Asian Journal of Civil Engineering最新文献

This study addresses a crucial research gap by investigating the optimal position of shear walls, the ideal shear wall-floor area ratio in building design, and their correlation with building height using non-linear analysis (Static and Dynamic). The results, including capacity curves, inter-story drift, and performance levels from both nonlinear static analysis and nonlinear dynamic analysis, are explored. Adopting principles of performance-based seismic design, the study reflects a comprehensive approach to seismic analysis and mitigation. The findings underscore that elevating the shear wall ratio not only enhances structural rigidity but also improves reliability in terms of inter-story drift, playing a crucial role in achieving the desired performance level during the design process. For a 7-story structure, a 1.00% shear wall–floor ratio is crucial, while a 1.5% ratio is essential for a 14-story structure to meet design conditions. The study highlights the intricate interplay among shear wall–floor ratios, optimal shear wall positions, and their correlation with building height as pivotal factors or main criteria influencing performance and structural integrity. Additionally, the presence of shear walls adopting compound forms (Box, U, and L) enhances reliability, while incomplete shear walls within the frame degrade half-filled frame stiffness, impacting short beam integrity. Furthermore, the study confirms the reliability of both nonlinear dynamic analysis and nonlinear static analysis, providing valuable insights into optimizing building designs for enhanced structural performance.

Walls with openings, such as doors or windows, are a common feature in building construction. These openings, regardless of their size, are strategically positioned on each floor to fulfill ventilation or other functional needs. This study primarily aimed to investigate the structural performance of 3D-printed walls with door openings under uniaxial loads. The research focused on three types of walls with an opening: unreinforced, reversed U-bar-reinforced, and reversed U-bar with rebar-reinforced walls. All walls were measured 2000 mm in width, 1310 mm in height, and 120 mm in thickness, with an opening size of 1200 mm in width and 1000 mm in height. The study examined the load-vertical deflection behavior and cracking behavior of the tested walls. It was found that reinforcing the walls improved their stiffness and cracking behavior compared to the unreinforced wall. Moreover, it was observed that vertical cracks, along with small stepped diagonal cracks induced by horizontal stress, were prevalent in the tested walls. Both the reversed U-bar with rebar-reinforced and unreinforced 3D-printed walls with an opening exhibited brittle failure, characterized by significant spalling of the 3D-printed mortar layer surfaces on the column part near the opening edge corner. For the wall with only the reversed U-bar-reinforced, the test was stopped due to safety concerns before failure occurred. The reversed U-bar with the rebar-reinforced wall exhibited a lower ultimate load at failure than the unreinforced wall. This reduction in ultimate load is attributed to higher stress concentrations around the grouted regions within the reinforced wall which causes the earlier failures. Additionally, the failure of the reversed U-bar with the rebar-reinforced wall was observed at the location where the grouted core was incompletely filled.

Despite having several qualities, the high thermal conductivity of concrete is considered as its shortcoming in tropical and subtropical countries, where temperature may reach a record high of up to 50 °C. This study deals with the experimental and numerical investigations to improve the heat insulation properties of hardened concrete hollow blocks by selecting a suitable insulation material at the ambient temperature range of 35 to 50° C. A total of ninety-six blocks were cast and tested. The dimensions of the outer moulds were 12” × 12” × 6” whereas the dimensions of the inner steel moulds (hollow section) were varied and categorised into three different batches. Each block was stuffed with the loose form of mineral wool which served as an insulating material. After preparation, the blocks were placed in the open air under direct exposure to sunlight. The difference in the temperature on the top and bottom surfaces of the blocks was recorded through several readings with regular intervals of time and compared to measure the amount of heat insulated by the mineral wool. Findings showed that with the temperature rise, insulated large hollow blocks stiffed and resisted more heat than medium and small insulated hollow blocks. It was also found that the control specimen (blocks with no insulation material) insulated less heat than when filled with mineral wool. The heat transfer coefficient for all categories of tested specimens was also calculated theoretically by making variations in the hollow space filled with mineral wool. The maximum temperature difference was more than 20 °C when the ambient temperature was 52 °C. A two-dimensional finite element (FE) model was developed and validated against the experimental results. The FE model showed close agreement with experimental results.

