Asian Journal of Civil Engineering最新文献

{"title":"Studies on soil stabilized hollow blocks using c & d waste","authors":"Umer Nazir Ganie, Parwati Thagunna, Preetpal singh","doi":"10.1007/s42107-024-01158-x","DOIUrl":"10.1007/s42107-024-01158-x","url":null,"abstract":"<div><p>The production of conventional building materials frequently results in resource depletion, environmental problems, and health problems, due to Production of building materials using fossil fuels which causes global environmental problem like global warming. With the potential to have a considerable impact on both society and the environment, the building and construction sector is a key participant in sustainable development. Stabilized mud blocks show to be an energy efficient, affordable, and ecologically friendly building material with the growing concern of awareness regarding sustainable building materials and environmental issue. Currently, stabilized mud block technology is being used in India to build more than 25,000 houses. The usage of stabilized soil-based construction materials, such soil stabilized Hollow blocks, can have several benefits over conventional building materials, including increased strength and durability, less negative environmental effects, and reduced costs. When old buildings are demolished, solid trash is usually categorized as either industrial waste or construction and demolition (C&D) waste. Massive volumes of waste are generated in India alone, and virtually little of it is recycled. This C&D waste can be used instead of soil or quarry sand to adjust the qualities of stabilized soil. This study investigates the utilization of combined C&D waste and a stabilizing agent in soil sampling. The studies involve soil stabilized Hollow blocks using combined C&D waste to check the strength of the hollow blocks for different replacements and its water absorption. The materials required for the research were procured from locally available demolished buildings. Cylindrical samples were cast for various compositions using mortar to test 30–34 different ratios of mixed building and demolition waste with 9% cement content. Compressive strength and water absorption tests were performed on the stabilized samples to evaluate their suitability for use in construction. The C&D waste was substituted for soil in ratios ranging from 0 to 100% based on the least compressive values discovered in cylindrical samples. Soil-stabilized hollow blocks were poured and their mechanical properties, strength, and longevity assessed. In this study, an attempt was made to construct cylindrical samples that might be utilized to create stabilized hollow blocks and concrete using different proportions of C&D waste, or brick and concrete waste. Various ratios of brick waste, and concrete waste were employed for 23 mix proportions to make cylindrical samples. Cement concentrations of 9 and 12% were used to create cylindrical samples. The mechanical and physical properties of these samples were examined, including their compressive strength, capacity to absorb water, and initial rate of absorption. The greatest compressive strength for 9% cement, CD-2, was 4.09 MPa, and the maximum compressive strength for 12% cement, ","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"25 8","pages":"5989 - 6005"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587757","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 trade-off between time, cost, and carbon emissions in construction using NSGA-III: an integrated approach for sustainable development","authors":"Amir Prasad Behera,&nbsp;Mayank Chauhan,&nbsp;Gaurav Shrivastava,&nbsp;Prachi Singh,&nbsp;Jyoti Shukla,&nbsp;Krushna Chandra Sethi","doi":"10.1007/s42107-024-01176-9","DOIUrl":"10.1007/s42107-024-01176-9","url":null,"abstract":"<div><p>The construction industry faces the critical challenge of balancing project time, cost, and carbon emissions to achieve sustainable development. This study introduces a Time–Cost–Carbon Emission Trade-Off (TCCET) model, optimized using the Non-dominated Sorting Genetic Algorithm III (NSGA-III), to address these conflicting objectives. The TCCET model evaluates various execution modes for construction activities, such as groundwork, excavation, footing, formwork, and finishing, taking into account their respective impacts on time, budget, and carbon emissions. By applying NSGA-III, the model generates a set of Pareto-optimal solutions, offering decision-makers diverse trade-offs among these objectives. A practical case study demonstrates the model’s effectiveness in real-world scenarios, yielding flexible and efficient solutions that support informed decision-making in construction management. Comparative analysis with existing optimization models and sensitivity analysis highlight the superior performance of NSGA-III in addressing time, cost, and environmental impact simultaneously. This study’s findings emphasize the potential of NSGA-III to guide sustainable construction practices, significantly reducing environmental footprints without compromising project timelines or costs. The developed framework aligns with global sustainable development goals, providing valuable insights for the construction industry’s transition to sustainable practices.