Asian Journal of Civil Engineering最新文献

{"title":"Optimization of twisted high-rise building geometries for wind load mitigation and pedestrian comfort","authors":"Himanshu Yadav,&nbsp;Amrit Kumar Roy","doi":"10.1007/s42107-025-01269-z","DOIUrl":"10.1007/s42107-025-01269-z","url":null,"abstract":"<div><p>This study investigates the aerodynamic performance and pedestrian-level wind comfort of high-rise buildings with varying degrees of twist. Utilizing Computational Fluid Dynamics (CFD) simulations and the Spalart–Allmaras Detached Eddy Simulation (DES) model, the analysis was conducted on a 150-m-high building with a base dimension of 40 m × 35 m. Five twist angles (0°, 10°, 15°, 20°, and 25°) were examined under a wind velocity of 50 m/s. The results indicated that increasing the twist angle significantly reduces the wind pressure on the building’s surface, with the maximum pressure reduction observed at a 10° twist, resulting in an 8.02% decrease from the 0° model. Additionally, the pressure distribution became more uniform with higher twist angles, indicating improved aerodynamic performance. Pedestrian-level wind speeds were assessed at six critical locations around the building base. It was observed that the twisted models significantly mitigated high wind velocities at pedestrian levels, enhancing comfort and safety. The study provides design recommendations for optimizing high-rise building geometries to balance structural integrity and urban livability.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 4","pages":"1595 - 1620"},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698542","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":"Optimized SVM-based model for health monitoring of joints in a multi-story 3D steel frame structure","authors":"Maloth Naresh,&nbsp;Maloth Ramesh,&nbsp;Ashish Balavant Jadhav","doi":"10.1007/s42107-025-01293-z","DOIUrl":"10.1007/s42107-025-01293-z","url":null,"abstract":"<div><p>Structural health monitoring (SHM) in civil engineering structures is essential for ensuring structural integrity and safety. The current study presents an integration of particle swarm optimization (PSO) with a support vector machine (SVM) model for SHM of joints in steel frame structures with statistical features of vibration data. In the study, the PSO is employed to optimize the SVM hyperparameters (penalty parameters and Gaussian kernel function) to enhance accuracy and robustness. For that purpose, a five-story 3D steel frame structure is considered. An impact excitation is used to excite the structure and record the time-history acceleration data for both damaged and undamaged cases. From the data, the statistical features were extracted and used as input to the PSO-based SVM model. The training and testing results show that the model is effective in distinguishing between undamaged and damaged cases. This study creates a robust model for SHM applications, advancing the development of autonomous structural evaluation systems.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 4","pages":"1837 - 1846"},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698543","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":"Study the effect of blast load on the steel offshore platforms","authors":"Abdulameer Al-Mubarak,&nbsp;Yahya Mohammad Younus","doi":"10.1007/s42107-025-01290-2","DOIUrl":"10.1007/s42107-025-01290-2","url":null,"abstract":"<div><p>The most vital and productive facilities at the present time are more exposed to blasting, especially offshore oil exporting platforms. The current paper studies the effect of a blast on a fixed offshore steel platform with dimensions of (20 × 20) meters, a height of 30 m, and a water level 10 m below the platform deck. ABAQUS finite element software was used to solve and analyze the problem appropriately. To find out the most dangerous blasting on the platform at this moment, two different locations of blasting were taken, the first on the platform deck and the other near the platform on the surface of the water. The amount of the blasting material TNT was also increased to find out its effect on the platform. The study proved that it is very necessary to design offshore platforms taking into account the blast loads and predicting the critical condition of the blasting. In this study, the location of the blast on the water surface and near the platform was the most dangerous and critical on the response of the platform and may have led to the collapse. Also, the near elements from the blasting source, the blast has less effect. Also, the steel platform structure is characterized by being resistant to blast due to the lack of strong bumpers to repel the blast.