Construction site layout planning (CSLP) is a typical problem in construction site management that urgently needs to be solved. A reasonable construction site layout can improve efficiency and reduce costs and unnecessary loss of time. To solve the CSLP problem, some emerging optimization algorithms and enhanced mathematical models have been developed. The building information model (BIM) provides data support for the implementation of previous research. However, manually extracting and modifying parameters from the BIM for use in optimization algorithms is cumbersome in practical applications, wasting considerable time and human resources. In this paper, we propose a framework of automated prefabricated building CSLP to realize the integration of the BIM and an optimization algorithm, which utilizes the Dynamo visual programming platform to create an information channel between the BIM and the optimization algorithm. A typical and practical case, which is optimized by the genetic algorithm (GA) and particle swarm optimization (PSO), is demonstrated to validate the proposed approach and to show an effective reduction in material transportation cost and unnecessary loss of time.

Limited to the current building information model (BIM) development stage, many engineers are still using CAD 2D drawings for top-down design, and then using drawings to generate BIM models for BIM-related applications. As the manual method of creating a semantic-rich BIM model is time consuming and could cause model defects, engineers are reluctant to apply the semantic-rich BIM model. To solve this problem, a semiautomatic methodology is proposed to generate a semantic-rich structural BIM model from two-dimensional (2D) computer-aided design (CAD) drawings. First, it uses the information classified by CAD layers to generate geometric models of beams, slabs, and columns. Then, the semantic information is appropriately structured and stored in the corresponding element models. Finally, the feasibility of this methodology is verified with two case studies of frame structures. The case studies show that this method can quickly and accurately create a structural BIM model with many elements that reflect the structural details described in the construction drawings. Nearly all text information in the construction drawings is accurately imported into the structural BIM model, and additional parameter spaces are reserved to facilitate the expansion of building information. The engineers in charge of creating a BIM model could benefit from this proposed methodology.

Tower cranes can cover most of the area of a construction site, which brings significant safety risks, including potential collisions with other entities. To address these issues, it is necessary to obtain accurate and real-time information on the orientation and location of tower cranes and hooks. As a non-invasive sensing method, computer vision-based (CVB) technology is widely applied on construction sites for object detection and three-dimensional (3D) localization. However, most existing methods mainly address the localization on the construction ground plane or rely on specific viewpoints and positions. To address these issues, this study proposes a framework for the real-time recognition and localization of tower cranes and hooks using monocular far-field cameras. The framework consists of four steps: far-field camera autocalibration using feature matching and horizon-line detection, deep learning-based segmentation of tower cranes, geometric feature reconstruction of tower cranes, and 3D localization estimation. The pose estimation of tower cranes using monocular far-field cameras with arbitrary views is the main contribution of this paper. To evaluate the proposed framework, a series of comprehensive experiments were conducted on construction sites in different scenarios and compared with ground-truth data obtained by sensors. The experimental results show that the proposed framework achieves high precision in both crane jib orientation estimation and hook position estimation, thereby contributing to the development of safety management and productivity analysis.

Tianjin 117 is a high-rise building comprised of a reinforced-concrete core and perimeter steel framing that includes megacolumns and megabraces, with a height of 597 m. As the construction process goes on, the influences of the external environment and loads are more crucial to structural safety. In order to verify the structural properties and behaviors in the construction stages, a structural health monitoring (SHM) system is developed in this paper by the authors’ research group. Various sensors are used in this SHM system to monitor different structural parameters that significantly affect the structural safety. The collected data can be analyzed using specifically developed software, thereby used for assessment of structural performance during construction. Interlinked with finite-element software, structural monitor/analysis/evaluation software is developed based on the idea of modular design, which can realize data exchange and result comparison between on-site monitoring and numerical mod...