The quest to enhance the strength of concrete, while at the same time reducing the environmental impacts occasioned by its use, has become quite imperative in sustainable construction. Traditional approaches toward supplementary cementitious materials optimization have often fallen short in revealing synergistic interactions that maximize mechanical properties. The current research overcomes these limitations by considering combined effects of different SCMs on concrete strength levels, using advanced artificial intelligence techniques. Current methods often make assumptions with respect to linearity of the models or simple interaction effects that insufficiently represent the multi-level, nonlinear relationships between SCMs and concrete properties. Moreover, integration of microstructural analysis into predictive models is poorly explored. In this paper, a hybrid GBM-CNN methodology is proposed to model complicated interactions within SCM compositions. GBMs are competent in dealing with numerical features, such as SCM proportions, curing time, and temperature, which hold nonlinear relationships in tabular data samples. Meanwhile, CNNs process microstructural images to extract spatial features correlating to mechanical properties. These models will predict the concrete strengths by fusing their outputs using an ensemble method expected to have an R’2 of about 0.85 and an RMSE of about 2 MPa levels. The complexity of the data is managed by using multi-modal data analytics, wherein feature engineering techniques are integrated with Principal Component Analysis, thereby improving the quality of the data while bringing down its dimensionality to retain only the most vital information to explain 95% of data variance. Further, polynomial regression models with regularization—that includes non-linear interaction terms of SCMs, curing conditions, and engineered features—will be built, which highlights the key interaction terms statistically significant with p Value < 0.05. In the field of sustainability, LCA and multi-objective optimization—for example, NSGA-II—are applied for estimating and optimizing the environmental impact, cost, and performance with respect to the combination of SCMs. This integrated approach has managed to reduce CO₂ emissions by 20% at an increase in cost of less than 10%, while maintaining the target strength above 40 MPa levels. The overall AI-driven methodology would not only deepen the understanding of SCM interactions in concrete but would also provide a pragmatic framework for developing sustainable and cost-effective construction materials, hence making huge contributions to the area of sustainable engineering processes.

Self-Compacting Concrete (SCC) is a highly flowable concrete that can spread into place, fill formwork, and encapsulate reinforcement without mechanical consolidation. This study investigates the use of steel slag as a partial replacement for fine aggregate in SCC, with replacement levels ranging from 0 to 70%. Eight different mixes were prepared and tested for their fresh, mechanical, durability, and microstructural properties. Materials used include Ordinary Portland Cement (OPC) conforming to IS 269:2015, natural river sand, crushed granite, steel slag, potable water, and a polycarboxylate ether superplasticizer. The concrete mix design was based on IS 10262:2019 and EFNARC guidelines for SCC. Fresh properties were assessed using slump flow, T50 time, V-funnel, and L-box tests following EFNARC specifications. Mechanical properties were evaluated through compressive strength, splitting tensile strength, and flexural strength tests. Durability properties were assessed by water absorption, sulfate attack resistance, and freeze-thaw cycle tests. Microstructural properties were analyzed using Scanning Electron Microscopy (SEM), Thermogravimetric Analysis (TGA), and X-Ray Diffraction (XRD). The results indicate that a 50% replacement level of steel slag optimizes the properties of SCC, leading to enhanced flowability, higher compressive strength (up to 59.3 MPa at 28 days), and improved durability against sulfate attack and freeze-thaw cycles. The microstructural analysis confirmed a denser matrix with increased formation of calcium silicate hydrate (CSH) at this optimal replacement level. These findings suggest that incorporating steel slag into SCC not only enhances its performance but also contributes to sustainable construction by reducing the need for natural aggregates and utilizing industrial byproducts.

Effective classification and detection of equipment on construction sites is critical for efficient equipment management. Despite substantial research efforts in this field, most previous studies have focused on classifying a limited number of equipment categories. Furthermore, there is a scarcity of research dedicated to heavy construction equipment. Hence, this study develops a robust Convolutional Neural Network (CNN) model to classify heavy construction machinery into 12 different types. The study utilizes a comprehensive dataset of equipment images, which was divided into three distinct subsets: 60% for training the model, 30% for validating its performance, and 10% for testing its accuracy. The model’s robustness was ensured by monitoring accuracy and loss measures during the training and validation phases. The CNN model achieved approximately 85% training accuracy with a minimum loss of 0.40. The testing phase revealed a high overall precision of 80%. The CNN model accurately classifies concrete mixer machines and telescopic handlers with an Area Under the Curve (AUC) of 0.92, however pile driving machines have a lower accuracy with an AUC of 0.83. These findings demonstrate the model’s high ability to distinguish between several types of heavy construction equipment. This paper contributes to the relatively unexplored area of classifying heavy construction equipment by providing a practical tool for automating equipment classification, leading to enhanced efficiency, safety, and maintenance protocols in construction management.

