Asian Journal of Civil Engineering最新文献

{"title":"Integration of earth-air heat exchangers in sustainable construction: a hybrid NSGA-III/Dual simplex approach for multi-objective optimization","authors":"Akash Deep Yadav,&nbsp;Sujit Kumar Verma,&nbsp;Vikas Kumar Sharma","doi":"10.1007/s42107-024-01251-1","DOIUrl":"10.1007/s42107-024-01251-1","url":null,"abstract":"<div><p>This study explores the integration of Earth-Air Heat Exchangers (EAHE) in sustainable construction to enhance energy efficiency and environmental sustainability. EAHE systems leverage stable underground temperatures to precondition air for HVAC systems, reducing energy consumption, carbon emissions, and operational costs. A novel hybrid optimization approach combining Non-Dominated Sorting Genetic Algorithm III (NSGA-III) and the Dual Simplex method is proposed to address multi-objective challenges, including minimizing lifecycle costs, maximizing energy performance, reducing carbon emissions, and minimizing installation time. The hybrid methodology utilizes NSGA-III for global Pareto optimization and the Dual Simplex method for precise local refinement of constrained objectives. A case study on a 150 m² residential building in Mathura, Uttar Pradesh, demonstrates the model's efficacy, achieving a 22% reduction in lifecycle costs and a 32% improvement in energy performance compared to conventional designs. Trade-off, sensitivity, and comparative analyses highlight the hybrid model's superiority over traditional methods in balancing cost, efficiency, and sustainability. These findings provide actionable insights for implementing EAHE systems in residential and commercial buildings, offering a replicable framework for optimizing sustainable construction practices.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 3","pages":"1287 - 1304"},"PeriodicalIF":0.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638254","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":"Investigating effective parameters for enhancing energy efficiency with an attached solar greenhouse in residential buildings: a case study in Tabriz, Iran","authors":"Mohammad Hassan Abedini,&nbsp;Elham Sarkardehi,&nbsp;Hadi Bagheri Sabzevar","doi":"10.1007/s42107-024-01249-9","DOIUrl":"10.1007/s42107-024-01249-9","url":null,"abstract":"<div><p>The global energy crisis necessitates enhancing energy independence for regions and countries by advancing the utilization of renewable energy sources. Solar energy offers a sustainable method for enhancing energy efficiency in buildings through the integration of solar greenhouses or sunspaces. These passive solar systems play a vital role in reducing the reliance on air conditioning by capturing and utilizing solar heat, thereby improving overall building performance. This study aims to thermally analyze the performance of a solar greenhouse attached to a residential building. Using DesignBuilder (version 6.1) for simulation, a comprehensive parametric analysis was conducted to evaluate various factors, including depth, glass type, thermal mass, and roof slope angle, to determine their impact on the overall performance of the integrated solar greenhouse. The findings reveal that a residential building without a solar greenhouse consumes 3261.5 kWh annually for heating and 1535.5 kWh for cooling. Incorporating a basic solar greenhouse (2 m depth, double glazing glass with argon gas, and 20 cm of thermal mass) results in energy savings of 20.6% for heating and 10.9% for cooling. Furthermore, by simulating various influential parameters, the optimal configuration for the solar greenhouse was identified.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 3","pages":"1257 - 1271"},"PeriodicalIF":0.0,"publicationDate":"2024-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638192","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 integrated hybrid machine learning and statistical analysis towards strength prediction of sugarcane bagasse ash-based sustainable concrete","authors":"Rupali S. Balpande,&nbsp;Yashika A. Gaidhani,&nbsp;Monica N. Kalbande,&nbsp;Tejaswini G. Panse,&nbsp;Niteen T Kakade,&nbsp;Pranali R. Dandekar,&nbsp;Nilesh Shelke","doi":"10.1007/s42107-024-01245-z","DOIUrl":"10.1007/s42107-024-01245-z","url":null,"abstract":"<div><p>Concrete compressive strength prediction with bagasse ash incorporation as an additive in the concrete could be more accurately predicted via advanced machine learning and statistical validation methods within this study. It is acknowledged that the conventional models cannot capture the accurate and statistically robust complex interactions of variables involved in concrete mixtures, mainly when alternative materials are incorporated. In the present investigation, ANNs in the form of MLP model and SVR model are employed for compressive strength prediction