Utilizing industry 4.0 on the construction site : challenges and opportunities

In recent years a step change has been seen in the rate of adoption of Industry 4.0 technologies by manufacturers and industrial organisations alike. This paper discusses the current state of the art in the adoption of industry 4.0 technologies within the construction industry. Increasing complexity in onsite construction projects coupled with the need for higher productivity is leading to increased interest in the potential use of industry 4.0 technologies. This paper discusses the relevance of the following key industry 4.0 technologies to construction: data analytics and artificial intelligence; robotics and automation; buildings information management; sensors and wearables; digital twin and industrial connectivity. Industrial connectivity is a key aspect as it ensures that all Industry 4.0 technologies are interconnected allowing the full benefits to be realized. This paper also presents a research agenda for the adoption of Industry 4.0 technologies within the construction sector; a three-phase use of intelligent assets from the point of manufacture up to after build and a four staged R&D process for the implementation of smart wearables in a digital enhanced construction site

Investigation of Tactile Sensory System Configuration for Construction Hazard Perception

The application of tactile-based wearable devices to assist in navigation for people with low sight/low memory has demonstrated the feasibility of using such devices as a means of communication. Accordingly, a previous study in construction research investigated various parameters of tactile signals to develop a communicable system for potential application in construction hazard communication. However, the nature of construction limits the application of such devices to the body of construction workers, and it is important to understand sensor design parameters for improved communication, which has not been given significant attention yet. Therefore, this study aims to determine key design factors such as the number of motors, spacing between sensors and the layout of a tactile sensory system to be used for communicating construction hazards to workers. For this purpose, this study focused on identifying the number of motors based on extensive literature and the problem of construction safety as to hazard communication, determining the arrangement that allowed for effective delivery and perception of information with minimum effort. The researchers conducted two experimental studies: First, to determine the minimum spacing between vibration motors that allows for the identification of each individual motor with high accuracy; and second, to determine the layout of motors that is suitable for effective communication of multiple types of information. More importantly, the tactile-sensor configuration identified from this study allows the workers to learn the signal patterns easily in order to identify multiple types of information related to hazards. Using such a communication system on construction sites will assist in transmitting hazard-related information to workers, and thus, protect the lives of workers. Such wearable technologies enable the detection of individual-level hazards and prevent worker fatalities and severe injurie

Robust Construction Safety System (RCSS) for Collision Accidents Prevention on Construction Sites

A proximity warning system to detect the presence of a worker/workers and to warn heavy equipment operators is highly needed to prevent collision accidents at construction sites. In this paper, we developed a robust construction safety system (RCSS), which can activate warning devices and automatically halt heavy equipment, simultaneously, to prevent possible collision accidents. The proximity detection of this proposed system mainly relies on ultra-wideband (UWB) sensing technologies, which enable instantaneous and simultaneous alarms on (a) a worker’s personal safety (personal protection unit (PPU)) device and (b) hazard area device (zone alert unit (ZAU)). This system also communicates with electronic control sensors (ECSs) installed on the heavy equipment to stop its maneuvering. Moreover, the RCSS has been interfaced with a global positioning system communication unit (GCU) to acquire real-time information of construction site resources and warning events. This enables effective management of construction site resources using an online user interface. The performance and effectiveness of the RCSS have been validated at laboratory scale as well as at real field (construction site and steel factory). Conclusively, the RCSS can significantly enhance construction site safety by pro-actively preventing collision of a worker/workers with heavy equipment

Automated Approach for the Enhancement of Scaffolding Structure Monitoring with Strain Sensor Data

Construction researchers have made a significant effort to improve the safety of scaffolding structures, as a large proportion of workers are involved in construction activities requiring scaffolds. However, most past studies focused on design and planning aspects of scaffolds. While limited studies investigated scaffolding safety during construction, they are limited to simple cases only with limited failure modes and simple scaffolds. In response to this limitation, this study aims to develop an automated scaffold monitoring approach capable of monitoring large scaffolds. Accordingly, this study developed an automated scaffold safety monitoring framework that leverages sensor data collected from a scaffold, scaffold modeling techniques, and a machine-learning approach. The proposed framework is based on the capability of the machine-learning approach to identify patterns, which in this study are the patterns of the scaffold structural response based on different loads acting on it. Due to the cost and safety issues related to testing an actual scaffold with varying load applications, the scaffold monitoring framework was experimentally tested under a controlled laboratory setting with a single-bay two-story scaffold with four safety cases. After the field trial, this approach was applied on a four-bay and three-story scaffold involving 1,411 safety cases through computational exploration. During this process, this study integrated a divide-and-conquer strategy with machine-learning models to improve the performance of large-scale classification. The results show that the proposed scaffold monitoring approach is capable of large-scale classification of scaffold safety status. Therefore, this approach can be reliably applied to monitor similar scaffolds on construction sites. Further, this approach is replicable to solve other classification problems. In addition, this study is expected to encourage the use of sensing technologies and data analysis techniques to develop automated monitoring approaches