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The purpose of this paper is to investigate the critical time window for pro-active construction accident prevention and response. Large to small organisations throughout the entire construction supply chain continue to be challenged to adequately prevent accidents. Construction worker injuries and fatalities represent significant waste of resources. Although the five C’s (culture, competency, communication, controls and contractors) have been focusing on compliance, good practices and best-in-class strategies, even industry leaders have only marginal improvements in recorded safety statistics for many years.

Right-time vs real-time construction safety and health identifies three major focus areas to aid in the development of a strategic, as opposed to tactical, response. Occupational safety and health by design, real-time safety and health monitoring and alerts and education, training and feedback leveraging state-of-the-art technology provide meaningful predictive, quantitative and qualitative measures to identify, correlate and eliminate hazards before workers get injured or incidents cause collateral damage.

The current state and development of existing innovative initiatives in the occupational construction safety and health domain are identified. A framework for right-time vs real-time construction safety and health presents the specific focus on automated safety and health data gathering, analysis and reporting to achieve better safety performance. The developed roadmap for right-time vs real-time safety and health is finally tested in selected application scenarios of high concern in the construction industry.

A strategic roadmap to eliminate hazards and accidents through right-time vs real-time automation is presented that has practical as well as social implications on conducting a rigorous safety culture and climate in a construction business and its entire supply chain.

Real-time location sensing (RTLS) systems offer a significant potential to advance the management of construction processes by potentially providing real-time access to the locations of workers and equipment. Many location-sensing technologies tend to perform poorly for indoor work environments and generate large data sets that are somewhat difficult to process in a meaningful way. Unfortunately, little is still known regarding the practical benefits of converting raw worker tracking data into meaningful information about construction project progress, effectively impeding widespread adoption in construction.

The presented framework is designed to automate as many steps as possible, aiming to avoid manual procedures that significantly increase the time between progress estimation updates. The authors apply simple location tracking sensor data that does not require personal handling, to ensure continuous data acquisition. They use a generic and non-site-specific knowledge base (KB) created through domain expert interviews. The sensor data and KB are analyzed in an abductive reasoning framework implemented in Answer Set Programming (extended to support spatial and temporal reasoning), a logic programming paradigm developed within the artificial intelligence domain.

This work demonstrates how abductive reasoning can be applied to automatically generate rich and qualitative information about activities that have been carried out on a construction site. These activities are subsequently used for reasoning about the progress of the construction project. Our framework delivers an upper bound on project progress (“optimistic estimates”) within a practical amount of time, in the order of seconds. The target user group is construction management by providing project planning decision support.

The KB developed for this early-stage research does not encapsulate an exhaustive body of domain expert knowledge. Instead, it consists of excerpts of activities in the analyzed construction site. The KB is developed to be non-site-specific, but it is not validated as the performed experiments were carried out on one single construction site.

The presented work enables automated processing of simple location tracking sensor data, which provides construction management with detailed insight into construction site progress without performing labor-intensive procedures common nowadays.

While automated progress estimation and activity recognition in construction have been studied for some time, the authors approach it differently. Instead of expensive equipment, manually acquired, information-rich sensor data, the authors apply simple data, domain knowledge and a logical reasoning system for which the results are promising.

Accidents resulting from poorly planned or setup work environments are a major concern within the construction industry. While traditional education and training of personnel offer well-known approaches for establishing safe work practices, serious games in virtual reality (VR) are increasingly being used as a complementary approach for active learning experiences. By taking full advantage of data collection and the interactions possible in the virtual environment, the education and training of construction personnel improves by using non-biased feedback and immersion.

This research presents a framework for the generation and automated assessment of VR data. The proposed approach is tested and evaluated in a virtual work environment consisting of multiple hazards. VR requires expensive hardware, technical knowledge and user acceptance to run the games effectively. An effort has been made to transfer the advantages VR gives to a physical setup. This is done using a light detection and ranging sensing system, which collects similar data and enables the same learning experiences.