This study explores the prediction of shear strength in fly ash-stabilized clayey soil using Artificial Neural Network (ANN) and Support Vector Regression (SVR). Clayey soils, characterized by low shear strength and high plasticity, present significant challenges in construction, necessitating effective stabilization methods. Fly ash, a byproduct of coal combustion, provides a sustainable alternative due to its pozzolanic properties. The research integrates ANN and SVR to model complex relationships between soil properties (grain size distribution, plasticity index, liquid limit, plastic limit, moisture content), fly ash content, and curing periods. Laboratory experiments and triaxial shear tests generated the dataset for training and testing the models. The ANN model achieved a training R² of 0.93 and a Mean Squared Error (MSE) of 0.00, while the testing R² was 0.69 with an MSE of 0.01. In contrast, the SVR model outperformed ANN with a training R² of 0.95 and MSE of 0.01, and a testing R² of 0.83 and MSE of 0.00. Sensitivity analysis identified key factors influencing shear strength predictions, with SVR demonstrating superior generalization capabilities. The study concludes that SVR is a more reliable tool for predicting shear strength in stabilized soils, contributing to sustainable construction practices.

Urban walkability is essential for sustainable city planning and construction, fostering public health, environmental benefits, and social equity. However, optimizing walkability involves balancing multiple, often conflicting objectives, such as accessibility, safety, environmental quality, and social inclusivity. This paper presents a novel approach to optimizing urban walkability using the Non-dominated Sorting Genetic Algorithm III (NSGA-III). By applying NSGA-III, we address the complexities of multi-objective optimization in urban environments, generating a set of Pareto-optimal solutions that cater to diverse planning priorities. A case study in a mid-sized urban area demonstrates the effectiveness of the proposed methodology. The results highlight key trade-offs between objectives, such as the balance between accessibility and safety or environmental quality and social inclusivity. The findings provide urban planners with a robust decision-making framework that supports the creation of walkable, sustainable cities. The study concludes with policy recommendations to enhance urban walkability and suggests avenues for future research, including the integration of economic considerations and the application of this approach in larger, more complex urban settings. This research contributes to the field of urban planning by offering a comprehensive tool for optimizing walkability, ultimately promoting more livable and sustainable cities.

This study investigates the structural performance of multi-story reinforced concrete buildings on sloped terrains, with a focus on comparing standard normal column, normal columns with shear wall and V-shaped column configurations. The various parameters analysed include story shear, maximum displacement, story drift, stiffness variation, and time period, all of which are crucial for understanding the dynamic behaviour of structures under various conditions. The results indicate that V-shaped columns significantly enhance structural stability, particularly in reducing maximum displacement and story drift, and in improving load distribution, as compared to standard columns. In a 10-story building with a 10-degree incline, V-shaped columns exhibited a maximum displacement of 13.582 mm, lower than the 22.697 mm observed in standard columns. The analysis also reveals that V-shaped columns maintain consistent performance across different incline angles and story heights, demonstrating their efficiency in controlling lateral movement and managing shear forces, especially in taller structures. The study also shows that time periods are generally shorter for models with V-shaped columns, indicating better dynamic performance. The findings suggest that V-shaped columns are preferable for the design of multi-story buildings on sloped terrains, offering superior stability, load management, and overall structural efficiency.

To address the ever-increasing environmental degradation caused by concrete construction, utilizing recycled aggregate (RA) in concrete mixes offers a significant solution. This study aims to assess the bond strength of both plain and deformed steel rebars in recycled aggregate concrete (RAC) using machine learning (ML) methods. The ML models employed include Decision Tree (DT), AdaBoost, CatBoost, Gradient Boosting, and Extreme Gradient Boosting (XGB). A comprehensive dataset of 158 pull-out tests from previous studies was collected. The features investigated associated with both concrete and rebar characteristics, namely recycled and natural coarse aggregates (RCA and NCA), fine aggregates, cement, water, the water-to-cement ratio (w/c), concrete compressive strength (({f}_{c}{prime})), yield strength of steel rebar (({f}_{y})), rebar type and diameter, and bond length. The findings highlighted that, before hyperparameter tuning, the CatBoost regressor, outperformed the other ML models with ({R}^{2}) score and RMSE value of 0.94, and 3, respectively. However, after hyperparameter tuning, the XGBoost regressor was the most accurate model, achieving an impressive ({R}^{2}) score of 0.94, and an RMSE value of 3. Furthermore, according to the Shapley values applied to the XGB model, the features ({f}_{c}{prime}), ({f}_{y}), and bond length were found to have the highest impact on the bond strength of the studied specimens. Whereas, the RAC replacement level has minimal impact on the target value.