The utilization of Building Information Modeling (BIM) has been growing significantly and translating into the support of various tasks within the construction industry. In relation to such a growth, ...many approaches that leverage dimensions of information stored in BIM model are being developed. Through this, it is possible to allow all stakeholders to retrieve and generate information from the same model, enabling them to work cohesively. To identify gaps of existing work and evaluate new studies in this area, a BIM application framework is developed and discussed in this paper. Such a framework gives an overview of BIM applications in the construction industry. A literature review, within this framework, has been conducted and the result reveals a research gap for BIM applications in the project domains of quality, safety and environmental management. A computable multi-dimensional (nD) model is difficult to establish in these areas because with continuously changing conditions, the decision making rules for evaluating whether an individual component is considered good quality, or whether a construction site is safe, also vary as the construction progresses. A process of expanding from 3D to computable nD models, specifically, a possible way to integrate safety, quality and carbon emission variables into BIM during the construction phase of a project is explained in this paper. As examples, the processes of utilizing nD models on real construction sites are described. It is believed to benefit the industry by providing a computable BIM and enabling all project participants to extract any information required for decision making. Finally, the framework is used to identify areas to extend BIM research.
•A BIM application framework is developed and discussed.•A literature review, within this framework, has been conducted.•A process of expanding from 3D to computable nD models is explained.•The processes of utilizing nD models on real construction job sites are described.•The framework is also used to identify areas to extend BIM research.
Falls from heights (FFH) are major contributors of injuries and deaths in construction. Yet, despite workers being made aware of the dangers associated with not wearing a safety harness, many forget ...or purposefully do not wear them when working at heights. To address this problem, this paper develops an automated computer vision-based method that uses two convolutional neural network (CNN) models to determine if workers are wearing their harness when performing tasks while working at heights. The algorithms developed are: (1) a Faster-R-CNN to detect the presence of a worker; and (2) a deep CNN model to identify the harness. A database of photographs of people working at heights was created from activities undertaken on several construction projects in Wuhan, China. The database was then used to test and train the developed networks. The precision and recall rates for the Faster R-CNN were 99% and 95%, and the CNN models 80% and 98%, respectively. The results demonstrate that the developed method can accurately detect workers not wearing their harness. Thus, the computer vision-based approach developed can be used by construction and safety managers as a mechanism to proactively identify unsafe behavior and therefore take immediate action to mitigate the likelihood of a FFH occurring.
•A Faster-R-CNN and deep CNN model is developed to identify workers and their harness.•Precision and recall rates for the Faster R-CNN to detect workers were 99% and 95%, respectively.•Precision and recall rates for the CNN to detect those not wearing their safety harness were 80% and 98%, respectively.
Quality management in shield tunneling projects is a challenging problem. To improve the quality of segment erection, the shield driver must constantly adjust the shield's attitude and position to ...reduce a snakelike motion for fitting the design tunnel axis based on the driver's experience, which is untimely and less reliable during excavation. Considering the disadvantage of this control method, this paper presents a predictive framework for the attitude and position in shield tunneling by applying a hybrid deep learning model. This framework contains a wavelet transform noise filter, convolutional neural network feature extractor, and long short-term memory predictor for determining the attitude and position of the shield machine in the future. The prediction framework is tested with the collected data of Mixshield operated in the river-crossing tunnel project of Yangtze Sanyang Road, Wuhan, China. Six variables characterizing the shield attitude and position are selected to validate the feasibility and performance of our method. Results reveal that the proposed model outperforms the other three similar models in predictive accuracy and provides decision support for adjusting the attitude and position in shield tunneling.
•A new method for predicting the attitude and position of shield machine was proposed.•A hybrid deep learning model consisting of wavelet transform, CNN, and LSTM was constructed for the prediction task.•The on-site data collected from the largest river-crossing tunnel in China has been tested.•The prediction results comparing with other three models and the potential practice value were discussed.
