Optimal Design of Sustainable Reinforced Concrete Precast Hinged Frames

Abstract

Sustainable development requires improvements in the use of natural resources. The main objective of the present study was to optimize the use of materials in the construction of reinforced concrete precast hinged frames. Proprietary software was developed in the Python programming language. This allowed the structure’s calculation, verification and optimization through the application of metaheuristic techniques. The final cost is a direct representation of the use of materials. Thus, three algorithms were applied to solve the economic optimization of the frame. By applying simulated annealing, threshold accepting and old bachelor’s acceptance algorithms, sustainable, non-traditional designs were achieved. These make optimal use of natural resources while maintaining a highly restricted final cost. In order to evaluate the environmental impact improvement, the carbon-dioxide-associated emissions were studied and compared with a reference cast-in-place reinforced concrete frame. The results showed designs with reduced upper slab and lateral wall depth and dense passive reinforcement. These were able to reduce up to 24% of the final cost of the structure as well as over 30% of the associated emissions.

Enhancing Robustness in Precast Modular Frame Optimization: Integrating NSGA-II, NSGA-III, and RVEA for Sustainable Infrastructure

“…The study specifically focuses on the life cycle design optimization of reinforced concrete precast modular frames (RCPMF). Building on previous SOO results for the precast and cast-in-place typologies [11,12], the study introduces a customized non-dominated sorting genetic algorithm II (NSGA-II), enhanced with three novel repair operators, to navigate the complex handling of the mixed integer programming (MIP) during the optimization process [13]. Furthermore, the framework proposed in the study is particularly relevant for its integration with MCDM techniques, computing the criteria weights via an entropy-based approach and applying the simple additive weighting (SAW) and faire un choix adéquat (FUCA) methods for assessing the optimization results [14].…”

Sustainable Road Infrastructure Decision-Making: Custom NSGA-II with Repair Operators for Multi-Objective Optimization

“…The ULS ensure the structural integrity of all sections under diverse loading scenarios. Compliance with ULS involves a multi-step verification process [15]. Initially, it requires verifying sectional shear stress resistance [26], followed by assessing normal stresses via N-M interaction diagrams.…”

“…These road frames in transportation infrastructure have been predominantly constructed on-site as reinforced concrete cast-in-place frames (RCCPF). However, recent research exploring the reduction in economic costs through SOO of reinforced concrete precast modular frames (RCPMF) has identified precasting as a viable alternative for road networks [15]. Furthermore, environmental assessments focusing on factors such as CO 2 emissions and embodied energy indicated that RCPMF presents environmental benefits compared to RCCPF [16].…”

Optimal Deep Learning Assisted Design of Socially and Environmentally Efficient Steel Concrete Composite Bridges under Constrained Budgets

“…The OBA algorithm was chosen for this study because it has been previously applied to other structural optimization problems [309]. In order to enhance exploration during the optimization process, a mutation operator was incorporated, following recent research [307].…”