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 1","pages":"73 - 87"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906091","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 ventilation system retrofitting: balancing time, cost, and indoor air quality with NSGA-III","authors":"Apurva Sharma,&nbsp;Anupama Sharma","doi":"10.1007/s42107-024-01143-4","DOIUrl":"10.1007/s42107-024-01143-4","url":null,"abstract":"<div><p>Improving ventilation systems is essential for better indoor air quality, energy efficiency, and overall building performance. This study introduces a new optimization model to tackle the trade-offs between time, cost, and indoor air quality (IAQ) in ventilation system retrofitting projects. Using the Non-dominated Sorting Genetic Algorithm III (NSGA-III), the model evaluates various retrofitting options, including upgrades for ventilation capacity, energy efficiency, air quality, noise reduction, and aesthetic improvements. Each option is assessed for its impact on project duration, cost, and indoor air quality. The goal is to find the best combinations of these options that minimize both project time and cost while improving indoor air quality and meeting resource constraints. The NSGA-III algorithm generates a set of optimal solutions, providing a range of choices for balancing these factors. A comparison with existing methods shows that this new approach offers better solutions for managing these trade-offs. By selecting the most effective solution from these options using a weighted sum method, the study demonstrates NSGA-III’s power in handling complex optimization problems. This model supports better decision-making in retrofitting projects, advancing both sustainability and indoor environment quality.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"25 8","pages":"5753 - 5764"},"PeriodicalIF":0.0,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587756","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":"Sustainability assessment of sheet pile materials: concrete vs steel in retaining wall construction","authors":"Oki Setyandito,&nbsp; Farell,&nbsp;Anggita Prisilia Soelistyo,&nbsp;Riza Suwondo","doi":"10.1007/s42107-024-01161-2","DOIUrl":"10.1007/s42107-024-01161-2","url":null,"abstract":"<div><p>The construction industry plays a pivotal role in global carbon emissions, prompting a critical need for sustainable infrastructure-development practices. Retaining walls, which are essential for stabilising earth and water pressure in civil engineering projects, represent a significant opportunity to mitigate environmental impacts through material optimisation. This study investigated the design efficiency and embodied carbon and cost implications of cantilever retaining walls constructed with concrete and steel sheet piles. This study employs a thorough methodology that incorporates quantitative studies of the cost and embodied carbon at varying retaining wall heights. The environmental effects and financial viability of the concrete and steel sheet piles were assessed using standardised procedures and local market data. The results indicate that in every height category, concrete sheet piles show consistently reduced total costs and embodied carbon when compared to their steel equivalents. Superior environmental sustainability is demonstrated by concrete, where the embodied carbon levels gradually increase as the wall height increases. On the other hand, steel provides better load-bearing capability, but at a higher cost to the environment and economy, which is especially noticeable in taller structures. This study offers significant perspectives for engineers and other relevant parties to enhance design results that harmonise ecological responsibility with cost-effectiveness in building methods.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"25 8","pages":"6037 - 6045"},"PeriodicalIF":0.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587822","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":"Innovative approaches to concrete health monitoring: wavelet transform and artificial intelligence models","authors":"Soumyadip Das,&nbsp;Aloke Kumar Datta,&nbsp;Pijush Topdar,&nbsp;Apurba Pal","doi":"10.1007/s42107-024-01178-7","DOIUrl":"10.1007/s42107-024-01178-7","url":null,"abstract":"<div><p>The health monitoring of concrete structures is of principal concern to avoid major accidents. Presently, many large-scale structures have been constructed throughout the world and in India. Therefore, there is an urgent need for sensor-aided research to keep all these large infrastructural facilities for the long life in an uninterrupted manner. As per the available literature, the Acoustic Emission (AE) sensor data and its deployment for the development of an artificial intelligence (AI) model is most suitable for health monitoring of these types of structures. Researchers have used the signal processing method. However, the AI models have significantly reduced the effort as well as errors in the computation process. In this study, an experimental investigation is done using the AE system for data generation. A good number of concrete slabs of different grades were cast and used for generating data deploying the Pencil Lead Break (PLB) approach. The generated data was utilized for finding the damage location using the WT method and AI models. The developed AI model is more effective in the health monitoring of concrete structures as the error in calculation is less as compared to the WT method. The model is also validated by identifying the damage source (simulated) in the concrete slab. This approach can be utilized for real-time health monitoring of large-scale concrete structures comprised of slab-like components without any interruption. Results show promising trends for further research for making the health monitoring process in wider application of civil engineering structures.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 1","pages":"107 - 120"},"PeriodicalIF":0.