•An automated computer vision-based method using CNN is developed to detect objects on construction sites.•The improved Faster R-CNN method is proposed, which achieves a good performance on detection ...of multi-scale objects.•Outperforms comparable results to the current state-of-the-art descriptor methods to detect construction objects on images.•A huge construction object database is established, and the robustness and generalization of CNN are demonstrated.
Detecting the presence of workers, plant, equipment, and materials (i.e. objects) on sites to improve safety and productivity has formed an integral part of computer vision-based research in construction. Such research has tended to focus on the use of computer vision and pattern recognition approaches that are overly reliant on the manual extraction of features and small datasets (<10k images/label), which can limit inter and intra-class variability. As a result, this hinders their ability to accurately detect objects on construction sites and generalization to different datasets. To address this limitation, an Improved Faster Regions with Convolutional Neural Network Features (IFaster R-CNN) approach is used to automatically detect the presence of objects in real-time is developed, which comprises: (1) the establishment dataset of workers and heavy equipment to train the CNN; (2) extraction of feature maps from images using deep model; (3) extraction of a region proposal from feature maps; and (4) object recognition. To validate the model’s ability to detect objects in real-time, a specific dataset is established to train the IFaster R-CNN models to detect workers and plant (e.g. excavator). The results reveal that the IFaster R-CNN is able to detect the presence of workers and excavators at a high level of accuracy (91% and 95%). The accuracy of the proposed deep learning method exceeds that of current state-of-the-art descriptor methods in detecting target objects on images.
The development of artificial intelligence technology is currently bringing about new opportunities in construction. Machine learning is a major area of interest within the field of artificial ...intelligence, playing a pivotal role in the process of making construction “smart”. The application of machine learning in construction has the potential to open up an array of opportunities such as site supervision, automatic detection, and intelligent maintenance. However, the implementation of machine learning faces a range of challenges due to the difficulties in acquiring labeled data, especially when applied in a highly complex construction site environment. This paper reviews the history of machine learning development from shallow to deep learning and its applications in construction. The strengths and weaknesses of machine learning technology in construction have been analyzed in order to foresee the future direction of machine learning applications in this sphere. Furthermore, this paper presents suggestions which may benefit researchers in terms of combining specific knowledge domains in construction with machine learning algorithms so as to develop dedicated deep network models for the industry.
•A systematic review of machine learning in construction is proposed.•The time intervals between theories and applications are analyzed.•The applications of deep learning are analyzed from different research directions.•The performance of machine learning applications in construction are discussed.
Hardhats are an important safety measure used to protect construction workers from accidents. However, accidents caused in ignorance of wearing hardhats still occur. In order to strengthen the ...supervision of construction workers to avoid accidents, automatic non-hardhat-use (NHU) detection technology can play an important role. Existing automatic methods of detecting hardhat avoidance are commonly limited to the detection of objects in near-field surveillance videos. This paper proposes the use of a high precision, high speed and widely applicable Faster R-CNN method to detect construction workers' NHU. To evaluate the performance of Faster R-CNN, more than 100,000 construction worker image frames were randomly selected from the far-field surveillance videos of 25 different construction sites over a period of more than a year. The research analyzed various visual conditions of the construction sites and classified image frames according to their visual conditions. The image frames were input into Faster R-CNN according to different visual categories. The experimental results demonstrate that the high precision, high recall and fast speed of the method can effectively detect construction workers' NHU in different construction site conditions, and can facilitate improved safety inspection and supervision.
•An algorithm based on deep learning for non-hardhat-use (NHU) detection is developed.•A diverse, annotated and reusable training dataset is built.•Image frames in testing dataset are characterized by various visual conditions.•Results demonstrate that precision, recall rate, speed and robustness are highly improved.•The proposed method can facilitate safety inspection and supervision.
•A non-parametric Bayesian network is developed for tunneling risk analysis.•The model is quantified by marginal distributions and pair-wise linear correlations.•Expert opinions on distributions and ...correlations are elicited.•The model includes both continuous and discrete variables.•The model is validated by real documented accidents.