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 4","pages":"1847 - 1864"},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698577","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":"Optimization of time–cost–quality-CO2 emission trade-off problems via super oppositional TLBO algorithm","authors":"Mohammad Azim Eirgash","doi":"10.1007/s42107-025-01282-2","DOIUrl":"10.1007/s42107-025-01282-2","url":null,"abstract":"<div><p>The teaching–learning-based optimization (TLBO) algorithm is widely recognized for its efficiency and effectiveness in solving optimization problems. However, it often encounters challenges with premature convergence, leading to local optimal solutions. To address this limitation, this study introduces an enhanced variant of TLBO, denoted as super oppositional teaching–learning-based optimization (SOTLBO) algorithm. This enhancement introduces a novel super opposition learning (SOL) strategy, which retains superior candidate solutions by simultaneously evaluating an individual and its corresponding opposite individual. The proposed SOTLBO is applied to a time–cost–quality-CO₂ emission (TCQCE) trade-off problem involving a 33 activity project that considers all logical dependencies among activities. Results demonstrate that SOTLBO achieves faster convergence and higher-quality optimal solutions. To assess the algorithm’s effectiveness, its performance is compared with well-established algorithms: slime mold algorithm opposition tournament mutation (SMOATM), golden ratio sampling based random oppositional aquila optimization (GRS-ROAO), and plain TLBO algroithms. Statistical analysis highlights that SOTLBO outperforms these algorithms, achieving the highest hyper-volume (HV) value of 0.889 and the suitable mean ideal distance (MID) and spread (SP) values of 1.918 and 0.382, respectively, for the 33 activity project. These findings highlight SOTLBO’s superior ability to enhance diversity and ensure more uniform solution distributions compared to other multi-objective evolutionary algorithms.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 4","pages":"1743 - 1755"},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698541","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":"Self-sensing capacity of strain-hardening fiber-reinforced cementitious composites: machine learning prediction and experimental validation","authors":"Duy- Liem Nguyen,&nbsp;Tan-Duy Phan","doi":"10.1007/s42107-025-01291-1","DOIUrl":"10.1007/s42107-025-01291-1","url":null,"abstract":"<div><p>This study focuses on the self-sensing capacity, which is indicated by gauge factor (GF) of strain-hardening fiber-reinforced cementitious composites (SH-FRCCs) for flexural specimen. At first, a machine learning model using a hybrid Random Forest–Particle Swarm Optimization (RF-PSO) technique was proposed to predict the GF for SH-FRCCs under direct tension. After that, an experimental program was conducted to validate the RF-PSO model in predicting GF of SH-FRCCs at the tensile zone of the flexural specimen. A dataset comprising 86 samples gathered from multiple previous studies was utilized to train and evaluate the proposed RF-PSO model. Eight potential input variables were considered: matrix strength (<span>(sigma_{mu})</span>), fiber type (FT), fiber geometry (<span>(L_{f} /d_{f})</span>), fiber volume content (<span>(V_{f})</span>), post-cracking strength (<span>(sigma_{pc})</span>), strain capacity (<span>(varepsilon_{pc})</span>), initial electrical resistivity (<span>(rho_{i})</span>), electrical resistivity at post cracking (<span>(rho_{c})</span>). The effectiveness of the hybrid RF-PSO model was assessed via four statistical metrics: R² (coefficient of determination), MSE (mean squared error), MAE (mean absolute error), and RMSE (root mean squared error). The analytical results showed that the proposed RF-PSO model showed excellent accuracy, with R² values of 0.935 in the training stage and 0.737 in the testing stage. The hybrid RF-PSO model demonstrated superior predictive performance compared to the pure RF model in predicting the GF of SH-FRCCs, improving the R² values by 1.05 and 1.14 times in the training and testing stages, respectively. Furthermore, one-dimensional partial dependence plot (PDP-1D) was used to investigate the effect of input variables on the GF of SH-FRCCs. It was found that the <span>(sigma_{pc})</span> and <span>(rho_{c})</span> extremely impacted to the GF predictions. The experimental results showed that the error between the experimental values and RF-PSO predictions is less than -13.63%, thus the proposed model in this study have high generalization capability in predicting the GF of SH-FRCCs.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 4","pages":"1801 - 1818"},"PeriodicalIF":0.