Asian Journal of Civil Engineering最新文献

In this study, two formulations have been suggested for the calculation of the shear capacity of stiffened steel plate shear wall (SSPSW) containing two rectangular openings by integrating verified finite element results, machine learning (ML) models, and gene expression programming. In this regard, a comprehensive nonlinear finite element analysis was conducted, which included 200 records with various values. Considered variables are the thickness and aspect ratio of the steel infill plate, yield strength of the infill plate and boundary frame as well as the ratio of opening area to the total area of the infill plate. Three machine learning (ML) models were employed namely Stochastic Gradient Descent (SGD), Decision Tree (DT), and Random Forest (RF). These models were evaluated on the test data, resulting in (:{R}^{2})scores of 0.96, 0.90, and 0.95, respectively. Among these models, SGD demonstrated superior performance and was identified as the best model for this dataset. Based on the SGD model, an equation was derived to predict the shear capacity of the shear wall. Furthermore, using gene expression programming (GEP) model, an accurate formulation was proposed to calculate the shear capacity of SSPSW system, which led to (R^{2}) of 0.98 on the same test data used in the ML models. In addition, by employing the SHapley values technique, the contribution of each characteristic to the final prediction values was explained. This technique showed that the prediction values were significantly influenced by the feature (L/h), while the mechanical characteristics of steel plate and boundary frame had the least impact. Overall, the study underscored the efficacy of the SGD model in predicting the shear capacity of the studied shear walls and provided insights into the relative importance of different features in the prediction process.    

This study investigates the impact of shear fuse link length and position on the seismic response of multi-story steel frames. Pushover evaluation and nonlinear time history analysis were applied to steel frames with varied link lengths (0.5 m, 0.75 m, 1 m) and locations (all beams, side beams, and middle beams) to predict earthquake effects. The numerical study assessed the seismic performance of 4, 8, and 12-story steel moment-resistant frames. In the investigation, 30 models will be tested using three earthquakes: Halabjah, EL-Centro, and Northridge. The data show that increasing shear fuse link length improves lateral displacements and drifts for all building heights (four, eight, and twelve stories), especially during high-intensity earthquakes. In contrast, the impact on base shear ability is exceedingly complex, most likely varying with building height and earthquake characteristics. In some circumstances, shorter links benefit low-rise frames, while longer links may favor taller frames. Shear fuse-links can significantly improve the seismic performance of steel frames by distributing energy and introducing protective structural elements. Shorter connections may be desirable for displacement management in low-story (4-story) constructions, whereas more extended linkages are likely required to maintain base shear capacity in taller (8-story) frames. Positioning linkages in all beams maximizes energy dissipation while achieving optimal displacement and drift. Pushover testing demonstrates the effectiveness of shear fuse linkages in increasing energy dissipation and safeguarding various structural parts.

The study proposes assessing the seismic vulnerability of G + 15-storey reinforced concrete (RC) buildings using finite element analysis (FEA) software (ETABS) and artificial neural networks (ANNs). The study utilizes finite element models of G + 15 buildings subjected to recent earthquake data, analysing them for seismic vulnerability and incorporating various retrofitting techniques, such as fluid viscous dampers (FVD). The damper locations are varied in the structure for the entire earthquake data considered to study the seismic vulnerability in storey displacements, storey shear, and storey drift. Key structural characteristics were systematically modified, and their impact on seismic response was evaluated through modal dynamic and non-linear time history analyses. The FEA results are used to train an ANN algorithm, creating a function that can predict the seismic behaviour of similar RC structures. This approach offers a fast and potentially generalizable method for seismic vulnerability assessment.

