Asian Journal of Civil Engineering最新文献

{"title":"Optimizing self-compacting concrete with steel slag and fiber additions: enhancing fresh, mechanical, durability, and microstructural properties","authors":"Sabhilesh Singh,&nbsp;Vivek Anand","doi":"10.1007/s42107-024-01214-6","DOIUrl":"10.1007/s42107-024-01214-6","url":null,"abstract":"<div><p>Building on previous research, which demonstrated that 50% steel slag replacement for fine aggregates optimally enhanced the fresh, mechanical, and durability properties of self-compacting concrete (SCC) (Singh &amp; Anand, 2024), this study further investigates the effects of incorporating steel and polypropylene fibers to improve performance. Steel fibers were added in proportions ranging from 0.5 to 2%, while polypropylene fibers were varied within the same range. The concrete mix design was based on IS 10262:2019, and EFNARC guidelines were followed to ensure the concrete mix met international standards for fresh properties such as flowability, passing ability, and viscosity. The results show that steel fiber significantly outperformed polypropylene fiber in terms of strength, with the optimal addition of 2% steel fiber resulting in compressive strengths of 51.5 MPa at 7 days, 71.8 MPa at 28 days, and 78.1 MPa at 56 days, along with notable improvements in tensile and flexural strengths. In contrast, the addition of 0.5% polypropylene fiber demonstrated optimal fresh properties but provided relatively lower strength. Durability tests, including water absorption and sulfate attack resistance, indicated superior performance with steel fiber, exhibiting lower water absorption and reduced weight loss under aggressive conditions. Microstructural analysis via SEM and XRD confirmed a denser interfacial transition zone (ITZ) and better bonding with steel fiber compared to polypropylene fiber. Overall, the incorporation of steel fiber in SCC with steel slag replacement leads to superior strength and durability, making it a promising solution for high-performance concrete applications.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 2","pages":"683 - 699"},"PeriodicalIF":0.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142994504","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":"Evaluating the impact of construction delays on project duration using machine learning and multi-criteria decision analysis","authors":"Ahmed salama","doi":"10.1007/s42107-024-01196-5","DOIUrl":"10.1007/s42107-024-01196-5","url":null,"abstract":"<div><p>Construction projects are inherently complex and prone to delays, significantly impacting project timelines and costs. This study addresses the critical issue of construction delays in Jordan by leveraging advanced methodologies such as Gaussian Process Regression (GPR) and the Analytical Hierarchy Process (AHP). The problem of accurately predicting and managing delays in construction projects has long challenged the industry, with existing approaches often failing to account for the multifaceted nature of delay factors. This research integrates GPR, a machine learning technique, with AHP, a Multi-Criteria Decision Analysis (MCDA) tool, to evaluate and predict the impact of delay factors on project duration. The study employs a comprehensive dataset comprising 191 construction projects in Jordan, with critical variables identified through expert evaluations and literature reviews. The GPR model demonstrated superior predictive capabilities, achieving an R² value close to 1, indicating its high accuracy in forecasting time and cost overruns. The AHP model, on the other hand, prioritized weather conditions and unrealistic contract requirements as the most significant contributors to delays. The findings suggest that the combined application of GPR and AHP offers a robust framework for predicting and managing construction delays, providing valuable insights for improving project management practices. Future work should focus on expanding the dataset and refining the models to enhance their applicability across different regions and project types.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 1","pages":"389 - 399"},"PeriodicalIF":0.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906054","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":"Performance of perforated double steel plate reinforcements in composite shear walls under axial compression","authors":"Chen Dejin,&nbsp;S.M. Iqbal S. Zainal,&nbsp;Salinah Dullah,&nbsp;Ahmad Nurfaidhi Rizalman,&nbsp;Noor Sheena Herayani Harith","doi":"10.1007/s42107-024-01204-8","DOIUrl":"10.1007/s42107-024-01204-8","url":null,"abstract":"<div><p>The double steel plate concrete shear wall (DSCSW) is an innovative vertical and lateral structural component composed of steel plates and concrete, linked through shear joints. These shear walls generally serve as the primary elements responsible for carrying lateral loads in tall structures. This research aims to evaluate the performance of steel plates