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906079","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":"Predictive modeling for concrete properties under variable curing conditions using advanced machine learning approaches","authors":"Nischal P. Mungle,&nbsp;Dnyaneshwar M. Mate,&nbsp;Sham H. Mankar,&nbsp;Vithoba T. Tale,&nbsp;Ankita Mehta,&nbsp;Shrikrishna A. Dhale,&nbsp;Vikrant S. Vairagade","doi":"10.1007/s42107-024-01174-x","DOIUrl":"10.1007/s42107-024-01174-x","url":null,"abstract":"<div><p>Such is the need for this work, since accurate concrete strength prediction at different curing conditions is critical to having structures that are both strong and long-lasting. Traditional methods for the prediction of concrete strength often lack the complexity that occurs in the interaction of the different environmental factors involved, hence leading to suboptimal practices in curing with potential structural weaknesses. Current research into this area has typically focused on disparate data sources and rather naïve modeling methods, further limiting predictive accuracy and creating a general lack of comprehensive knowledge of curing dynamics. Such limitations bring out the need for a more integrated, sophisticated predictive modeling approach to explain variability in concrete strength levels. This paper proposes a novel predictive modeling framework that will be powered by advanced machine learning techniques to take up these challenges. It will adopt a multimodal data integration approach driven by a combination of sensor data related to temperature, humidity, and strain gauges; environmental data related to weather conditions and atmospheric pressure; and historical records, such as mix design and curing duration, further leveraging techniques from data fusion, including the Kalman filter and Bayesian networks. This will be further integrated into a unified, enriched dataset, encapsulating the complex interaction of factors influencing concrete strength. In the present work, this is a chosen approach: hybrid modeling with ensemble learning using XGBoost for the prediction of static features, and Long Short-Term Memory (LSTM) networks for capturing temporal dependencies. In this case, a combination of these models via weighted averaging or stacking improves the accuracy of the predictions to a very great extent: the R² increased from 0.85 to 0.92, and MAE levels by 10–15%. In addition to that, AutoML with Feature Tools implements advanced feature engineering through the generation and selection of optimal features on transformation and aggregation primitives, further refining model performance and interpretability. This process at times reduces the Root Mean Squared Error levels by 5–10%. Finally, Bayesian Neural Networks together with Sobol sensitivity analysis can be used for handling uncertainty and uncovering key factors. BNN provides probabilistic predictions, therefore 95% confidence intervals, while Sobol analysis identifies those critical features that contribute more to variability and allows an in-depth understanding of the role each factor has in driving concrete strength. Indeed, the framework propounded in this work has made great strides in predictive abilities concerning concrete strength variability and will permit more efficient curing practices, thereby making construction outcomes more secure and long-lasting.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"25 8","pages":"6249 - 6265"},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587909","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":"Anticipation of shear strength of recycled aggregate reinforced concrete beams: a novel hybrid RF-TGC model and realistic implementation","authors":"Duy-Liem Nguyen,&nbsp;Tan-Duy Phan","doi":"10.1007/s42107-024-01162-1","DOIUrl":"10.1007/s42107-024-01162-1","url":null,"abstract":"<div><p>This study proposes the hybrid machine learning model combining random forest and Taguchi optimization (RF-TGC) to predict the shear strength of recycled reinforced concrete beams (RARC). For this objective, a total of 128 experimental results of shear strength of RARC beams from published papers were used to develop the proposed RF-TGC model. The performance of the hybrid RF-TGC model was compared with the pure RF model, the k-nearest neighbour (k-NN) model, and the multiple linear regression (MLR) model based on the four indicators of the error metric: mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R²). As a result, the hybrid RF-TGC model showed the best accuracy in predicting the shear strength of the RARC beam compared to the pure RF, k-NN and MLR models with an R² value of over 0.9 in training and a value of 0.89 in testing. In addition, the sensitivity analyses of the input parameters for the shear strength of the RARC beam were also investigated. It was found that the percentage of transverse steels is the most important parameter for predicting the shear strength of RARC beams. Finally, a free web application was developed to quickly predict the shear strength of the RARC beam in practical implementation.