Cost overrun has remained a key risk of construction projects that can be prevented by utilizing new technologies. This paper aims to identify the gap in the literature, which can potentially be addressed by using digital tools and technologies, by reviewing the current and state of the art practices. The paper presents the results of a systematic and critical content reviews on cost overruns, to address the question of what factors are affecting the cost overrun. This paper also reviews how building information modeling (BIM) in conjunction with other tools, such as the common tools in the Asia and Asia Pacific regions, are used for cost estimation and monitoring. The paper presents the results of the content review, including their contributions and limitations, which are also used to set key directions for future investigations. A total of 176 papers was identified to develop the construction cost management (CCM) dataset. The method was a mix of systematic reviews, including co-authorship network analyses, co-occurrence analytical map development covering 5671 keywords, and content analysis including theme identification and a critical review of selected papers. The paper critically reviewed 63 selected papers from CCM, which are divided into four clusters based on their scopes: BIM adoption for cost estimation and quantity surveying; BIM implementation for a bill of quantity, risk paths, and cost overruns; cost control and management; and, finally, BIM, virtual design, and value management. A trend analysis using a set of 16 themes (e.g., 3D model, BIM, Decision, Energy, and Life Cycle) for all the papers over the past ten years was developed. The content of each cluster of papers was reviewed based on the frequency of the selected themes in each cluster. The content of each cluster of papers was also reviewed critically and gaps were identified, so a set of directions for future investigations are presented.

Bridges are one of the most important elements of a sustainable transportation infrastructure network and require significant care to guarantee they function in a safe and reliable manner. Ensuring effective bridge maintenance and life cycle management in the face of aging and deterioration requires precise performance data and efficient assessment technologies. Because of the overall mediocre quality of U.S. infrastructure, there is a crucial need to enhance its monitoring and management. Digital twin is an emerging and promising concept that can offer the required solution. Advances in sensors, computing, communications, and predictive data analytics, coupled with biannual inspections, offer a wealth of bridge assessment data that will need a centralized digital twin framework for integration, analysis, and optimal decision-making with regard to maintenance. Leveraging and integrating these technologies in the new paradigm of a bridge digital twin will provide state transportation agencies with additional resources not previously available to enhance bridge operation and maintenance. A major value of a digital twin is the new knowledge created by the integration of information resources. This paper provides a framework for bridge digital twin that incorporates existing technologies, namely, building information modeling, structural health monitoring, intelligent transportation systems, and geographic information systems. The paper also provides an overview of practical applications and use cases of digital twin for bridges and structures, and offers an assessment of the challenges posed by the technology.

In recent years, a growing amount of research has focused on improving the performance of industrialized construction using emerging technologies. It is still necessary to have an in-depth understanding of the industry practitioners’ perspectives on the application of emerging technologies. Thus, a well-designed survey was distributed to industry practitioners who have been involved in industrialized construction projects. Then, a set of data analysis methods were utilized on the collected data to address the proposed four specific research questions. Results indicate that 3D and nD models, sensing techniques, and business information models are the technologies with the highest current utilization level. Extended reality, additive manufacturing, and advanced data analytics are the technologies with the highest development potential. Project inputs (e.g., cost, time, and labor), as well as implementation cost and software constraints (e.g., capital costs, software upgrading, and compatibility), are the main factors that affect practitioners’ decisions to adopt emerging technologies in industrialized projects. Inter-group comparison results indicate that company background has little significant influence on practitioners’ perspectives, while personal career profiles can significantly affect practitioners’ perspectives. Significantly, by uncovering the suggestions and viewpoints of practitioners, this paper aligns academic research with industry needs, ultimately providing guidance on future research directions and applications.

Active leading indicators (ALIs) have the potential to identify safety hazards and prompt immediate actions to prevent incidents. Currently, there is a major gap in research that incorporates a fully automated ALI system because implementation has been hindered by a lack of established industry thresholds of measurable performance that would trigger an actionable response. Therefore, this paper addresses this gap by presenting a new method that utilizes the Internet of Things (IoT) to collect quantifiable data which can trigger an actionable response in real time based on established thresholds. This novel method integrates the Construction Industry Institute (CII) active leading indicator framework with a prototype IoT-based system. Significantly, the ALI provides the physical–virtual feedback loop, which is an essential aspect of the IoT system because it provides real-time feedback to both the users and systems. This paper also identifies potential inputs to the ALI framework from emerging IoT-enabled systems. A case study was presented to initially validate the IoT-based ALI framework. Bluetooth-enabled heart rate monitors were issued to workers in a hazardous and critical mining construction site. The ALIs that were recorded included heart rate and body temperature. Thresholds were established that alerted the monitoring safety staff when a worker exhibited potentially unsafe conditions. The results of the study demonstrated the feasibility of the system. Additionally, other results included worker resistance; non-disclosing of medical conditions, and limitations for IoT connectivity.