Asian Journal of Civil Engineering最新文献

{"title":"Optimizing sustainability and resilience of composite construction materials using life cycle assessment and advanced artificial intelligence techniques","authors":"Prashant B. Pande,&nbsp;Sagar W. Dhengare,&nbsp;Jayant M. Raut,&nbsp;Rajesh M. Bhagat,&nbsp;Boskey V. Bahoria,&nbsp;Nilesh Shelke,&nbsp;Sachin D. Upadhye,&nbsp;Vikrant S. Vairagade","doi":"10.1007/s42107-024-01200-y","DOIUrl":"10.1007/s42107-024-01200-y","url":null,"abstract":"<div><p>The need for sustainable and resilient composite construction materials that can cope with increasing environmental and structural demands of modern construction is becoming urgently critical. The proposed model will handle the lack of sustainability and mechanical performance of the existing approaches. Specifically, they are not capable of dynamically adapting up to the changing environmental conditions and the intrinsic complexity of optimizing the material properties for the composites. To overcome these constraints, the present study develops an innovative multimethod framework by integrating the implementation of several state-of-the-art optimization and machine-learning techniques in order to enhance the sustainability and resilience of composite materials. The work is initialized by proposing a Multiple Objective Genetic Algorithm (MOGA) with dynamic fitness functions for the optimization of material designs, by balancing environmental impacts with mechanical performance in real time. This approach, hence, fits different environmental conditions and material requirements at the same time while importantly enhancing the design stage itself. At the same time, Gaussian Process Regression is the method that enables future LCA outcome prognoses undertaken using RL; it is possible to deal with the sustainability prediction as uncertain, and hence it is incorporated in the ongoing process of material optimization. In this way, RL will adaptively optimize processing parameters for the manufacturing of composites: both material resilience and goals regarding sustainability are realized through self-learning. Finally, a hybrid Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithm is introduced to probe and polish the solution space for composite material designs to leap over local optima hurdles. The overall improvement in the integrated attributes is 15% of the carbon footprint decrease, 20% in the tensile strength, and 12% decrease in energy consumption during processing. This study exemplifies one of the outstanding novel designs of composite materials, offering dynamism, adaptiveness, and robustness in enhancement of sustainability and resilience parameters in the process.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 2","pages":"471 - 489"},"PeriodicalIF":0.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142995866","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":"Serviceability assessment of lively skywalk footbridge under dynamic loadings of human motions in the vadodara metropolitan area","authors":"Yati R. Tank,&nbsp;G. R. Vesmawala","doi":"10.1007/s42107-024-01209-3","DOIUrl":"10.1007/s42107-024-01209-3","url":null,"abstract":"<div><p>Urbanisation and increased pedestrian traffic in metropolitan areas necessitate the construction of skywalk footbridges. This study focuses on the serviceability assessment of the lively skywalk footbridge at Vadodara Railway Station, which experiences significant pedestrian footfall during peak hours, potentially leading to vibration issues. The objective of this study is to evaluate the dynamic performance of skywalks under various human-induced loads via finite element analysis (FEA) and field measurements. Finite element analysis revealed that the bridge’s first natural frequency is 1.8652 Hz, which is within the range of pedestrian-induced frequencies, potentially causing resonance. The maximum vertical displacement under running conditions reached 21.4527 mm, and the peak vertical acceleration was 0.8474 m/s², exceeding the comfort limit of 0.5 m/s² per Eurocode 1 standards. Field measurements confirmed the numerical findings, with similar displacement and acceleration values observed. The study concludes that skywalks do not meet serviceability criteria under dynamic pedestrian loads, particularly during high-traffic periods. To address these concerns, the implementation of vibration mitigation measures, such as tuned mass dampers (TMDs), is recommended to improve the bridge’s serviceability and ensure pedestrian comfort.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 2","pages":"617 - 634"},"PeriodicalIF":0.