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698516","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":"Integrating environmental sustainability in construction Time-Cost trade-off for decision-making using hybrid NSGA-III and MOPSO approach","authors":"Analene Montesines Nagayo,&nbsp;Rekha Singh,&nbsp;Amit Dhawan,&nbsp;T. C. Manjunath,&nbsp;Altayeb Qasem,&nbsp;Krushna Chandra Sethi,&nbsp;Kamal Sharma","doi":"10.1007/s42107-025-01265-3","DOIUrl":"10.1007/s42107-025-01265-3","url":null,"abstract":"<div><p>Construction project management often involves optimizing time and cost while ensuring minimal environmental impact. This study presents an innovative hybrid approach combining non-dominated sorting genetic algorithm III (NSGA-III) and multi-objective particle swarm optimization (MOPSO) to address the time-cost-environmental sustainability trade-off (TCEST) in construction projects. The proposed model aims to minimize project completion time and cost while maximizing environmental sustainability. A case study is conducted to validate the model, incorporating diverse construction activities and their respective time, cost, and environmental sustainability metrics. The results reveal Pareto-optimal solutions demonstrating significant trade-offs among the three objectives. The hybrid approach outperforms standalone algorithms in terms of solution diversity, convergence, and hypervolume indicators. Weighted sum methods are employed to select the most suitable solution from the Pareto front based on project priorities. Correlation analysis further explores interdependencies among objectives, emphasizing the feasibility of balancing these critical factors. The study contributes a robust decision-support tool for sustainable project planning, facilitating informed decision-making in modern construction management.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 4","pages":"1527 - 1542"},"PeriodicalIF":0.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698517","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":"Experimental investigation on fresh and hardened geopolymer concrete by considering various mix design parameters","authors":"Smita Patil,&nbsp;Deepa A. Joshi","doi":"10.1007/s42107-024-01259-7","DOIUrl":"10.1007/s42107-024-01259-7","url":null,"abstract":"<div><p>Fly ash and ground granulated blast furnace slag (GGBS) were mixed with each other at different percentages and various solution-to-binder ratios were used to form the geopolymer concrete (GPC). Sodium hydroxide (NaOH) and sodium silicate (Na2SiO3) make up the alkaline solution, which is used to activate aluminosilicates. NaOH of 8M and 10M concentration was used for experimental work. Different solution-to-binder ratios from 0.4 to 0.45 were studied in this research work. Na2SiO3/NaOH ratio is considered lower for experimental work to achieve economical concrete without compromising mechanical properties. Ambient curing and temperature-maintained curing were used in this research work for curing of GPC specimens. The slump cone test and compressive strength test were carried out on the GPC to analyse the results of workability and strength by varying the mix design parameters of GPC. Experimental results show that changes in solution-to-binder ratio, concentration of NaOH, type of curing, proportion of raw material, and properties of raw materials also affect the fresh and hardened properties of GPC.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 4","pages":"1479 - 1494"},"PeriodicalIF":0.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698518","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 enhanced framework for multi-objective optimization of cement-slag concrete for the balancing performance, economics, and sustainability","authors":"Amol Shivaji Mali,&nbsp;Atul Kolhe,&nbsp;Pravin Gorde,&nbsp;Sandesh Solepatil","doi":"10.1007/s42107-025-01276-0","DOIUrl":"10.1007/s42107-025-01276-0","url":null,"abstract":"<div><p>This research presents an innovative computational approach that merges artificial intelligence with multi-objective optimization techniques to enhance cement slag concrete design. The proposed framework integrates deep neural networks (DNN), gradient boosting machines (GBM), and extreme learning machines (ELM) with particle swarm optimization (PSO) and multi-objective genetic algorithms (MOGA) to concurrently optimize mechanical properties, cost-effectiveness, and environmental impact. The methodology involved comprehensive data pre-processing, model training, and validation using laboratory tests. Among the models, DNN exhibited the best performance in predicting the uniaxial compressive strength (UCS), achieving an R² of 0.98, and MSE of 0.009, surpassing both the GBM and ELM models. The application of PSO-optimized