Since tremendous resources are consumed in the architecture, engineering, and construction (AEC) industry, the sustainability and efficiency in this field have received increasing concern in the past few decades. With the advent and development of computational tools and information technologies, structural optimization based on mathematical computation has become one of the most commonly used methods for the sustainable and efficient design in the field of civil engineering. However, despite the wide attention of researchers, there has not been a critical review of the recent research progresses on structural optimization yet. Therefore, the main objective of this paper is to comprehensively review the previous research on structural optimization, provide a thorough analysis on the optimization objectives and their temporal and spatial trends, optimization process, and summarize the current research limitations and recommendations of future work. The paper first introduces the significance of sustainability and efficiency in the AEC industry as well as the background of this review work. Then, relevant articles are retrieved and selected, followed by a statistical analysis of the selected articles. Thereafter, the selected articles are analyzed regarding the optimization objectives and their temporal and spatial trends. The four major steps in the structural optimization process, including structural analysis and modelling, formulation of optimization problems, optimization techniques, and computational tools and design platforms, are also reviewed and discussed in detail based on the collected articles. Finally, research gaps of the current works and potential directions of future works are proposed. This paper critically reviews the achievements and limitations of the current research on structural optimization, which provide guidelines for future research on structural optimization in the field of civil engineering.

Precast concrete elements are widely adopted and the performance of precast structures is relying on the quality of connections between adjacent elements. For reinforced precast concrete elements, rebar positions are important for the overall structural performance, however, they are usually manually inspected. This study develops a technique for automated position estimation of rebars on reinforced precast concrete elements using colored laser scan data. A novel mixed pixel filter is developed to remove mixed pixels from the raw scan data based on both distance and color difference. A one‐class classifier is used for extracting rebars from all the data based on both geometric and color features of points. Furthermore, a novel rebar recognition algorithm is developed to recognize individual rebars based on two newly defined metrics. Experiments on two reinforced precast concrete bridge deck panels were conducted and showed that the proposed technique can accurately and efficiently estimate rebar positions.