Giraudon was utilized to make predictions based on a number of characteristics, including compressive strength, maximum aggregate size, and w/c ratio. compressive strength, maximum aggregate size, and water to cement ratio are among the characteristics that are trained, verified, and tested for ANN. The ANN model developed using literature-based data, Bažant and Becq-Giraudon equation derived data and experimental data gives promising results with R values 0.999, 0.981, 0.984 respectively. The present study concludes, ANN model shows the excellent output for prediction of G_<i>f</i><i>.</i></p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 6","pages":"2629 - 2644"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073965","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":"Application of artificial intelligence and machine learning in construction project management: a comparative study of predictive models","authors":"Amol Shivaji Mali,&nbsp;Atul Kolhe,&nbsp;Pravin Gorde,&nbsp;Aniket Kolekar,&nbsp;Amit Umbrajkar,&nbsp;Sandesh Solepatil,&nbsp;Kirti Zare","doi":"10.1007/s42107-025-01335-6","DOIUrl":"10.1007/s42107-025-01335-6","url":null,"abstract":"<div><p>This study examined the application of artificial intelligence and machine learning techniques in managing construction projects, focusing on planning, cost management, scheduling, quality control, and risk evaluation. This study employed Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forest (RF) algorithms to create predictive models. Findings revealed efficient resource allocation, with a minimal cost difference of 0.12% between projected and actual expenses. The Schedule Performance Index (SPI) of 1.04 suggested that the project was ahead of schedule, while a Cost Performance Index (CPI) of 0.91 indicated slight budget excesses. Quality measurements showed a defect rate of 2.5%, with three defects per 100 units. Among the tested ML models, Random Forest exhibited the best performance with an R² of 0.88, MSE of 1800, MEA of 36.25, and AUC of 0.95, outperforming ANN (R² = 0.85, MSE = 2000, MEA = 38.50, and AUC = 0.92) and SVM (R² = 0.80, MSE = 2500, MEA = 42.75, and AUC = 0.89). The safety performance index achieved 0.9 for compliance and 0.8 for training. These results show AI and ML can improve construction management, with RF being the top model for risk prediction and task management.</p><h3>Graphical abstract</h3>\u0000<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 6","pages":"2671 - 2686"},"PeriodicalIF":0.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073742","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 machine learning-based framework for seismic vulnerability assessment of reinforced concrete educational facilities","authors":"Tapan Kumar,&nbsp;Mohammad Al Amin Siddique,&nbsp;Raquib Ahsan,&nbsp;Tanvir Mustafy","doi":"10.1007/s42107-025-01332-9","DOIUrl":"10.1007/s42107-025-01332-9","url":null,"abstract":"<div><p>This study presents the development of a machine learning (ML) based framework for assessing the seismic vulnerability of existing educational reinforced concrete (RC) buildings under the jurisdiction of the Rajdhani Unnayan Kartripakkha (RAJUK) in Dhaka. The conventional three major stages of assessment methods are often resource-intensive and time-consuming, especially when applied to large building stocks. The primary objective is to assess the seismic vulnerability of existing RC educational buildings in similar contexts using the ML method, focusing in predicting analytical parameters Story Shear Ratio (SSR) as a critical analytical risk indicator, implementing Rapid Visual Assessment (RVA) 8 parameters. The RVA parameters are construction year, building condition, number of stories, typical floor area, redundancy, pounding, plan irregularity and elevation irregularity, and corresponding building’s SSR value in the preliminary Engineering Assessment (PEA) survey. Three well-known ML models, including Support Vector Regression (SVR), Random Forest Regression (RFR), and Artificial Neural Networks (ANN), were employed to predict SSR using RVA parameters. The dataset of 268 RC educational buildings was collected from the RAJUK. Based on the analysis, the SVR model obtained a higher coefficient of determination (R²) of 0.34 than the 0.17 and 0.16 of the RFR, and ANN models and 0.038, 0.04, and 0.04 for the Mean Square Error, respectively, though all models exhibited limited explanatory power for SSR. The findings reveals that the SVR handled comparatively well the complexities and nonlinearities in the dataset. This study proposes a cost-effective ML framework for seismic vulnerability assessment, with potential to support urban resilience efforts following further validation.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 6","pages":"2605 - 2627"},"PeriodicalIF":0.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073865","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":"Assessing urban air quality of Pune city using AI-based predictive model: a data-driven approach for forecasting air quality index","authors":"Sushant Waghmare,&nbsp;Gopi Ghadvir","doi":"10.1007/s42107-025-01334-7","DOIUrl":"10.1007/s42107-025-01334-7","url":null,"abstract":"<div><p>India, the most populous country in the world, ranks as the seventh largest by area. As per IQAir reports, in 2024, India was the fifth most polluted country, preceded by Chad, Congo, Bangladesh, and Pakistan, based on Air Quality Index (AQI) values. This study aims to predict air quality in Pune, Maharashtra, using an AI-driven data-centric approach. The dataset, obtained from sources such as Kaggle, CPCB, and WHO, comprises 3,170 records covering fifteen key factors influencing AQI, including SO₂, NOx, RSPM, precipitation, maximum and minimum temperature, sun hours, UV index, wind gust, humidity, pressure, average temperature, and wind speed. Data spanning nineteen years (2006–2024) is utilized to develop the predictive model, with records from 2006–2019 used for training and testing, while data from 2020–2024 is reserved for validation. This research proposes Linear Regression (LR) as a machine learning approach, achieving an R-value of 0.9611. The LR model's performance metrics include an RMSE of 21.4079, MAPE of 7.8945%, and MAE of 13.5884. The developed model can assist in forecasting air quality for urban residents, contributing to public health protection. Furthermore, it can aid in identifying effective mitigation strategies and operational measures to enhance air quality.