Knowledge capture and reuse are critical in the risk management of tunneling works. Bayesian networks (BNs) are promising for knowledge representation due to their ability to integrate domain knowledge, encode causal relationships, and update models when evidence is available. However, the model development based on classic BNs is challenging when expert opinions are solicited due to the discretization of variables and quantification of large conditional probability tables. This study applies non-parametric BNs, which only require the elicitation of the marginal distribution corresponding to each node and correlation coefficient associated with each edge, to develop a knowledge-based expert system for tunneling risk analysis. In particular, we propose to use the pair-wise Pearson's linear correlations to parameterize the model because the assessment is intuitive and experts in the engineering domain are more familiar and comfortable with this notion. However, when Spearman's rank correlation is given, the method can also be used by modification of the marginals. The method is illustrated with a tunnel case in the Wuhan metro project. The expert knowledge of risk assessment for common failures in shield tunneling is integrated and visualized. The developed model is validated by real documented accidents. Potential applications of the model are also explored, such as decision support for risk-based design.
The manufacture, transportation and on-site assembly sectors of precast construction projects are often considered separately and managed by rule of thumb, causing an inefficient use of resources and ...postponed delivery. This study views these sectors as a whole from the perspective of a single machine batch-scheduling problem. A dynamic programming algorithm, which aims to search for solutions that entail maximum production efficiency, was developed accordingly with the constraints of changeover costs and production deadlines. We tested the method’s ability by processing as many products as possible simultaneously using real data collected from a precast factory in a simulation and compared the effect with a previous study. We found that our method possesses great potential to improve the efficiency of precast production.
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•A single machine batch scheduling model considering the manufacturing, transportation and on-site assembly of precast construction project simultaneously is proposed.•The proposed model can potentially reduce total production cost by processing as many products as possible in the same period.•A quicker algorithm based on the schema of existing dynamic programming algorithm is developed to solve the proposed problem.
The determination of people.s unsafe behavior from images in construction has been typically based on hand-made rule approaches, which renders it difficult to identify multiple acts of unsafe ...behavior within an image and accordingly apply safety rules. This article aims to develop a computer vision and deep learning method that can match images of people's unsafe behavior with semantic safety rules. Our proposed method consists of: 1) image feature representation; 2) safety rule feature representation; and 3) feature fusion similarity whereby unsafe behavior extracted from an image is matched with safety rules. We validate the effectiveness of our method using an image database of people's unsafe behavior from different sites associated with the construction of the Wuhan Metro Project (China). The results of our research explicitly demonstrate that our method is robust and can accurately recognize people's unsafe behavior and the corresponding safety rule that has been contravened. To this end, we suggest that construction organizations can use our method to manage safety better as part of a behavior-based safety strategy and thus prevent accidents.
•Association rules mining is combined with complex network to perform risk analysis.•An improved Apriori algorithm is developed to unearth abnormal monitoring types.•CN is used to reveal coupled ...relationships between abnormal monitoring types.•Coupled risks are analyzed using a large monitoring dataset.•The new approach for risk management is applied in a real subway project.
Due to geotechnical uncertainties, existing underground infrastructure, the construction of deep-pit foundations in dense urban areas is particularly challenging as there is a propensity for building and structural settlement to occur. Recognizing the need to proactively manage safety risks during construction, a new risk analysis approach that combines complex networks and association rules mining (ARM) is proposed. An improved Apriori algorithm is developed to unearth abnormal monitoring types. Then, complex network theory is introduced to examine the characteristics of the coupled relationships existing between different types of abnormal monitoring types. This research identifies and examines complex network measures to understand the topology of settlement networks. It is revealed that settlement networks confirm to both scale-free and small-word properties indicating that risks are not random events. This new approach of combining ARM with complex network is applied to examine deep foundation pits that are constructed for a subway project in Wuhan, China. It is demonstrated that proposed approach can successfully reveal the association rules between safety risk monitoring types and the coupling of risks. Preventative actions can therefore be undertaken in advance to mitigate against potential risks that are identified from the abnormal monitoring combinations.
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