and ANOVA analysis is performed to establish the statistical significance of the content of bagasse ash on concrete strength. The MLP model reached a good high accuracy of 95% on the training set and 93% on validation, while the MAE was 2.5 MPa, MSE was 1.2 MPa² with the SVR model reaching 92% accuracy on the training set and 90% on validation. ANOVA results showed strong effects of bagasse ash on compressive strength at F-statistic = 10.2, <i>p</i> = 0.001. The results showed that these data led to a significant improvement in predictive reliability relative to methods of conventional approaches. The outcome of such integration is the enhancement in the accuracy of concrete strength predictions, as well as support for the sustainable use of industrial by-products, prospective practical application of bagasse ash in the construction industry process.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 3","pages":"1193 - 1208"},"PeriodicalIF":0.0,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638644","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 sustainability in concrete construction: improving thermal resilience and structural strength with ground granulated blast furnace slag and ceramic powder","authors":"Amit Gautam,&nbsp;Smita Tung","doi":"10.1007/s42107-024-01250-2","DOIUrl":"10.1007/s42107-024-01250-2","url":null,"abstract":"<div><p>Concrete remains the most widely used construction material globally, but its environmental impact and need for improved performance in extreme conditions pose significant challenges. This study introduces a novel approach to enhancing both the thermal resilience and structural strength of concrete by incorporating ground granulated blast furnace slag (GGBS) and ceramic powder (CP) as sustainable, supplementary materials. Unlike previous studies that focus on these materials individually, this research investigates their combined effects, providing new insights into their synergistic interaction. Concrete mixtures with varying proportions of GGBS and CP were evaluated for thermal stability, compressive strength, and microstructural integrity using advanced techniques such as thermogravimetric analysis (TGA) and scanning electron microscopy (SEM). Results show that the optimal combination of 20% GGBS and 20% CP significantly improves compressive strength by up to 30% and reduces mass loss at elevated temperatures by 35%, demonstrating enhanced durability and sustainability. The microstructural analysis revealed denser hydration products and refined interfacial transition zones (ITZs), which contribute to improved bond strength and reduced porosity. These findings of the study highlight the potential of GGBS and CP to revolutionize sustainable concrete production, offering a viable solution to both environmental concerns and the performance demands of modern construction.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 3","pages":"1273 - 1285"},"PeriodicalIF":0.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638560","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 an intelligent hybrid global optimization (IHGO) algorithm for enhanced seismic analysis in masonry-infilled RC frames","authors":"Ahmad S. Alfraihat","doi":"10.1007/s42107-024-01237-z","DOIUrl":"10.1007/s42107-024-01237-z","url":null,"abstract":"<div><p>This study introduces the Intelligent Hybrid Global Optimization (IHGO) algorithm to improve the predictive accuracy of neural network models for estimating the fundamental period of vibration in masonry-infilled reinforced concrete (RC) frame structures. Using a dataset of 4,026 entries, which includes critical structural parameters such as the number of storeys (ranging from 2 to 15), span length (3–8 m), opening ratio (0–50%), and masonry wall stiffness (up to 10⁵ kN/m), the IHGO algorithm optimizes neural network hyperparameters. The IHGO-optimized neural network outperforms baseline models, achieving an R² value of 0.92, a Mean Absolute Error (MAE) of 0.012 s, and a Root Mean Square Error (RMSE) of 0.017 s, compared to 0.85 R², 0.018 MAE, and 0.026 RMSE for the standard neural network. The optimization balances exploration and exploitation, enhancing precision and revealing complex nonlinear relationships between structural features and seismic behavior. The study demonstrates the critical role of accurate period estimation in seismic design, supporting better assessments of structural vulnerabilities and compliance with safety standards. This work highlights the efficacy of hybrid optimization in structural engineering and suggests future research on adaptive tuning and broader seismic applications.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 3","pages":"1115 - 1127"},"PeriodicalIF":0.