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 6","pages":"2645 - 2655"},"PeriodicalIF":0.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073961","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":"Flexural behavior of cellular stainless-steel beams: experimental and statistical analysis","authors":"Ali Imran Ahmad Kamal,&nbsp;Fariz Aswan Ahmad Zakwan,&nbsp;Ruqayyah Ismail","doi":"10.1007/s42107-025-01325-8","DOIUrl":"10.1007/s42107-025-01325-8","url":null,"abstract":"<div><p>This study examines the flexural performance of cellular stainless-steel beams (cssbs) with circular web openings associated with experimental validation, statistical analysis, and sustainability evaluation. Cssbs are advantageous in reducing weight and material use, while the weight reduction negatively affects flexural strength and introduces new failure mechanisms such as shear buckling and local yielding. Three-point bending tests were conducted on welded austenitic stainless-steel beams (en 1.4301), comparing cssbs to solid stainless-steel beams (ssbs). Experimental results revealed a 16.77% reduction in flexural capacity and a 12% decrease in stiffness for cssbs relative to ssbs, accompanied by increased mid-span deflections under identical loading. Statistical analysis found a strong relationship (r²=0.96) between web opening geometry and reduction in capacity, while significance testing showed the influence of all perforation parameters. Modes of failure were found to differ between cssbs and ssbs. An analytical model is proposed that predicts loss of strength as a function of web opening, while being supported by eurocode (en 1993-1-4), and aims to manage limitations of existing design codes for perforated stainless-steel beams. In addition, the study addresses sustainability of cssbs with an observation of over 90% recyclability of material, and reduced weight, approximately 12% overall. While cssbs had an increased initial cost per kg of material, they will offer economic and environmental benefits over the life of the structure and any recycled material. The study cumulatively provides evidence to support the use of cssbs in sustainable structural designs, and it demonstrates experimental values and models intended for practice.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 6","pages":"2303 - 2317"},"PeriodicalIF":0.0,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144074037","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":"Seismic response prediction of asymmetric structures with SMA dampers using machine learning algorithms","authors":"Anant Parghi,&nbsp;Jay Gohel,&nbsp;Apurwa Rastogi,&nbsp;Melda Yucel,&nbsp;Cigdem Avci-Karatas,&nbsp;Snehal Mevada","doi":"10.1007/s42107-025-01323-w","DOIUrl":"10.1007/s42107-025-01323-w","url":null,"abstract":"<div><p>The dynamic response of asymmetric structures to seismic forces is challenging due to mass, stiffness, and damping distribution irregularities. Shape memory alloy (SMA) dampers have successfully dealt with these issues because of their distinctive super elasticity and energy dissipation characteristics. In this work, we study regression algorithms’ effectiveness in predicting the seismic behavior of asymmetric structures installed with SMA dampers. A numerical simulation produces a comprehensive dataset of structural parameters consisting of the structure’s varying periods, frequency ratios, and eccentricity ratios. The critical responses of structures, including lateral and torsional displacement, lateral and torsional acceleration, and stiff and flexible edge damper forces, are predicted using machine learning (ML) techniques, artificial neural networks, decision trees, support vector machines, ensemble bagged trees, and Gaussian process regression. The model is validated using performance metrics such as mean absolute error and root mean square error, mean absolute percentage error, coefficient of determination, and Shapley Additive explanations values, ensuring that predictions are robust and consistent. The results revealed that regression methods accurately model the nonlinear dynamic behavior of SMA dampers in asymmetric structures, providing exact and computationally efficient predictions of seismic response. This predictive paradigm facilitates optimal damper configuration, minimizing the computational complexity of iterative design methods. The proposed research integrates advanced materials with ML methods to create seismically resilient structural systems.