This paper presents a multi-objective optimization of steel trusses using direct analysis. The total weight and the inter-story drift or displacements of the structure were two conflict objectives, while the constraints relating to strength and serviceability load combinations were evaluated using nonlinear inelastic and nonlinear elastic analyses, respectively. Six common metaheuristic algorithms such as nondominated sorting genetic algorithm-II (NSGA-II), NSGA-III, generalized differential evolution (GDE3), PSO-based MOO using crowding, mutation, and ε-dominance (OMOPSO), improving the strength Pareto evolutionary algorithm (SPEA2), and multi-objective evolutionary algorithm based on decomposition (MOEA/D) were applied to solve the developed MOO problem. Four truss structures were studied including a planar 10-bar truss, a spatial 72-bar truss, a planar 47-bar powerline truss, and a planar 113-bar truss bridge. The numerical results showed a nonlinear relationship and inverse proportion between the two objectives. Furthermore, all six algorithms were efficient at finding feasible optimal solutions. No algorithm outperformed the others, but NSGA-II and MOEA/D seemed to be better at both searching Pareto and anchor points. MOEA/D was also more stable and yields a better solution spread. OMOPSO was also good at solution spread, but its stability was worse than MOEA/D. NSGA-III was less efficient at finding anchor points, although it can effectively search for Pareto points.

Irrigation and hydropower are among the most important sectors in the construction industry that propel the economic needs of a developing country like Vietnam. The construction of these projects often suffers from severe delays, leading to financial losses and other negative impacts on the economy. This paper aims to determine delay factors in the construction of these projects. Among many, 39 most important candidates of delay causes were identified from the literature review. Further surveys on project participants were conducted for the severity of these causes. An exploratory factor analysis was utilized to identify latent factors that cause delays in construction projects. The analysis result categorized a few groups of factors such as abnormal factors on the construction site (e.g., labor accidents, hydrology, water flow, extreme weather) and technical factors related to the construction contractor (e.g., unsuitable schedule, outdated construction technology, unprofessional workers) that have the greatest impact on the delay in construction of irrigation and hydropower projects in Vietnam. These findings contribute to the body of knowledge of project management and risk management, hence an improvement in the efficiency of the project sectors' performance.