Ultra-high-performance concrete (UHPC) incorporating waste cementitious materials has become widely used due to its extraordinary mechanical strength and durability. Adding such waste also addresses the environmental sustainability aspect of the materials, making them a potential alternative. This study explores using Random Forest (RF) and XGBoost (XGB) as the primary model. Further, metaheuristic algorithms like the Pelican optimization algorithm (POA) and Walrus optimization algorithm (WOA) should be used to tune the hyperparameters of the primary model. This study shows that the XGB-POA is highly accurate, exceeding R² of 0.96 in the testing set. Additionally, ten-fold cross-validation ensures the model’s robustness by mitigating the overfitting issues. Similarly, other employed models, like XGB-WOA, RF-POA, and RF-WOA, also exhibited better training and testing set results. Moreover, this study is subjected to Shapley’s Additive Explanation (SHAP) analysis to explore the model’s explainable behaviour. The study reveals that the XGB-POA is the best-performing model, identifying age, fiber content, cement, and SF dosage as the most influential features in the development of UHPC. Experimental data sets that showcase more than 95% accuracy are used to validate the model performance. These insights help to understand the relationships of features involved with comprehensive assessments of UHPC for adopting sustainable practices.

The need to promote sustainability and reduce environmental effects has fueled a substantial increase in the use of recycled and waste materials in construction applications in recent years. Concerns about harmful environmental threats are heightened when output increases and waste generation rises. An economically viable solution to this problem could be found by using waste materials to create new products, which would reduce the burden on landfills. This analysis clarifies the wide range of waste products that can be used in buildings, including recycled concrete aggregates, fly ash, slag, waste glass, plastics, rubber, construction and demolition (C&D) wastes, and reclaimed wood. These materials can partially or completely replace natural resources like sand, cement, and gravel. As a result, there will be advantages like improved resource efficiency, decreased greenhouse gas emissions, decreased solid waste, and reduced air and water pollution. These advantages will ultimately translate to energy and cost savings. The consistency and quality of recycled and waste materials for use in construction remain a top priority. Additionally, the circular economy concept, which is closely related to the use of waste or recycled resources in construction, emphasizes the significance of waste reduction and material reuse as opposed to adhering to the linear "take, make, and dispose" model. By switching out traditional materials for recycled or discarded ones, the construction industry may take advantage of the circular economy by reducing the need for new resources and cutting down on waste production. Using recycled or waste materials in buildings is an effective way to create a more sustainable and circular economy.

Laboratory experiments for estimating concrete properties can be costly and time-consuming. Alternatively, predictive models based on artificial intelligence (AI) methodologies offer a viable solution. This paper presents predictive modeling employing artificial neural networks (ANNs) and support vector regression (SVR) to forecast two critical properties, slump, and compressive strength, of concrete incorporating plastic waste as fine aggregate, with a focus on PET material. Over 100 data points from literature were carefully selected to train these models, considering ten input variables including the percentage of PET content (PET_%), water-cement ratio(w/c), minimum size of PET (P_min), maximum size of PET (P_max), minimum size of sand (S_min), maximum size of sand (S_max), minimum size of gravel (G_min), maximum size of gravel (G_max), cement (C) and superplasticizer (PS). The results indicated that SVR outperforms ANN in accuracy for predicting these properties. Additionally, the study acknowledges limitations and points to avenues for further research to enhance predictive modeling’s applicability in sustainable concrete design.

Graphical abstract

Tuned mass dampers (TMDs) and multiple tuned mass dampers (MTMDs) are among the simplest, most reliable, and most frequently employed structural control devices. The efficiency of TMDs and MTMDs depends on their parameters, including the mass, frequency ratio, and damping ratio. Several analytical and metaheuristic methods have been proposed for the optimal design of tuned mass dampers. The aims of this study are: (1) to investigate the differences in efficiency between a number of TMD design methods and (2) to evaluate the differences in efficiency between a single TMD and double TMD (DTMD) in reducing the seismic response of structures by considering soil-structure interactions (SSI). In the first numerical study, the effectiveness of TMDs optimized using seven analytical methods, and TMD and DTMD optimized using a metaheuristics algorithm called Mouth Brooding Fish (MBF) in reducing the response of a fifteen-story structure under 22 far-field and 14 near-field earthquakes is compared. In the second example, in addition to the seven analytical-based designed TMDs, and MBF-based TMDs and DTMDs, the Jaya algorithm based, and plasma generation optimization-based designed TMDs are used, and the same procedure is applied to a forty-story structure. The results show that there is no important difference among these methods, which may indicate that the uppermost optimization level for a regular TMD has been reached. Furthermore, the results indicate that the TMD and DTMD are almost equally effective at reducing the seismic response of structures when the SSI effect is taken into consideration.