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142995744","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":"Smart housing: integrating machine learning in sustainable urban planning, interior design, and development","authors":"Mazin Arabasy,&nbsp;Mayyadah F. Hussein,&nbsp;Rana Abu Osba,&nbsp;Samah Al Dweik","doi":"10.1007/s42107-024-01144-3","DOIUrl":"10.1007/s42107-024-01144-3","url":null,"abstract":"<div><p>Smart housing, therefore, theoretically becomes very vital in this context of a smart city for sustainable urban planning and development. Machine learning technologies can be considered quite fundamental in enhancing efficiency, sustainability, and livability through incorporating into smart housing. However, rapid urbanization, population growth, traffic congestion, and energy management are huge problems. The main objective of this research work is to identify the feasibility of ML application in smart housing for resource management optimization, environmental sustainability, and public safety. It conducts an analysis on key factors like energy consumption, waste management, and public safety measures by applying machine learning’s efficient algorithms on the comprehensive dataset. There is a 20% decrease in total energy consumption, 15% increase in renewable source energy consumption, and a 25% efficiency improvement in waste management. In addition, public safety response times decreased by 30%. Also, ML models gave out very accurate predictions for power use, traffic patterns, and air quality that turned out with an average accuracy of 92%, thus saving 10% carbon emissions. The study clearly showed that ML will play a very key role in housing planning and interior design. The results bring out the importance of ML in tackling challenging urban issues and promoting better sustainable urban planning practices.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 1","pages":"59 - 71"},"PeriodicalIF":0.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906081","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":"Multi-objective optimization of school environments to foster nature connectedness using NSGA-III in school design","authors":"Sonali Walimbe,&nbsp;Rama Devi Nandineni,&nbsp;Sumita Rege","doi":"10.1007/s42107-024-01203-9","DOIUrl":"10.1007/s42107-024-01203-9","url":null,"abstract":"<div><p>The integration of nature into school environments has been shown to enhance student well-being and academic performance, fostering a deeper sense of connection to the natural world. However, designing school infrastructure that balances nature exposure, sustainability, and cost-effectiveness remains a challenge. This study addresses the multi-objective optimization of school construction designs to foster nature connectedness using the non-dominated sorting genetic algorithm III (NSGA-III). The optimization objectives include maximizing green space for nature exposure, minimizing construction and maintenance costs, maximizing sustainability in materials and processes, and optimizing space utilization efficiency. Constraints related to budget, space, and environmental regulations are also incorporated. By applying NSGA-III, this research generates Pareto-optimal solutions that offer trade-offs between competing objectives, such as enhancing nature exposure while controlling costs and ensuring sustainability. The study compares these optimized designs with traditional school construction approaches, highlighting the benefits of using multi-objective optimization in creating environmentally conscious, cost-effective educational spaces. The results demonstrate that NSGA-III is an effective tool for optimizing school designs that prioritize nature connectedness while adhering to practical constraints. This research provides valuable insights for construction managers, architects, planners, and policymakers involved in the design and construction of sustainable educational environments.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 2","pages":"515 - 530"},"PeriodicalIF":0.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142995689","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":"Exploring the potential of Himalayan Giant Nettle fiber and supplementary cementitious materials for sustainable concrete development","authors":"Ajaya Subedi,&nbsp;Bhum Bahadur Thapa,&nbsp;Ashish Poudel,&nbsp;Binaya Adhikari,&nbsp;Binod Khadka,&nbsp;Samrat Poudel,&nbsp;Sanjog Chhetri Sapkota","doi":"10.1007/s42107-024-01211-9","DOIUrl":"10.1007/s42107-024-01211-9","url":null,"abstract":"<div><p>The concrete industry’s increasing demand poses a significant environmental challenge due to its high carbon footprint. Addressing this issue requires the integration of sustainable materials into concrete production. This study aimed to evaluate the Himalayan Giant Nettle (HGN) fiber as a novel natural reinforcement alternative, alongside Fly Ash (FA) and Rice Husk Ash (RHA) as supplementary cementitious materials (SCMs) in M30 concrete. While FA and RHA have been almost thoroughly researched as SCMs to enhance concrete characteristics and lower cement usage, the use of HGN fiber in concrete remains to be explored. The FA and RHA replacements of 20% by weight enhanced compressive strength (CS), flexural strength (FS) and split tensile strength (STS), respectively. HGN fibers at 1% volume optimally increased CS, FS, and STS by 16.2%, 33.33%, and 36.90%, respectively. However, exceeding 1% HGN fiber content negatively affected workability and strength. The fiber’s ability to bridge cracks, reduce stress concentration, and improve flexibility contributed to the observed improvements. Further, HGN fiber’s low cost, renewable nature, and potential to significantly reduce concrete weight make it a promising sustainable option. This paper confirms the significant positive impact of incorporating HGN fiber as a promising eco-friendly alternative for concrete reinforcement, thereby contributing to the ongoing research on developing more eco-friendly construction materials.