Heritage buildings are subject to severe damage due to their exposure to dynamics such as environmental changes, earthquakes, structural loads, etc., thus needing a proper maintenance and management system. However, during the restoration, maintenance, and management process, heritage building practitioners face numerous challenges, such as inefficient project management, financial loss, and project delay. These problems arise due to a lack of digital documentation and updated information management systems. Heritage building management still uses traditional management techniques where heritage information is managed using multiple format systems by different professionals, thus missing collaboration, information integration, and interoperability. Building information modeling (BIM) is presented here as a supporting tool to address these issues. The aim of this study is to develop a novel HBIM framework to manage heritage buildings in an integrated and interoperable environment that supports 3D digital documentation to conserve a heritage building and facilitate restoration planning and facility management (FM) activities. Moreover, we demonstrate FM integration with BIM to promote this digital technique. The results indicate that the proposed framework is feasible and effective in providing information integration and communication between stakeholders in managing heritage buildings.

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.

The successful quality control and quality assurance of compaction operations are vital for the long-term performance of earth structures. Traditional in situ measurement methods are in practice for assessing compaction project specifications. These methods have several shortcomings and cannot provide complete compaction quality information. With the advancement in automation and information technology in the construction section, intelligent compaction roller technology has the potential to solve this problem. However, this technology still has many problems and needs more comprehensive studies for its implementation. This study focuses on the development of a Web-GIS-based intelligent compaction system and the assessment of the practical application of this technology. The developed system consists of major components—namely, system hardware and software—to provide real-time compaction information and an effective management system. An experimental study was conducted to assess the correlation between the developed system’s compaction quality and traditional measurement methods. The linear regression analysis identifies the strong correlation and promises the feasibility of an intelligent compaction system instead of a traditional in situ test for compaction quality control. Two implementation case studies of real-time projects are presented to validate and demonstrate the practicality of the developed system.