The construction sector is widely recognized as having the most hazardous working environment among the various business sectors, and many research studies have focused on injury prevention strategies for use on construction sites. The risk-based theory emphasizes the analysis of accident causes extracted from accident reports to understand, predict, and prevent the occurrence of construction accidents. The first step in the analysis is to classify the incidents from a massive number of reports into different cause categories, a task which is usually performed on a manual basis by domain experts. The research described in this paper proposes a convolutional bidirectional long short-term memory (C-BiLSTM)-based method to automatically classify construction accident reports. The proposed approach was applied on a dataset of construction accident narratives obtained from the Occupational Safety and Health Administration website, and the results indicate that this model performs better than some of the classic machine learning models commonly used in classification tasks, including support vector machine (SVM), naïve Bayes (NB), and logistic regression (LR). The results of this study can help safety managers to develop risk management strategies.

Many research studies have focused on fire evacuation planning. However, because of the uncertainties in fire development, there is no perfect solution. This research proposes a fire evacuation management framework which takes advantage of an information-rich building information modeling (BIM) model and a Bluetooth low energy (BLE)-based indoor real-time location system (RTLS) to dynamically push personalized evacuation route recommendations and turn-by-turn guidance to the smartphone of a building occupant. The risk score (RS) for each possible route is evaluated as a weighted summation of risk level index values of all risk factors for all segments along the route, and the route with the lowest RS is recommended to the evacuee. The system will automatically re-evaluate all routes every 2 s based on the most updated information, and the evacuee will be notified if a new and safer route becomes available. A case study with two testing scenarios was conducted for a commercial office building in Tianjin, China, in order to verify this framework.

In order to support smart construction, digital twin has been a well‐recognized concept for virtually representing the physical facility. It is equally important to recognize human actions and the movement of construction equipment in virtual construction scenes. Compared to the extensive research on human action recognition (HAR) that can be applied to identify construction workers, research in the field of construction equipment action recognition (CEAR) is very limited, mainly due to the lack of available datasets with videos showing the actions of construction equipment. The contributions of this research are as follows: (1) the development of a comprehensive video dataset of 2,064 clips with five action types for excavators and dump trucks; (2) a new deep learning‐based CEAR approach (known as a simplified temporal convolutional network or STCN) that combines a convolutional neural network (CNN) with long short‐term memory (LSTM, an artificial recurrent neural network), where CNN is used to extract image features and LSTM is used to extract temporal features from video frame sequences; and (3) the comparison between this proposed new approach and a similar CEAR method and two of the best‐performing HAR approaches, namely, three‐dimensional (3D) convolutional networks (ConvNets) and two‐stream ConvNets, to evaluate the performance of STCN and investigate the possibility of directly transferring HAR approaches to the field of CEAR.

Due to inadequate use of building information modeling (BIM) at the individual level, many construction organizations have failed to realize the full benefits of BIM use, even in the postadoption stage. In this paper, BIM postadoption usage behaviors are classified into two categories: exploitation and exploration. To investigate whether and how confirmation, satisfaction, and perceived usefulness influence BIM usage behaviors, the expectation-confirmation model (ECM) of information technology (IT) continuance was adapted for an empirical study, and personal innovativeness with IT (PIIT) was incorporated as a moderator. A questionnaire survey was conducted, and 151 samples from nine companies were used in the analysis. The survey results revealed that (1) the effects of confirmation on BIM usage behaviors are mediated by perceived usefulness and satisfaction; (2) exploitation and exploration are intrinsically motivated by satisfaction and extrinsically motivated by perceived usefulness; and (3) PIIT enhances the positive effect of satisfaction on exploration. This research contributes to BIM adoption literature by focusing on postadoption usage behaviors and highlighting their diversity. Furthermore, this research has implications for understanding the influence mechanisms of exploitation and exploration and for adapting ECM in the context of BIM use. Suggestions are provided for ways that managers can encourage exploitation and stimulate exploration to realize the full value and potential of BIM.

While many countries have developed green building rating systems (GBRSs) to promote the concept of green buildings, it is difficult for designers to achieve better sustainability in the design process when using the real-time green building rating score as a reference. This paper proposes an intelligent green building rating (iGBR) framework supported by a semantic and social approach to realize real-time rating in building design. The framework features four components: (1) An ontology that is used to encapsulate the knowledge of green building rating, (2) score calculation rules that are encoded in Semantic Web Rule Language (SWRL), (3) Autodesk Forge, which is employed as a building information modeling (BIM)–based design platform to synchronize design models from different professions in the cloud, and (4) a group chat tool to connect all project participants in a social communication environment to effectively exchange data/information required for score calculation. A prototype iGBR system is developed based on the Evaluation Standard for Green Building of China (ESGBC) to verify the framework, so that a total of 95 articles can be assessed automatically in the real-time approach.

Commercial and public buildings are more vulnerable to fires because of their complex use functions, large number of centralized occupants, and the dynamic nature of the use of space. Due to the large number of these types of buildings and the limited availability of manpower, annual fire inspections cannot ensure the continuous compliance of fire codes. A crowdsourcing application, iInspect, is proposed in this paper to harvest collective intelligence in order to conduct mass inspection tasks. This approach is supported by building information modeling (BIM) based virtual reality (VR) and an indoor real-time localization system. Based on the International Fire Code and 27 fire inspection checklists compiled by various local authorities, a generic list of inspection items suitable for iInspect is proposed, along with a reputation-based monetary incentive model. A prototype of iInspect was created for Android mobile phones, and a case study was performed in an office building in Tianjin, China, for verification of this crowdsourcing inspection approach.