with perforated openings in concrete shear walls under monotonic axial compression. In the preliminary stage, eight shear wall designs were evaluated using Finite Element (FE) analysis. The influence of different distance-to-thickness ratios, concrete thickness, perforated opening ratio, and load-bearing capacities were explored to establish the interaction effects of these different components on the design’s performance. In the primary stage, the numerical FE results were experimentally verified. The results indicated that the distance-to-thickness ratios in the design play a significant role in the failure mode of the wall. An appropriate opening rate can delay the occurrence of buckling, allowing the steel plate and concrete to perform their respective roles in terms of durability and safety. Conclusively, the FE models demonstrated a minimal degree of deviation when compared to the experimental data across all evaluated criteria.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 2","pages":"531 - 548"},"PeriodicalIF":0.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142995800","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 autogenous shrinkage in ultra-high-performance concrete (UHPC) using hybridized machine learning","authors":"Md Ahatasamul Hoque,&nbsp;Ajad Shrestha,&nbsp;Sanjog Chhetri Sapkota,&nbsp;Asif Ahmed,&nbsp;Satish Paudel","doi":"10.1007/s42107-024-01212-8","DOIUrl":"10.1007/s42107-024-01212-8","url":null,"abstract":"<div><p>This study explores hybridized machine learning (ML) techniques to predict autogenous shrinkage (AS) in ultra-high-performance concrete (UHPC). The ensemble model, namely random forest (RF), extra tree regressor (ETR), light gradient boosting machine (LGBM), and extended gradient boosting (XGBoost), are adopted as the base algorithm. Further, a newly developed Sparrow Search Algorithm (SSA) is hybridized with XGBoost and proposed in the study for the prediction of shrinkage. The study adopts K-fold cross-validation to reduce the risk of overfitting. The results show that the hybridization of SSA-XGBoost outperforms all the algorithms with those without optimization, with the highest performance of R² of 0.91 and RMSE of 79.2 in the testing set. The model is subjected to five-fold cross-validating, ensuring the model is not overfitted. Regarding RMSE, the performance of other models like XGB, LGBM, ETR, and RF is restricted to 102.22,108.38,87.42 and 98.57, respectively. Further, the study incorporated the model explainability behavior and revealed that the curing relative humidity (CRH), steel fiber content (SFS), and sand are the highly influential features for predicting AS. The comprehensive assessment helps understand the parameters influencing AS, making it a helpful tool for researchers to make well-informed decisions.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 2","pages":"649 - 665"},"PeriodicalIF":0.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142995797","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":"Influence of corner geometry on wind-induced forces in tall building models","authors":"Rahul Kumar Meena,&nbsp;Ajay Pratap,&nbsp;Ritu Raj,&nbsp;S. Anbukumar","doi":"10.1007/s42107-024-01208-4","DOIUrl":"10.1007/s42107-024-01208-4","url":null,"abstract":"<div><p>Structural designers are compelled to opt for tall buildings to accommodate the growing population throughout the world. This research study presents a numerical analysis of tall building models, both regular and irregular in cross sectional shape, under the influence of wind forces. Wind load assessments were conducted using the computational fluid dynamics tool ANSYS CFX. The results are illustrated through various graphical representations, including pressure contours, mean pressure along the vertical centreline, and across the peripheral distance at different heights such as 250, 375 and 500 mm from the base of the model. Among the regular-shaped models, the rectangular chamfered corner design demonstrated superior wind resistance, while in the case of the irregular Y-shaped models, the design with corner cuts performed best in terms of resisting the strong wind and performed well in compression to other plan shape. The present research study also utilized the ANN-based forecasting model and different parameters are varied while the average surface pressure coefficient considered as the output. The findings indicated a strong co-ordination among the predicted outcomes and the given data, with a maximum forecasting error of less than 5%, was observed. The study concludes with a comparative analysis of irregular and regular-shaped models with equal floor areas, each featuring chamfered and filleted corners.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 2","pages":"593 - 616"},"PeriodicalIF":0.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142995865","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":"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}