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 6","pages":"2475 - 2497"},"PeriodicalIF":0.0,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144074111","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 prediction of compressive strength of plastic optical fiber embedded transparent concrete","authors":"Manish Pratap Singh,&nbsp;Anish Kumar,&nbsp;Sanjeev Sinha","doi":"10.1007/s42107-025-01327-6","DOIUrl":"10.1007/s42107-025-01327-6","url":null,"abstract":"<div><p>This study investigates the effect of plastic optical fiber integration on the compressive strength of M20 concrete. SVM-RBF, SVM-Linear, and XGBoost based machine learning prediction models were also trained and compared with conventional linear regression model. The compressive strength analysis confirms that POF inclusion reduces strength due to weak interfaces and void formation, particularly at smaller fiber spacings. However, increasing fiber spacing to 20 mm minimizes strength loss, demonstrating a more viable configuration for practical applications. The performance metrics, regression error characteristic (REC) curves, taylor diagram, and area over curve (AOC) results highlight XGBoost as the most accurate predictive model, outperforming SVM-RBF, SVM-Linear, and linear regression models. The R² values in training and testing for the XGBoost model are 0.999 and 0.997 respectively. The RMSE values in training and testing for the XGBoost model are 0.151 and 0.259 respectively. The monotonicity analysis reveals that fiber spacing and curing days positively affect compressive strength, while other mix variables remain relatively unchanged within the tested range.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 6","pages":"2527 - 2545"},"PeriodicalIF":0.0,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073810","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 NSGA-III and simulated annealing approach for multi-objective time–cost-quality-sustainability optimization in wastewater treatment plant construction projects","authors":"Vijay Kumar,&nbsp;Lilesh Gautam,&nbsp;Ritu Dahiya","doi":"10.1007/s42107-025-01329-4","DOIUrl":"10.1007/s42107-025-01329-4","url":null,"abstract":"<div><p>The construction of wastewater treatment plants (WWTPs) involves complex trade-offs among time, cost, quality, and sustainability. This study proposes a hybrid NSGA-III and Simulated Annealing (SA) approach to optimize these conflicting objectives in WWTP construction projects. The multi-objective optimization model considers four objectives: (1) minimizing total project duration, (2) minimizing total project cost, (3) maximizing project quality, and (4) maximizing project sustainability. The problem is formulated as a multi-mode resource-constrained project scheduling problem (MRCPSP) with real-world constraints such as budget limits, project deadlines, and activity dependencies. The hybrid NSGA-III and SA algorithm enhances solution diversity and convergence efficiency, overcoming limitations of traditional metaheuristic methods. A case study on a WWTP project is conducted, where multiple Pareto-optimal solutions are obtained and analyzed. A Weighted Sum Method is used to select the most balanced trade-off solution. Results demonstrate that the proposed hybrid approach outperforms conventional optimization algorithms in terms of solution quality, hypervolume, and computational efficiency. A comparative analysis with existing models highlights its superior performance in balancing trade-offs in large-scale construction projects. This research contributes to the field of sustainable infrastructure development by integrating optimization techniques that support efficient decision-making in WWTP construction.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 6","pages":"2569 - 2584"},"PeriodicalIF":0.0,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073811","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":"The anticipation of compressive strength of geopolymer mortars with tree-based machine learning models: effect of training-testing ratios","authors":"Talip Cakmak,&nbsp;İlker Ustabas","doi":"10.1007/s42107-025-01336-5","DOIUrl":"10.1007/s42107-025-01336-5","url":null,"abstract":"<div><p>Concrete, produced from cement, is the best greatly utilised building material. However, greenhouse gas discharges from cement preparation and consumption cause significant damage to the environment. Geopolymer production, which is one of the important alternatives, plays an important role in preventing this problem. In this study, tree-based machine learning (ML) algorithms such as Gradient Boosting Regression (GBR), Decision Tree (DT), Extremely Randomized Tree (ET), and Random Forest (RF) were utilized to anticipate the compressive strength (CS) of silica fume substituted obsidian-based two-component geopolymer mortars with different alkali activator properties. These ML algorithms were implemented using different train-test ratios (0.6 − 0.4, 0.7 − 0.3, 0.8 − 0.2, 0.9 − 0.1). The prediction and generalization performances of the applied models were measured by applying different statistical metrics like R², MAE, MAPE, MSE and RMSE. For the prediction of compressive strength, the GBR algorithm showed a better prediction performance than the other algorithms, with an R² value of 0.972. The RF algorithm showed the most consistent and balanced prediction performance. Significant decreases in R²_adjusted values were observed as the training rate increased. This is due to the tendency of the models to overlearn as the training rate increases. The results show that the models perform best at a training rate of 70%, and the generalization execution of the models reduces importantly as the training rate augments. The machine learning method applied to the forecasting of the CS of geopolymer mortars provides significant benefits to engineering applications due to its contributions in terms of workload and time savings.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 6","pages":"2657 - 2670"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073808","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}