The construction industry is suffering from a high rate of accidents that significantly affect the overall performance of projects. Compared to the conventional safety training methods, Virtual Reality (VR) games offer a more immersive and interactive learning experience for the participants. However, training scenarios in most of the existing VR games lack complex tasks and the realistic environment required for actual construction. This work proposes a conceptual framework for dynamically updating VR training games through information streaming from the digital twins. The training scenarios in VR games are automatically created from the project intent information, project status knowledge, safety regulations, and historical knowledge provided by the digital twins. Therefore, construction workers can be trained in realistic training environments with relevant tasks they soon afterwards pursue. Dynamic VR training is expected mutually beneficial for enhanced digital twins and periodic construction safety management. Inadequate technology readiness level of the construction industry and difficulty in collecting good quality data are some of the major challenges for implementing this framework.
PurposeReal-time location sensing (RTLS) systems offer a significant potential to advance the management of construction processes by potentially providing real-time access to the locations of workers and equipment. Many location-sensing technologies tend to perform poorly for indoor work environments and generate large data sets that are somewhat difficult to process in a meaningful way. Unfortunately, little is still known regarding the practical benefits of converting raw worker tracking data into meaningful information about construction project progress, effectively impeding widespread adoption in construction.Design/methodology/approachThe presented framework is designed to automate as many steps as possible, aiming to avoid manual procedures that significantly increase the time between progress estimation updates. The authors apply simple location tracking sensor data that does not require personal handling, to ensure continuous data acquisition. They use a generic and non-site-specific knowledge base (KB) created through domain expert interviews. The sensor data and KB are analyzed in an abductive reasoning framework implemented in Answer Set Programming (extended to support spatial and temporal reasoning), a logic programming paradigm developed within the artificial intelligence domain.FindingsThis work demonstrates how abductive reasoning can be applied to automatically generate rich and qualitative information about activities that have been carried out on a construction site. These activities are subsequently used for reasoning about the progress of the construction project. Our framework delivers an upper bound on project progress (“optimistic estimates”) within a practical amount of time, in the order of seconds. The target user group is construction management by providing project planning decision support.Research limitations/implicationsThe KB developed for this early-stage research does not encapsulate an exhaustive body of domain expert knowledge. Instead, it consists of excerpts of activities in the analyzed construction site. The KB is developed to be non-site-specific, but it is not validated as the performed experiments were carried out on one single construction site.Practical implicationsThe presented work enables automated processing of simple location tracking sensor data, which provides construction management with detailed insight into construction site progress without performing labor-intensive procedures common nowadays.Originality/valueWhile automated progress estimation and activity recognition in construction have been studied for some time, the authors approach it differently. Instead of expensive equipment, manually acquired, information-rich sensor data, the authors apply simple data, domain knowledge and a logical reasoning system for which the results are promising.
Over the last two decades, numerous approaches have been developed for automating construction safety assessment, that is, prevention through design and planning (PtD/P), that overcome the limitations of a purely manual safety assessment process. Despite this, such automated approaches are yet to be widely adopted in practice. Barriers include a lack of standardization of construction safety concepts within regulatory frameworks and a lack of confidence in automated approaches on the part of practitioners. This work presents a freely available 4D BIM‐based benchmark model (3D Building Information Model and 68 schedules) and construction safety ontology covering the two most frequent and severe construction site accidents: falls from height and struck‐by falling object hazards. The benchmark model and ontology enable the construction safety community to test and compare the performance of alternative automated approaches. Moreover, they are utilized in our PtD/P tool SafeConAI, and empirical evaluation results show how the safety situation changes among different construction schedules of the same project.
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