Science Mapping for Recent Research Regarding Urban Underground Infrastructure

The presented research conducted a bibliometric analysis regarding academic publications, especially journal publications, in the area of urban underground infrastructure (UI) systems (which include sewer pipes, drinking water pipes, cables, tunnels, etc.). In total, 547 journal papers published from 2002 to July 2022 (around 20 years period) were retrieved from Scopus using the proposed data collection method. Bibliometric analysis was conducted to extract and map the hidden information from retrieved papers. As a result, networks regarding co-citation, co-authorship, and keywords co-occurrence were generated to visualise and analyse the knowledge domain, patterns, and relationships. The eight most investigated topics in the UI research are identified and discussed, which provides an overview of the research history and focuses. Further, five potential research directions are suggested for researchers in the UI research area. The main contribution of this research is on revealing the knowledge domain of UI research in a quantitative manner as well as identifying the possible research directions

Exploring the effect of stakeholder relationship quality on technological innovation in off-site construction: the mediating role of the knowledge sharing

Off-site construction (OSC) is generally propagated as a sustainable and green construction method in the global construction industry. Over the past few decades, OSC has become famous worldwide for its numerous benefits. Technological innovation can speed up the development of OSC and has attracted a lot of attention from stakeholders who are promoting technological innovation by seeking collaborations. OSC is different from traditional manufacturing, and little effort has been spent on how the stakeholder relationship quality affects technological innovation. This study therefore makes efforts to explore the mechanism of how stakeholder relationship quality influences the OSC technological innovation and to explain the stakeholder relationship quality in terms of communication, trust, and commitment. This paper constructs a multidimensional hypothesis model consisting of five concepts: communication, trust, commitment, knowledge sharing, and technological innovation. A valid sample of 125 was collected through a questionnaire survey in mainland China. The sample data were dealt with and analyzed using partial least squares structural equation modeling (PLSSEM) to validate the proposed hypothesis model. The results reveal that trust and knowledge sharing affect technological innovation directly. Communication and commitment are not identified to have statistically significant influences on technological innovation in OSC. Communication, trust, and commitment positively contribute to knowledge sharing. Last, knowledge sharing completely and partially mediates between relationship quality and technological innovation. This study explores the impact of stakeholder relationship quality on OSC technological innovation and verifies the mediating role of knowledge sharing. These findings provide valuable theoretical guidance for OSC technological innovation and practical insights for stakeholders to promote technological innovation by enhancing relationship quality and knowledge sharing. First published online 13 December 202

Construction and maintenance of urban underground infrastructure with digital technologies

Urban underground infrastructure is a critical component in cities to provide essential services to residents. Research efforts have been made to facilitate different activities of underground infrastructure projects using various methods, particularly digital technologies. To obtain deeper insights from existing research and provide directions for future research, this study conducts a comprehensive review of research on underground infrastructure construction and Operation & Maintenance (O&M) with a focus on digital technologies. The in-depth review was conducted based on 145 publications from the perspective of locating and mapping, construction and coordination, as well as O&M. Consequently, critical limitations and challenges are revealed, such as the lack of as-built and as-is information, the requirement of data quality and quantity for deep learning methods, the lack of fully automated robotic systems, etc. Afterwards, a status matrix was presented to identify the level of different digital technologies being studied and their future application potential for key activities of underground infrastructure projects. In the end, future research trends are proposed, including (1) digital twinning of underground infrastructure, (2) quality and uncertainty of inspection data, (3) data generation and semi-supervised learning, (4) predictive maintenance, and (5) fully automated robotic systems for inspection and maintenance. This study contributes to the body of knowledge by identifying the challenges and limitations of existing studies through a systematic review, providing a clear view of the achievements and potentials of digital technologies for underground infrastructure, and proposing future research directions to facilitate digital transformation in this area

Recent advancements of robotics in construction

In the past two decades, robotics in construction (RiC) has become an interdisciplinary research field that integrates a large number of urgent technologies (e.g., additive manufacturing, deep learning, building information modelling (BIM)), resulting in the related literature being both fragmented and vast. This paper has explored the advances in RiC in the past two decades using a mixed quantitative-qualitative review method. Initially, 940 related articles (170 journal articles and 770 conference papers) were identified by keyword-searching in Scopus and then fed into a bibliometric analysis to build science maps. Following this, a qualitative discussion highlights recent achievements in RiC across three dimensions: tasks, algorithms, and collaborations. Moreover, four future research directions are proposed: 1) in-depth integration of BIM and robotics; 2) near-site robotic fabrication; 3) deep reinforcement learning for flexible environment adaption; and 4) high-level robot-to-robot collaboration. The contributions of this research are twofold: 1) identifying the latest research topics and trends concerning robotic technologies in construction; and 2) providing in-depth insights into the future direction of RiC. The findings from this research can serve both academia and industry in terms of promoting robotic algorithms, hardware, and applications in construction industry

