Construction site layout planning (CSLP) that considers multi-objective optimization problems is essential to achieving sustainable construction. Previous CSLP optimization methods have applied to traditional cast-in-place buildings, and they lack the application for sustainable prefabricated buildings. Furthermore, commonly used heuristic algorithms still have room for improvement regarding the search range and computational efficiency of optimal solution acquisition. Therefore, this study proposes an improved multi-objective optimization and decision-making method for layout planning on the construction sites of prefabricated buildings (CSPB). Firstly, the construction site and temporary facilities are expressed mathematically. Then, relevant constraints are determined according to the principles of CSLP. Ten factors affecting the layout planning on the CSPB are identified and incorporated into the method of layout planning on the CSPB in different ways. Based on the elitist non-dominated sorting genetic algorithm (NSGA-II), an improved multiple population constraint NSGA-II (MPC-NSGA-II) is proposed. This introduces the multi-population strategy and immigration operator to expand the search range of the algorithm and improve its computational efficiency. Combined with the entropy weight and technique for order preference by similarity to an ideal solution (TOPSIS), improved multi-objective optimization and decision for the CSLP model is developed on the CSPB. Practical cases verify the effectiveness and superiority of the algorithm and model. It is found that the proposed MPC-NSGA-II can solve the drawbacks of the premature and low computational efficiency of NSGA-II for multi-constrained and multi-objective optimization problems. In the layout planning on the CSPB, the MPC-NSGA-II algorithm can improve the quality of the optimal solution and reduce the solution time by 75%.

Bugholes are surface imperfections that appear as small pits and craters on concrete surface after the casting process. The traditional measurement methods are carried out by in situ manual inspection, and the detection process is time‐consuming and difficult. This paper proposed a deep‐learning‐based method to detect bugholes on concrete surface images. A deep convolutional neural network for detecting bugholes on concrete surfaces was developed, by adding the inception modules into the traditional convolution network structure to solve the problem of the relatively small size of input image (28 × 28 pixels) and the limited number of labeled examples in training set (less than 10 K). The effects of noise such as illumination, shadows, and combinations of several different surface imperfections in real‐world environments were considered. From the results of image test, the proposed DCNN had an excellent bughole detection performance and the recognition accuracy reached 96.43%. By the comparative study with the Laplacian of Gaussian (LoG) algorithm and the Otsu method, the proposed DCNN had good robustness which can avoid the interference of cracks, color‐differences, and nonuniform illumination on the concrete surface.

The long-span double-deck truss girder bridge has become a recommend structural form because of its good performance on traffic capacity. However, the vortex-induced vibration (VIV) characteristics for double-deck truss girders are more complicated and there is a lack of related research. In this research, wind tunnel tests were utilized to investigate the VIV characteristics of a large-span double-deck truss girder bridge. Meanwhile, the VIV suppression effect of the aerodynamic mitigation measures was measured. Furthermore, the VIV suppression mechanism was studied from the perspective of vortex shedding characteristics. The results indicated that the double-deck truss girder had a significant VIV when the wind attack angles were +3° and +5°. The aerodynamic mitigation measures had an influence on the VIV response of the double-deck truss girder. The upper chord fairing and lower chord inverted L-shaped deflector plate played a crucial role in suppressing VIV. Numerical analysis indicated that vortex shedding above the upper deck or in the wake region may dominate vertical VIV, while vortex shedding in the wake region of the lower deck may dominate torsional VIV. The upper chord fairing and lower chord inverted L-shaped deflector plate disrupted the original vortex shedding pattern in both regions, thereby suppressing VIV. This research can provide a foundation for bridge design and vibration suppression measures for large-span double-deck truss girder bridges.