Encouraging results on the participants’ experiences are presented and discussed based on actual needs in the Danish construction industry. An outlook presents future avenues towards enhancing existing learning methods.

The proposed method will help develop active learning environments, which could lead to safer construction work stations in the future, either through VR or physical simulations.

The utilization of run-time data collection and automatic analysis allows for better personalized feedback in the construction safety training. Furthermore, this study investigates the possibility of transferring the benefits of this system to a physical setup that is easier to use on construction sites without investing in a full VR setup.

Construction activities conducted in urban areas are often a source of significant noise disturbances, which cause psychological and health issues for residents as well as long-term auditory impairments for construction workers. The limited effectiveness of passive noise control measures due to the close proximity of the construction site to surrounding neighborhoods often results in complaints and eventually lawsuits. These can then lead to delays and cost overruns for the construction projects.

The paper proposes a novel approach to integrating construction noise as an additional dimension into scheduling construction works. To achieve this, a building information model, including the three-dimensional construction site layout object geometry, resource allocation and schedule information, is utilized. The developed method explores further project data that are typically available, such as the assigned equipment to a task, its precise location, and the estimated duration of noisy tasks. This results in a noise prediction model by using noise mapping techniques and suggesting less noisy alternative ways of construction. Finally, noise data obtained from sensors in a case study contribute real values for validating the proposed approach, which can be used later to suggest solutions for noise mitigation.

The results of this study indicate that the proposed approach can accurately predict construction noise given a few available parameters from digital project planning and sensors installed on a construction site. Proactively integrating construction noise control measures into the planning process has benefits for both residents and construction managers, as it reduces construction noise-related disturbances, prevents unexpected legal issues and ensures the health and well-being of the workforce.

While previous research has concentrated on real-time data collection using sensors, a more effective solution would also involve addressing and mitigating construction noise during the pre-construction work planning phase.

Construction is a risky industry. Therefore, organizations are seeking ways towards improving their safety performance. Among these, the integration of technology into health and safety leads to enhanced safety performance. Considering the benefits observed in using technology in safety, this study aims to explore digital technologies' use and potential benefits in construction health and safety.

An extensive bibliometrics analysis was conducted to reveal which technologies are at the forefront of others and how these technologies are used in safety operations. The study used two different databases, Web of Science (WoS) and Scopus, to scan the literature in a systemic way.

The systemic analysis of several studies showed that the digital technologies use in construction are still a niche theme and need more assessment. The study provided that sensors and wireless technology are of utmost importance in terms of construction safety. Moreover, the study revealed that artificial intelligence, machine learning, building information modeling (BIM), sensors and wireless technologies are trending technologies compared to unmanned aerial vehicles, serious games and the Internet of things. On the other hand, the study provided that the technologies are even more effective with integrated use like in the case of BIM and sensors or unmanned aerial vehicles. It was observed that the use of these technologies varies with respect to studies conducted in different countries. The study further revealed that the studies conducted on this topic are mostly published in some selected journals and international collaboration efforts in terms of researching the topic have been observed.

This study provides an extensive analysis of WoS and Scopus databases and an in-depth review of the use of digital technologies in construction safety. The review consists of the most recent studies showing the benefits of using such technologies and showing the usage on a systemic level from which both scientists and practitioners can benefit to devise new strategies in technology usage.

The purpose of this paper was to map the safety management research of construction industry by scientometric analysis, which can predict important highlights and future research directions of safety management research in the construction industry. As an important issue in the construction industry, safety management issues have been researched from different perspectives. Although previous studies make knowledge contributions to the safety management research of construction industry, there are still huge obstacles to distinguish the comprehensive knowledge map of safety management research in the construction industry.

