Tunnel construction fundamentally differs from building and aboveground civil infrastructure projects. Drill-and-blast is one of the most common and flexible tunnel construction methods. However, it is complex and challenging because a large amount of data is generated from dispersed, independent, and heterogeneous sources. The tunneling industry still uses traditional project management techniques to manage complex interactions between these data sources that are hardly linked, and independent decisions are often made without considering all the relevant aspects. In this context, tunnel construction exhibits uncertainties and risks due to unforeseen circumstances, intricate design, and ineffective information management. Building information modeling (BIM) in the construction industry provides a solution to such issues with effective data information modeling. Existing research has considered a very general BIM semantic model and focused only a small portion of the entire drill-and-blast construction process. Tunnel boring machine (TBM) projects have successfully applied linked data models and multimodel concepts in BIM, but those technologies have yet to be adopted in drill-and-blast tunneling. To address that gap, a novel BIM-based multimodel tunnel information modeling (TIM) framework is presented here to improve project management, construction, and delivery by integrating five interlinked data models and project performance data for drill-and-blast tunnel construction. Data models of tunnel construction processes are linked to propose the Industry Foundation Classes (IFC)-Tunnel classes based on the objects, relationships, and property set definitions of the IFC schema. To validate the proposed framework, an implementation case study of a hydropower tunneling project is presented. The results indicate that the framework facilitates data sharing, information integration, data accessibility, design optimization, project communication, efficient project management, and visualization of tunnel design and construction processes.

Underground utilities are important assets that provide basic services for society’s daily life. They are generally very complex and remain unnoticed until they fail due to any particular reason. The stakeholders involved in the design, construction, and maintenance of utility infrastructure face many problems due to the traditional underground utility management system, resulting in injuries, loss of life, disruptions, project delays, and financial loss. The problem with the traditional system is that it uses 2D drawings and keeps unreliable information or a lack of updated information, which makes it an inefficient utility management system. With the advancement in construction information technology, we can address this effectively by integrating BIM and GIS. In this paper, a novel integrated BIM-GIS framework for underground utility management systems was developed on the basis of IFC to CityGML mapping. It provides an effective underground utility management system that facilitates designers in optimization of the design, assists in the excavator’s operator by providing real-time three-dimensional spatial information during the construction process, and acts as an as-built information database for utility facility management. For validation, a real-time project case study indicated that the proposed system can effectively provide comprehensive underground utility information at different project stages.

The rapid development in underground infrastructure is encouraging faster and more modern ways, such as TBM tunneling, to meet the needs of the world. However, tunneling activities generate complex and heterogeneous data, which makes it difficult to visualize the performance of a project. Advancements in information technology, such as digital twins and machine learning, provide platforms for digital demonstration, visualization, and system performance monitoring of such data. Therefore, this study proposes a digital twin-driven framework for TBM performance prediction through machine learning, visualization, and monitoring. This novel approach integrates machine learning and real-time performance data to predict, visualize, and monitor the status of the tunnel construction progress. A digital twin virtual model of TBM was constructed based on TBM design parameters, the input parameter, boring energy, RPM, torque, thrust force, speed, gripper pressure, total revolution, and Q-value provided to SVR and ANN models to predict the TBM AR and PR, and TBM daily progress was visualized continuously. The predictive performance indices R² (0.97) and RMSE (0.011) were estimated for AR prediction, showing the accuracy of the proposed model. To demonstrate the proposed framework, this study shows the its effectiveness. By implementing this framework, stakeholders can minimize the risk associated with the cost and schedule of a tunneling project by simultaneously visualizing and monitoring the performance of TBMs through digital twin and machine learning algorithms.

Virtual reality (VR) can potentially enhance various design and construction assessment intensive tasks, such as construction design and review. However, it may lead to cognitive overload, adversely affecting the participants’ performance. It is critical to understand the effects of VR cognitive behavior for implementing VR technology in the construction industry. The principal objective of this study was to investigate the participants’ cognitive load (CL), task performance (TP), and situational awareness (SA) in the VR environment for the evaluation of building design review tasks. Participants were asked to review the design task based on their memory knowledge and understanding in one of the three environments: paper-based, monitor-based, and immersive virtual environment. Participants’ CL was measured using the National Aeronautics and Space Administration Task Load Index (NASA TLX), TP was evaluated on completion time and the number of errors correctly detected, and situational awareness (SA) was assessed using the Situational Awareness and Review Technique (SART). The statistical results show a high CL and better performance in the immersive virtual environment. These findings can contribute to a better understanding of cognitive process characteristics and capabilities for design review activities in the VR environment.

With rapid urbanization, the utilization of underground space has become an important part of infrastructure. However, the stability of underground spaces such as large caverns remains a key challenge in civil engineering throughout the lifecycle of a project. Traditional methods of stability assessment rely on static models and periodic monitoring and often fail to capture real-time changes in rock behavior, leading to potential safety risks and, in severe cases, even the collapse of underground infrastructure. To address this challenge, this study introduces a digital twin (DT) framework to improve stability assessments and monitor deformations in underground structures. The framework enables the continuous monitoring and adaptive optimization of rock support systems by combining real-time sensor data with virtual simulations. A five-dimensional DT framework comprises physical objects, virtual objects, service systems, DT data, and their interconnections. It incorporates six key modules, which are structure, geology, material, behavior, performance, and environment, to enhance the understanding of cavern stability. The framework is based on Industry Foundation Classes standards to ensure seamless data exchange, interoperability, and the standardized representation of geotechnical and structural data. A seven-step methodology is developed for this framework, encompassing geological assessment, virtual modeling, Building Information Modeling (BIM)-based design, construction processes, real-time monitoring, and optimization strategies. To evaluate its effectiveness, the framework is applied to a case study, demonstrating improvements in deformation monitoring and rock support efficiency. The findings highlight the potential of integrating DT with BIM to enhance safety, reliability, and long-term stability in underground construction projects.

Advancements in deep learning and vision-based activity recognition development have significantly improved the safety, continuous monitoring, productivity, and cost of the earthwork site. The construction industry has adopted the CNN and RNN models to classify the different activities of construction equipment and automate the construction operations. However, the currently available methods in the industry classify the activities based on the visual information of current frames. To date, the adjacent visual information of current frames has not been simultaneously examined to recognize the activity in the construction industry. This paper proposes a novel methodology to classify the activities of the excavator by processing the visual information of video frames adjacent to the current frame. This paper follows the CNN-BiLSTM standard deep learning pipeline for excavator activity recognition. First, the pre-trained CNN model extracted the sequential pattern of visual features from the video frames. Then BiLSTM classified the different activities of the excavator by analyzing the output of the pre-trained convolutional neural network. The forward and backward LSTM layers stacked on help the algorithm compute the output by considering previous and upcoming frames’ visual information. Experimental results have shown the average precision and recall to be 87.5% and 88.52%, respectively.