Search

Abstract This article investigates the behavior of hybrid FRP Concrete-Steel columns with an elliptical cross section. The investigation was carried out by gathering information through literature and conducting a parametric study, which resulted in 116 data points. Moreover, multiple machine learning predictive models were developed to accurately estimate the confined ultimate strain and the ultimate load of confined concrete at the rupture of FRP tube. Decision Tree (DT), Random Forest (RF), Adaptive Boosting (ADAB), Categorical Boosting (CATB), and eXtreme Gradient Boosting (XGB) machine learning techniques were utilized for the proposed models. Finally, these models were visually and quantitatively verified and evaluated. It was concluded that the CATB and XGB are standout models, offering high accuracy and strong generalization capabilities. The CATB model is slightly superior due to its consistently lower error rates during testing, indicating it is the best model for this dataset when considering both accuracy and robustness against overfitting.

Abstract This paper presents an analysis and prediction of the shear strength of wide-shallow reinforced concrete beams, utilizing Finite Element Analysis (FEA) and machine learning techniques. The methodology involves validating a detailed Finite Element Model (FEM) against experimental results, conducting a parametric study, and developing three Machine Learning prediction equations. The FEM captures concrete and steel behaviors, including cracking and crushing for concrete and linear isotropic properties for steel reinforcement. Loading and boundary conditions are defined for accuracy and validated against 13 experimental specimens, exhibiting a maximum 8% and 12% difference in loads and deflections, respectively. A parametric study generates a dataset of 77 wide beam configurations, exploring variations in beam widths, concrete strengths, compression rebars, and shear reinforcement. This dataset is used to develop machine learning models, including “Genetic Programming (GP)”, “Evolutionary Polynomial Regression (EPR)”, and “Artificial Neural Network (ANN)”. Comparative analysis reveals GP and EPR models with over 95% correlation, while the ANN model outperforms with 99% accuracy. Sensitivity analysis underscores the significant influence of concrete strength and beam aspect ratio on shear strength. In conclusion, the study demonstrates the potential of FEA and machine learning models to predict shear strength in wide-shallow reinforced concrete beams, providing valuable insights for architectural design and engineering practices and emphasizing the role of concrete strength and beam geometry in shear behavior.

Abstract Materials require special consideration when developing a project plan because they make up such a sizable chunk of the overall budget. Materials supply and delivery are crucial especially in road construction projects as they are required for the daily construction process. Lack of materials is a major source of jobsite productivity loss. This is due to the lack of structured communication and clearly defined tasks in the current materials management methods. The divergence between design and construction, the failure to coordinate and integrate multiple functional specializations, and poor communication lead to excessive fragmentation. All of these contribute to performance issues like late material ordering and delivery, low productivity, and budget overruns. This research develops a material supply chain (MSC) framework for best practices in road construction projects at all phases. This ensures that contractors receive the supplies they need at the optimum time, with the required quantities, and at the lowest possible cost. Contractors can enhance output, save money, and stay competitive. A questionnaire was designed to investigate current practices in MSC, identify the most common obstacles that faced contractors throughout the project phases, and identify the most important contributors to the integration of supply chain in construction. The developed framework was then evaluated by road construction experts; 90% stated that the proposed framework promotes project participants to share information and data. 80% assured that the framework promotes completing the project with desired quality and encourages problem solving before it even occurs.

The structural progress of bridges in conjunction with efficiency has gained researchers’ attention in the last few decades. Structures optimization applying mathematical analysis is utilized to achieve sustainability in the design and construction of bridges. Despite the extensive research in this area of knowledge, further structural optimization development needs to be developed.The main goal of this research is to develop a decision support system (DSS) that selects the optimum superstructure configuration for highway bridges, considering financial and technical parameters. The most common structural systems in the longitudinal and transverse directions of bridges are considered in this research. Simple and continuous spans are included in the longitudinal direction, while open and closed sections for the transverse direction. Different construction materials are considered as well, like reinforced concrete, pre-stressed concrete, steel sections, and composite sections, to achieve a wide diversity of alternatives. The developed DSS was illustrated graphically as a map for the optimum superstructure configuration for certain span and span to depth ratio combinations. These different configurations obtained from the DSS were mapped three times. The first was based on direct cost only, the second on construction time only, and the third on the total cost of each alternative. Eventually, the DSS was verified using collected case studies and proposed a convenient selection of bridge superstructure configurations within the considered range of span dimensions.