Science mapping for recent research regarding urban underground infrastructure

The presented research conducted a bibliometric analysis regarding academic publications, especially journal publications, in the area of urban underground infrastructure (UI) systems (which include sewer pipes, drinking water pipes, cables, tunnels, etc.). In total, 547 journal papers published from 2002 to July 2022 (around 20 years period) were retrieved from Scopus using the proposed data collection method. Bibliometric analysis was conducted to extract and map the hidden information from retrieved papers. As a result, networks regarding co-citation, co-authorship, and keywords co-occurrence were generated to visualise and analyse the knowledge domain, patterns, and relationships. The eight most investigated topics in the UI research are identified and discussed, which provides an overview of the research history and focuses. Further, five potential research directions are suggested for researchers in the UI research area. The main contribution of this research is on revealing the knowledge domain of UI research in a quantitative manner as well as identifying the possible research directions.</p

Precast production scheduling in off-site construction: Mainstream contents and optimization perspective

Precast production scheduling (PPS) is a key factor that enables efficient off-site construction (OSC) and has received considerable attention from researchers. However, there is still a lack of systematic analysis and summary of existing PPS-related studies in the OSC domain to identify current research gaps and predict future research directions. Thus, 75 relevant academic publications were selected for this systematic review. The current research status of PPS was analyzed from four aspects (flow shop scheduling, production rescheduling, internal resource constraints, and external supply chain constraints) to explore the mainstream contents and optimization perspectives of PPS research. The research findings showed that (1) research on flow shop scheduling of precast components (PCs) was dominant in the PPS domain, and the genetic algorithm (GA) was the most applied optimization algorithm; (2) worker allocation strategy, mold grouping, and layout planning of production space were the main starting points for optimizing PPS from a resource perspective; and (3) establishing a collaborative scheduling mechanism by integrating various departments of the OSC supply chain to achieve the just-in-time (JIT) delivery strategy was the main idea for optimizing PPS from the supply chain perspective. This study revealed potential future research focuses in the field of PPS, including PC flow shop scheduling based on carbon emission targets, distributed permutation flow shop scheduling of PCs, layout optimization for intelligent production shops, and production scheduling mechanisms based on digital technology. This study provides theoretical guidance to promote the future development of PPS research in the field of OSC and can help precast production practitioners manage production scientifically and efficiently

Construction and maintenance of urban underground infrastructure with digital technologies

Urban underground infrastructure is a critical component in cities to provide essential services to residents. Research efforts have been made to facilitate different activities of underground infrastructure projects using various methods, particularly digital technologies. To obtain deeper insights from existing research and provide directions for future research, this study conducts a comprehensive review of research on underground infrastructure construction and Operation & Maintenance (O&M) with a focus on digital technologies. The in-depth review was conducted based on 145 publications from the perspective of locating and mapping, construction and coordination, as well as O&M. Consequently, critical limitations and challenges are revealed, such as the lack of as-built and as-is information, the requirement of data quality and quantity for deep learning methods, the lack of fully automated robotic systems, etc. Afterwards, a status matrix was presented to identify the level of different digital technologies being studied and their future application potential for key activities of underground infrastructure projects. In the end, future research trends are proposed, including (1) digital twinning of underground infrastructure, (2) quality and uncertainty of inspection data, (3) data generation and semi-supervised learning, (4) predictive maintenance, and (5) fully automated robotic systems for inspection and maintenance. This study contributes to the body of knowledge by identifying the challenges and limitations of existing studies through a systematic review, providing a clear view of the achievements and potentials of digital technologies for underground infrastructure, and proposing future research directions to facilitate digital transformation in this area

Vision-based method of automatically detecting construction video highlights by integrating machine tracking and CNN feature extraction

Automatic analysis of construction video footage is beneficial for project management tasks such as productivity analysis and safety control. However, construction videos are usually long in duration and only contain limited useful information to engineers, while the storage of video data from construction projects is challenging. To obtain and store useful video footage systematically and concisely, this research proposes a vision-based method to automatically generate video highlights from construction videos. The proposed approach is validated through two case studies: a gate scenario and an earthmoving scenario. In experiments, the proposed method has achieved 89.2% on precision and 93.3% on recall, which outperforms the feature-based method by 12.7% and 17.2% on precision and recall, respectively. Meanwhile, the proposed method reduces the required digital storage space by 94.6%. The proposed approach offers potential benefits to construction management in terms of significantly reducing video storage space and efficiently indexing construction video footage