hyperparameters considerably improved the model accuracy, whereas MOGA identified the optimal mix designs through Pareto front analysis. Grey Relational Analysis determined an ideal cement-to-slag ratio of 85:15, yielding a UCS of 59.8 MPa and the highest grey relational grade (γi = 0.982). The framework achieved a 15% enhancement in the strength-to-cost ratio compared to traditional methods while maintaining environmental advantages through decreased cement usage. This study shows the potential of integrated AI-driven approaches in developing sustainable building materials, offering a solid foundation for future advancements in concrete mix design optimization that balances performance, cost, and environmental factors.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 4","pages":"1669 - 1681"},"PeriodicalIF":0.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698515","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 building design: optimizing building geometry and external envelope to minimize energy consumption and CO2 emissions","authors":"Rohit R. Salgude,&nbsp;Manish Ghag,&nbsp;Vishakha Sakhare,&nbsp;Shubhangi Shekokar","doi":"10.1007/s42107-025-01279-x","DOIUrl":"10.1007/s42107-025-01279-x","url":null,"abstract":"<div><p>Large energy use throughout a building's operating phase leads to greater energy expenditures. Optimising energy use is therefore essential to reducing these costs. Pre-construction stages can be used to investigate several options to make this happen using building optimisation. This study aims towards optimizing energy consumption, CO₂ emissions from the use of electricity, and saving energy costs by investigating several design options in the pre-construction phase. Specifically, this study analyses building geometry, utilising the Autodesk Insight software to determine best possible orientation, windows-to-wall ratio (WWR), and glass selection. A commercial building in Mumbai serves as the case study, where the building model is examined in three different geometries. Each geometry is rotated 360° at 90-degree intervals, utilising Building Information Modelling (BIM) to apply energy-efficient design considerations, and integrating its operating schedule. Five options of window glass and WWR are examined in the study, along with four distinct building orientations, leading to a comprehensive analysis of 300 scenarios. The objective is to identify the configuration that optimises energy costs while reducing energy use and CO₂ emissions. The findings of this research provide valuable insights for building designers and developers seeking to minimize energy use, cut CO₂ emissions, and design low-carbon, energy-efficient buildings.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 4","pages":"1703 - 1722"},"PeriodicalIF":0.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698519","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":"Advanced strategies in earthquake-resistant structural engineering: seismic design, materials, and innovations","authors":"V. Ramesh,&nbsp;M. Indhumathi Anbarasan,&nbsp;B. Muthuramu","doi":"10.1007/s42107-025-01298-8","DOIUrl":"10.1007/s42107-025-01298-8","url":null,"abstract":"<div><p>This paper is a general overview of the advanced strategies involved in earthquake-resistant structural engineering, including seismic design, materials, and innovations. Earthquakes are threats to infrastructure and human safety and thus demand effective and scalable design principles and materials. This study uniquely integrates advanced materials such as high-performance concrete, fiber-reinforced polymers, shape memory alloys, and engineered cementitious composites with cutting-edge technologies like IoT-based structural health monitoring and AI-driven seismic response prediction. It emphasizes the importance of stringent building codes, base isolation systems, and energy dissipation devices in enhancing structural resilience. It discusses the latest approaches: performance-based seismic design, adaptive control strategies, and integrated methods to face the challenges presented with a multiple-hazard setting. Resilience-based frameworks of fast recovery following an earthquake significantly bridge gaps that pertain to the long-term effectiveness, scalability, and feasibility of advanced seismic methodologies. It emphasizes the role of interdisciplinary and continuous development in earthquake-resistant engineering as a means of safeguarding urban environments in seismic regions, thereby fostering sustainability and economic viability.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 4","pages":"1413 - 1428"},"PeriodicalIF":0.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698520","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}