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638557","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":"Recycling glass waste in mortar: a sustainable approach to enhancing strength and density","authors":"Bhukya Govardhan Naik,&nbsp;Nakkeeran Ganasen,&nbsp;Dipankar Roy,&nbsp;Golla Uday kiran","doi":"10.1007/s42107-024-01248-w","DOIUrl":"10.1007/s42107-024-01248-w","url":null,"abstract":"<div><p>The handling of waste glass poses a significant environmental challenge, resulting in substantial landfill buildup due to its non-biodegradable nature. This study addresses the issue by exploring the application of recycled glass can be used in place of some of the fine aggregate in cube, focusing on enhancing both strength and density. Various quantities of glass waste (0%, 10%, 20%, 30%, 40%, 50%, and 100%) were incorporated to evaluate its impact on the mechanical and physical characteristics of mortar. The evaluation of compressive strength, water absorption, and dry density was conducted following a 28-day curing period, along with statistical analysis performed using Minitab software to identify the optimal replacement levels. ImageJ software was utilized for microstructural analysis to evaluate changes in internal structure. The results indicate a notable improvement in strength and density, resulting from the pozzolanic reaction that takes place between glass particles and the cement matrix. This study promotes eco-friendly methods in the construction industry by providing a feasible strategy for handling glass waste, which reduces environmental impacts and improves mortar performance.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 3","pages":"1243 - 1256"},"PeriodicalIF":0.0,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638456","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 integrated machine learning and genetic algorithm approach for properties prediction of fly ash-based steel fiber-reinforced concrete","authors":"Rashmi Keote,&nbsp;Minal Keote,&nbsp;Rupali S. Balpande,&nbsp;Bharati Masram,&nbsp;Pragati Dubey,&nbsp;Latika Pinjarkar,&nbsp;Manjushree Muley","doi":"10.1007/s42107-024-01244-0","DOIUrl":"10.1007/s42107-024-01244-0","url":null,"abstract":"<div><p>Enhancement of concrete strength is critically important for increasing construction materials’ lifespan and sustainability. Traditionally, concrete mixture optimization methods—especially those used for fly ash and steel fiber concretes—normally fail to accurately predict the strength due to the high degree of complexity and non-linearity involved in the interaction of their components. These limitations are overcome in this study, which uses advanced artificial intelligence techniques—The Multilayer Perceptron (MLP) Neural Networks, Gradient Boosting Machines (GBM), and Convolutional Neural Networks (CNN) to optimize concrete mixtures for improved strength. Among these, the MLP neural network was selected for this work because of its ability to model highly complex, nonlinear relationships and hence will be able to capture the intricate interactions among fly ash, steel fibers, and other additives. For this reason, Gradient Boosting Machine was chosen for its robustness against overfitting and high accuracy in handling linearity or nonlinearity in an optimization problem. Traditionally, CNN has been applied to image processing, but in this work, it had been uniquely adapted to include the spatial distribution of concrete mix components, hence giving a new dimension in strength prediction. In this study, every method was used with a comprehensive data set and the input variables were taken as the percentages of fly ash and steel fibers, the water-cement ratio, aggregate size distribution, and curing delays. The accuracies of prediction for the proposed models were improved significantly, with the Mean Absolute Error (MAE) for compressive strength by the MLP model and an R² value of 0.90–0.95 by the GBM model. It is interpreted from CNN that there could be a potential reduction in prediction error by 10–15% compared to traditional methods. The work provides a robust framework for concrete strength optimization with substantial improvements in the reliability and performance of concrete materials used in construction.