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 2","pages":"635 - 648"},"PeriodicalIF":0.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142995687","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 analytical method to determine the ultimate shear strength of continuous steel plate girders","authors":"Aliakbar Hayatdavoodi,&nbsp;Yancheng Li","doi":"10.1007/s42107-024-01206-6","DOIUrl":"10.1007/s42107-024-01206-6","url":null,"abstract":"<div><p>This study presents a straightforward method for predicting the ultimate shear capacity of Continuous Steel Plate Girders (CSPG); an accurate solution is obtained by incorporating the moment/shear ratio into an equilibrium equation originally designed for Single-span Steel Plate Girders (SSPG). A detailed three-dimensional finite element model was developed to effectively capture the geometric and material nonlinearities characteristic of plate girders. This model exhibits high precision and is particularly useful in the early design phases of CSPG. A parametric analysis reveals that the ultimate shear strength of CSPG decreases almost linearly as the moment/shear ratio increases. The analytical results were compared with finite element outcomes and existing experimental data to validate the proposed method, showing a strong agreement. This consistency underscores the method’s reliability and suitability for practical design applications.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 2","pages":"565 - 575"},"PeriodicalIF":0.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142995690","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":"Prediction of swelling pressure of expansive soil using machine learning methods","authors":"Sumit Gahlot,&nbsp;Rajat Mangal,&nbsp;Abhishek Arya,&nbsp;Barada Prasad Sethy,&nbsp;Krushna Chandra Sethi","doi":"10.1007/s42107-024-01205-7","DOIUrl":"10.1007/s42107-024-01205-7","url":null,"abstract":"<div><p>Expansive soils present significant challenges in construction engineering due to their ability to swell, leading to structural damage. Accurate prediction of swelling pressure is essential for safe construction designs. This study compares the performance of four machine learning models, that is, Multiple Linear Regression (MLR), Decision Tree Regression (DTR), Support Vector Regression (SVR), and Artificial Neural Networks (ANN) in predicting swelling pressure based on soil properties such as KCl concentration, dry density, and moisture content. The ANN model outperformed the others, achieving the highest prediction accuracy with an R² of 0.92 and a mean squared error (MSE) of 0.0008 on the test data. SVR also performed well, with an R² of 0.91 and an MSE of 0.02. In contrast, the MLR model had an R² of 0.82 and an MSE of 0.002, while DTR had the lowest generalization capability with an R² of 0.54 and an MSE of 0.02. Although black box models like ANN and SVR are less interpretable, they significantly outperform traditional models like MLR and DTR in terms of prediction accuracy. This study highlights the potential of machine learning techniques in construction engineering and suggests that black box models, particularly ANN, can be highly effective in predicting swelling pressure with greater precision.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 2","pages":"549 - 564"},"PeriodicalIF":0.0,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142995717","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":"Exploring steel fiber integration in dry lean concrete: predictive analysis of compressive strength and performance via machine learning","authors":"Prasenjit Kumar,&nbsp;Prince Yadav,&nbsp;Vikash Singh","doi":"10.1007/s42107-024-01188-5","DOIUrl":"10.1007/s42107-024-01188-5","url":null,"abstract":"<div><p>This research investigates the effects of varying percentages of steel fibers (1%, 1.5%, 2.5%, 3.5%, 4.5%) on the compressive strength of Dry Lean Concrete (DLC). The study aims to identify the optimal steel fibre content for enhancing compressive strength and explore the use of machine learning techniques for performance prediction. The experimental program involved casting and testing DLC specimens with different steel fibre contents. The compressive strength was evaluated at 7, 14, and 28 days. Machine learning methods like as linear regression, decision trees, and random forest were used to predict compressive strength while accounting for fiber content and curing period. The results indicate a significant improvement in compressive strength with increasing fibre content up to 3.5%, beyond which the strength gain diminishes. The machine learning models demonstrated high accuracy in predicting compressive strength, with random forest providing the best performance. This research offers useful insights into the design of fiber-reinforced DLC and demonstrates the potential of machine learning in performance prediction.