Geopolymer concrete, made with by-products from industrial waste, is a promising construction material that reduces carbon dioxide emissions and eliminates the need for natural resources used in traditional Portland cement. Despite numerous studies conducted over the years to investigate different characteristics of geopolymer concrete, there remains a lack of understanding on how various factors affect its properties. In this investigation, we explore the setting time, workability and compressive strength of ambient-cured geopolymer concrete, using GGBS and class F fly ash as geopolymer binder. We consider the quantity of sodium hydroxide (SH), fly ash to GGBS ratio and binder content to alkaline solution ratio (AS/B) as influencing factors. Based on experiments with 45 mixes of geopolymer concrete, we found that increasing SH concentration and GGBS content, as well as reducing AS/B ratio, decreased workability by about 60% and shortened setting time by 63–71%. However, a reduction in AS/B ratio and increased replacement of GGBS led to improved compressive strength. Compared to mixes with various SH concentrations, a slight decrease in strength was observed at higher SH concentrations (10 M and 12 M). These findings will be useful to produce geopolymer concrete components with greater strength.

The accumulation of material waste on construction sites often stems from underutilized procured materials, prompting a plethora of practical and scientific endeavors to mitigate waste and enhance construction project sustainability. This research study analyzes and synthesizes existing research on material waste management in construction projects from 2000 to 2023, aiming to investigate significant research themes and propose potential future research directions. Bibliometric and scientometric analyses are employed to examine the 205 documents retrieved from the Scopus database. The findings highlight that keywords like construction waste, waste management, and construction and demolition waste are the most prominent in the research domain. China and Malaysia are the leading contributors to this research subject. The Waste Management Journal is recognized as a seminal journal on construction material waste management. The qualitative evaluation reveals several prominent research themes: waste quantification and minimization, lifecycle assessment of waste, waste disposal and diversion practices, causal factors of waste generation and management, and circular economy and sustainable construction. The paper offers valuable insights for both scholars seeking new research avenues and practitioners interested in exploring prospective business opportunities.

The design of foundations for tall structures is an interesting and challenging task. In structural design usually, the effect of foundation soil is generally overlooked. In this study, 3D analysis of the superstructure is carried out considering the self-weight, imposed load and earthquake load. The provisions of (BIS Code of practice for criteria for earthquake resistant design of structures: Part-1, general provisions and buildings, IS 1893, Bureau of Indian Standards, New Delhi, 2016a) are used to carry out the dynamic analyses. Three-dimensional analysis of the building system is used to design and analyse the superstructure in which the structural performance parameters viz., base shear, lateral load distribution, natural time period, storey drift ratio, beam-column capacity (BCC) and percentage reinforcement were evaluated for low-rise building (G + 4) and high-rise building (G + 15) located in seismic zone- II & III. The support reactions of the superstructure were exported and used to design the foundation. SAFE software was used to design the raft foundation for low-rise buildings (G + 4) whereas the piled raft was designed for high-rise buildings (G + 15). The parametric study was carried out considering different values of soil bearing capacity as 165, 200 and 250 kN/m² and the thickness of the raft was varied from 0.3 to 1 m in the case of low-rise building. For high-rise buildings, the diameter of the pile was kept constant and the length of the pile was varied to arrive at a suitable dimension. Parameters like settlement, soil pressure and punching shear failure were compared and discussed. It was found that the column size of G + 15 building was increased when compared to G + 4 building. The time period and base shear of the building evaluated by the equivalent static method and response spectrum method were found to be in good agreement. The storey drift ratio and beam-column capacity were found to be within the prescribed limits in relevant IS codes. For G + 4 building, located in SZ II and III a minimum raft thickness of 1000 mm is recommended for soil with a bearing capacity of 165 kN/m² while for soil with a bearing capacity of 200 and 250 kN/m² the minimum thickness of the raft is 500 mm. For G + 15 building, located in SZ II and III even a raft with a thickness of 2.0 m was not found sufficient and hence piled raft was used with a raft thickness of 1.75 m and a pile length of 10 m for all bearing capacity of soil considered.