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 3","pages":"1175 - 1191"},"PeriodicalIF":0.0,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638455","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":"Comparative study of sections and joints by designing conventional steel frames and pre-engineered steel frames","authors":"Musaddiq S. Momin,&nbsp;R. D. Patil","doi":"10.1007/s42107-024-01242-2","DOIUrl":"10.1007/s42107-024-01242-2","url":null,"abstract":"<div><p>The research focuses the design and comparison of steel portal frames with their joints by using Staad Pro and Idea Statica software for industrial warehouse. Steel buildings are easy to construct and offer greater strength. After demolition, the scrap retains value, making them a more economical choice. It provides economical solution and reduces weight of building. In this research, design and comparison for steel warehouse frames by conventional and pre-engineered method is carried out. Typical frames along with end wall frames for conventional and pre-engineered method are designed in Staad Pro software. Joints are designed in Idea Statica software. Design of steel structures is done many more times, but which one is suitable according to design of sections and joints, that is analyzed. In this work, comparative study of sections and joints by designing conventional steel frames and pre-engineered steel frames is carried out by Indian codes. The economical and reduced weight for steel warehouse research is carried and results are compared. (viz. section sizes, frame weights, joints).</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 3","pages":"1165 - 1174"},"PeriodicalIF":0.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive review on performance-based design methodology","authors":"Pooja Gadekar,&nbsp;Prashant Pawade,&nbsp;Sanket Sanghai","doi":"10.1007/s42107-024-01239-x","DOIUrl":"10.1007/s42107-024-01239-x","url":null,"abstract":"<div><p>This study offers a comprehensive review of Performance-Based Plastic Design (PBPD) under various dynamic loads, including seismic, blast, fire, and impact, with a focus on how these loads affect structural behavior. While seismic design has been extensively researched, studies on PBPD for other dynamic loads are scarce. This review aims to bridge that gap by analyzing structural behavior under multiple dynamic loads using both linear and non-linear static and dynamic analytical methods, such as pushover analysis, time history analysis, and response spectrum analysis. The study also covers several analytical tools, including SAP2000, PERFORM3D, STAADPro, and LS-Dyna, which are essential for conducting such analyses. In addition to reviewing existing literature, a parametric comparison is presented to assess the effects of different dynamic loads. The study further proposes a simplified methodology, drawing from past research, to guide the design of structures that can withstand all major dynamic loads. It also examines the growing importance of blast-resistant design alongside earthquake-resistant methods, while modern innovative techniques and future research trends in PBPD are discussed. This review provides a detailed analysis of key aspects contributing to a better understanding of how PBPD can be applied to make structures resilient against multiple dynamic forces.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 3","pages":"1023 - 1042"},"PeriodicalIF":0.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638201","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 mortar into paver block using agricultural waste ash and recycled glass: ANN-based prediction of mechanical properties","authors":"Vivek Kumar Mishra,&nbsp;Anurag Sharma,&nbsp;Sumant Nivarutti Shinde,&nbsp;S. Thenmozhi,&nbsp;T. J. Rajeeth","doi":"10.1007/s42107-024-01241-3","DOIUrl":"10.1007/s42107-024-01241-3","url":null,"abstract":"<div><p>In the contemporary context, achieving a balance between construction activities and environmental protection is critically significant. As a result, there exists a significant need to investigate the potential of using waste materials as alternatives to conventional construction materials. The utilisation of recycled glass, rice husk, and sugarcane bagasse ash in concrete production presents a significant area of exploration. This study examines an important void in existing literature regarding the longevity and efficiency of sugarcane bagasse ash (SCBA) and waste glass (WG) as alternatives to cement and fine aggregates. The aim is to evaluate the mechanical characteristics and sustainability of paver blocks that include SCBA and WG, employing experimental testing alongside machine learning methodologies. A two-stage mixing procedure was used to evaluate water absorption, dry density, compressive strength, ultrasonic pulse velocity, and rebound hammer results. An artificial neural network (ANN) model was applied to predict these properties based on experimental datasets. The results showed that increasing SCBA and WG content elevated water absorption but reduced compressive strength, density and workability. Optimal strength and sustainability were achieved with 10% SCBA and 10% WG. Both SCBA and WG demonstrate potential as sustainable alternatives, with ANN providing accurate property predictions.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 3","pages":"1153 - 1164"},"PeriodicalIF":0.0,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638600","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}