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 1","pages":"263 - 271"},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906048","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 approaches to soil-structure interaction under seismic loading: predictive modeling and analysis","authors":"Ahmad Alkhdour,&nbsp;Tamer shraa","doi":"10.1007/s42107-024-01146-1","DOIUrl":"10.1007/s42107-024-01146-1","url":null,"abstract":"<div><p>Soil-structure interaction (SSI) under seismic loading is a rather complex phenomenon that has immense effects on the seismic performance of structures. Traditional approaches are the finite element method (FEM) and the boundary element method (BEM), which have been used rather widely in analyzing SSI. Both methods usually fail to capture the complex dynamics of the underlying process. Recent advances in machine learning offer promising alternatives for predictive modeling and analysis of SSI. This paper deals with the applicability of the XGBoost machine learning model, optimized with particle swarm optimization (PSO) in predicting Soil-Structure Interaction under Seismic Loading. The presented model shows accuracy with mean squared error (MSE): 0.04, Root Mean Squared Error (RMSE): 0.2, R-squared (R²): 0.95, and mean absolute error (MAE): 0.1. The results show the better performance of the model over traditional methods like the finite element method (FEM) and the boundary element method (BEM). Comparisons through visualization show that there were close agreements in the displacements predicted and real displacements. Stress distributions and stress–strain curves, predicted from the analysis, validate the model's accuracy. The important outcomes are that the model can deliver more accurate and reliable predictions, enhancing seismic design, and safety to a great extent. It contributes to the literature by being the first application of machine learning combined with an optimization technique; it provides a full comparison to traditional methods for the community and shows future research opportunities, for example, including real-time seismic data or exploring model transferability.</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":"25 8","pages":"5781 - 5792"},"PeriodicalIF":0.0,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587908","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":"Enhancing the predictive accuracy of recycled aggregate concrete’s strength using machine learning and statistical approaches: a review","authors":"Jawad Tariq,&nbsp;Kui Hu,&nbsp;Syed Tafheem Abbas Gillani,&nbsp;Hengyu Chang,&nbsp;Muhammad Waqas Ashraf,&nbsp;Adnan Khan","doi":"10.1007/s42107-024-01192-9","DOIUrl":"10.1007/s42107-024-01192-9","url":null,"abstract":"<div><p>Recycled aggregate concrete (RAC) has emerged as a sustainable alternative in the construction industry, reducing environmental impacts. However, predicting the mechanical properties of RAC using traditional experimental methods is challenging due to material variability and the complex interactions within the concrete matrix. This review paper explores the application of machine learning (ML) techniques for predicting the engineering properties of RAC, with a focus on compressive strength (CS), split tensile strength (STS), and durability. Various ML models, including artificial neural networks (ANN), support vector machines (SVM), and ensemble methods, are examined for their effectiveness in handling high-dimensional data and modeling non-linear relationships. The paper emphasizes the critical role of input parameters such as the water-to-cement ratio (W/C), aggregate replacement ratio, and curing period in determining RAC strength. It also discusses the advantages of ML over conventional statistical methods in predicting RAC properties, demonstrating enhanced accuracy and predictive reliability. Recommendations for future research include adopting hybrid ML approaches and further exploring feature importance analysis to optimize RAC mix designs. This comprehensive review highlights the potential of ML to revolutionize material property predictions and promote the informed use of recycled materials in construction.</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 1","pages":"21 - 46"},"PeriodicalIF":0.0,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906045","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}