In construction sites, safety issue has always been a threatening risk that must be taken seriously. In order to improve the safety management capability of construction site, this paper establishes a construction site fall hazard management system. This system takes computer vision instead of sensor systems as an information exchange bridge between the physical construction site and the virtual model. The main contribution of this paper is to propose a deep learning-based approach to achieve bi-directional information exchange from physical construction sites to digital models. To train the deep learning model, this paper builds a dataset containing approximately 4000 images from multiple sources to detect on-site safety risks. This paper also proposes a method for acquiring coordinates of workers and risk sources, then establishes an automated safety management framework based on the real-time locations of risk sources and workers. The effectiveness of this safety management framework is verified through a case study comparing the differences between safety officers and the automated system. In addition, the method proposed in this paper integrating building information modeling (BIM) with the real construction process, achieving bi-directional coordination between the digital level and the physical level, providing data basis for intelligent construction.

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.

Cyber–physical systems (CPSs) are generally considered to be the next generation of engineered systems. However, the actual application of CPSs in the Architecture, Engineering and Construction (AEC) industry is still at a low level. The sensing method in the construction process plays a very important role in the establishment of CPSs. Therefore, the purpose of this paper is to discuss the application potential of computer vision-based sensing methods and provide practical suggestions through a literature review. This paper provides a review of the current application of CPSs in the AEC industry, summarizes the current knowledge gaps, and discusses the problems with the current construction site sensing approach. Considering the unique advantages of the computer vision (CV) method at the construction site, the application of CV for different construction entities was reviewed and summarized to achieve a CV-based construction site sensing approach for construction process CPSs. The potential of CPS can be further stimulated by providing rich information from on-site sensing using CV methods. According to the review, this approach has unique advantages in the specific environment of the construction site. Based on the current knowledge gap identified in the literature review, this paper proposes a novel concept of visual-based construction site sensing method for CPS application, and an architecture for CV-based CPS is proposed as an implementation of this concept. The main contribution of this paper is to propose a CPS architecture using computer vision as the main information acquisition method based on the literature review. This architecture innovatively introduces computer vision as a sensing method of construction sites, and realizes low-cost and non-invasive information acquisition in complex construction scenarios. This method can be used as an important supplement to on-site sensing to further promote the automation and intelligence of the construction process.

Currently, construction projects are getting more complex, applying more information and communication technologies (ICT), while few studies use real-time data to dynamically optimize construction. The purpose of this article is to study the current development status of the optimization applied dynamically in the construction phase and their potential for applying real data collected by ICT. This article reviews 72 relevant optimization methods and identified some of the ICT research studies that can provide them with dynamic data. The dynamic triggering mode of each research is first analyzed, then its dynamic way, dynamic data, data resource, optimization object, and method are identified and formulated. The results reveal the great value of dynamic optimization in dealing with the complicated and uncertain contextual conditions in construction. Different dynamic triggering modes have different affinities with real data. Then, through the analysis of ICT articles, the huge potential of these dynamic optimization methods in applying real data is shown. This paper points out the most practical dynamic mode for engineers or managers to continuously apply optimization methods to solve dynamic problems in construction, and put forward scientific questions for related researchers: How does one combine ICT with the event dynamics or uncertain parameters? Based on this, the research gap of this area is identified a conceptual solution is proposed.

A cast‐steel joint is always used as an important component for the safety of the whole structure at key points, and the capability of the joint is important for the cast‐steel joint. This paper conducts an experimental and numerical study on a 1/2 scale model of a joint in a real project. The experiment is used to validate the numerical model and then make further studies on the behavior of the joint by using a numerical method. Combining the result of the scale model test and numerical analysis shows that the joint can bear the prescribed load and it is still in the elastic stage. This paper also discusses the influence of the absence of the secondary moment, torque, and shear force. For the core area of the joint, the secondary moment and torque have little effect on the stress distribution, and the axial force of the secondary branches can increase the capability of the joint. But the secondary shear forces do have some effect on the stress of core area, and an enhancement coefficient is used to enlarge the axial force and moment to cover the absence of shear in the test.