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"25 8","pages":"6047 - 6072"},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587770","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 seismic resilience: a machine learning approach for vertical irregular buildings","authors":"Ahmed Hamed El-Sayed SALAMA","doi":"10.1007/s42107-024-01173-y","DOIUrl":"10.1007/s42107-024-01173-y","url":null,"abstract":"<div><p>The paper is a landmark in earthquake and structural engineering, with modern machine-learning techniques applied to introduce innovative investigations into forecasting seismic behavior for vertically uneven structures using sophisticated machine-learning methodologies. The research constructs a very accurate model for making predictions using the XGBoost algorithm with the Owl Search algorithm (OSA) for hyperparameter tuning, which explicitly considers complex behavior in the structures under seismic stresses. The variety within the dataset is broad and covers all kinds of irregularities in the structures, such as stiffness and mass irregularities; thus, it has been used to accurately represent the complex characteristics of actual buildings. The results indicate a strong dependence of base shear capacity and seismic performance on the irregularity of stiffness and mass. The test accuracy of the optimized XGBoost model was 98.8%. The result was better than that of conventional models, thus proving the effectiveness of integrating the Owl Search Algorithm in further fine-tuning the parameters. These results give new variables as insight into affecting earthquake resilience and represent practical applications that enhance building design and retrofitting processes. This is further underlined by the proposal of future research directions that would extend the model’s applicability to other structural anomalies and include additional machine-learning methodologies. Through AI-driven approaches, this study captured complicated structural dynamics with the utmost precision, thus opening new insights that could be brought into practice to improve building design and retrofitting strategies in a way that would diminish the impact of seismic events.</p><h3>Graphical abstract</h3><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":"6233 - 6248"},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587745","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":"Effect of human-induced dynamic loading and its mitigation on pedestrian steel truss bridges","authors":"Yati R. Tank,&nbsp;G. R. Vesmawala","doi":"10.1007/s42107-024-01165-y","DOIUrl":"10.1007/s42107-024-01165-y","url":null,"abstract":"<div><p>Vibration challenges in lightweight pedestrian structures, such as footbridges, have been extensively studied, particularly following the notable lateral vibrations observed during the opening of the London Millennium Bridge on June 10, 2000. This incident underscores the critical need for a deeper understanding of the dynamic behavior of pedestrian bridges subjected to human-induced loads. This study focuses on the dynamic responses of pedestrian steel truss bridges under various loading conditions, including walking, jogging, and crowd-induced vibrations. Finite element analysis is used to identify critical parameters such as the fundamental frequency, acceleration, and damping and evaluate these parameters against the comfort criteria specified in BS EN 1991-2: 2003. Initial findings revealed that acceleration values exceeded the acceptable limits, prompting structural modifications to enhance mass, stiffness, and damping properties. Additionally, incorporating tuned mass dampers as a mitigation strategy demonstrated significant efficacy, achieving up to a 90% reduction in deck acceleration. The results provide valuable insights into optimising pedestrian bridge designs to improve both structural performance and user comfort, contributing to safer and more resilient infrastructures.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"25 8","pages":"6105 - 6117"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587889","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 mechanical and physical properties of spent bleaching earth based fired bricks: an experimental study using RSM and ANN","authors":"M. A. Bouzidi,&nbsp;N. Bouzidi,&nbsp;D. Eliche Quesada","doi":"10.1007/s42107-024-01148-z","DOIUrl":"10.1007/s42107-024-01148-z","url":null,"abstract":"<div><p>In this study, Artificial Neural Networks (ANN) and Response Surface Methodology (RSM) are used to develop models to predict compressive strength, thermal conductivity, porosity and water absorption of eco-friendly fired clay bricks containing different amounts of water, percentages of spent bleaching earth (SBE) and, firing temperatures. Water content was varied between 5 and 8 wt.%, SBE was varied in the range of 0 to 50 wt.% and, firing temperature ranges from 800 to 950 °C. The fired bricks properties were strongly influenced by the SBE content and fairing temperature as confirmed by the SEM images. The percentages of water strongly influenced the compressive strength but had less influence on the porosity and water absorption and no influence on the thermal conductivity. The statistical values for both RSM and ANN models: (coefficient of determination (R²), adjusted coefficient of determination (R² _adj), mean square error (MSE), root mean square error (RMSE) and relative percent deviation (RDP)), were used to compare the two models. The results reveal high correlation coefficients, adjusted coefficients and low root mean square errors. The models were found robust and accurate in their predictions. Based on these results, the RSM and ANN models can be applied as an effective tool to predict compressive strength, thermal conductivity, porosity, and water absorption of fired bricks. Nevertheless, the artificial neural network model showed better accuracy.</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":"5811 - 5833"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587888","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}