This study applies three scientometric analysis methods, collaboration network analysis, co-occurrence network analysis and cocitation network analysis, to the safety management research of construction industry. 5,406 articles were retrieved from the core collection database of the Web of Science. CiteSpace was used for constructing a comprehensive analysis framework to analyze and visualize the safety management research of construction industry. According to integrating the analysis results, a knowledge map for the safety management research of construction industry can be constructed.

The analysis results revealed the academic communities, key research topics and knowledge body of safety management research in the construction industry. The evolution paths of safety management research in the construction industry were divided into three development stages: “construction safety management”, “multi-objective safety management” and “comprehensive safety management”. Five research directions were predicted on the future safety management research of construction industry, including (1) comprehensive assessment indicators system; (2) intelligent safety management; (3) cross-organization collaboration of safety management; (4) multilevel safety behavior perception and (5) comparative analysis of safety climate.

The findings can reveal the overall status of safety management research in the construction industry and represent a high-quality knowledge body of safety management research in the construction industry that accurately reflects the comprehensive knowledge map on the safety management research of construction industry. The findings also predict important highlights and future research directions of safety management research in the construction industry, which will help researchers in the safety management research of construction industry for future collaboration and work.

Construction safety management (CSM) has been intensively researched in the last four decades but hitherto mostly aimed at understanding root causes of accidents, recommending preventive measures and evaluating their implications. However, a systematic effort to present a comprehensive picture of construction safety research is hardly witnessed. Therefore, the study aims to investigate construction safety research contributors, ontologies, themes, evolution, emerging trends and future directions using quantitative and qualitative content analysis.

A total of 877 journal articles were extracted using preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines and Scopus literature database and were analyzed using VOSviewer and Nvivo tools to present a comprehensive picture of the CSM body of knowledge.

The study observed rapid growth in construction safety research with contributions from various countries, organizations and researchers. This study identified 3 research levels, 8 project phases, 10 project types, 6 research instruments and 19 research data sources along with their usage in the research domain. Further, the study identified 13 emerging research themes, 4 emerging research trends and an observed paradigm shift from reactive to proactive CSM approach.

The comprehensive study on the emerging themes and findings on proactive CSM has strategic implications to practice to incorporate safety. The identified future directions can assist researchers in bridging the existing gaps and strengthening emerging research trends.

The study presents a comprehensive picture of the CSM body of knowledge using the content analysis approach that was absent in past literature and opened future research avenues.

Sensing- and warning-based technologies are widely used in the construction industry for occupational health and safety (OHS) monitoring and management. A comprehensive understanding of the different types and specific research topics related to the application of sensing- and warning-based technologies is essential to improve OHS in the construction industry. The purpose of this paper is to examine the current trends, different types and research topics related to the applications of sensing- and warning-based technology for improving OHS through the analysis of articles published between 1996 and 2017 (years inclusive).

A standardized three-step screening and data extraction method was used. A total of 87 articles met the inclusion criteria.

The annual publication trends and relative contributions of individual journals were discussed. Additionally, this review discusses the current trends of different types of sensing- and warning-based technology applications for improving OHS in the industry, six relevant research topics, four major research gaps and future research directions.

Overall, this review may serve as a spur for researchers and practitioners to extend sensing- and warning-based technology applications to improve OHS in the construction industry.

Many companies invest in innovations because of the inherent benefits, and research on innovation has increased over the year. However, the vast majority of research papers deals with purely technical matters. There seem to be growing concerns over ethical issues in adopting innovations in the construction industry. This extant review of literature aims to analyse the interrelations between the concepts of ethics and innovations in construction research to understand the advances of current scientific production and future lines of research.

Thus, this work presents a bibliometric analysis covering articles obtained from the Web of Science Core Collection Database published between 1995 and May 2021. A sample size of 5,786 research papers relevant to the study was evaluated using VOSviewer software.

The results of the analysis shed light on the evolution of the connection between the two concepts. The study highlighted Heng Li as the most productive author. The country with the most publications and citations is China. The most productive institution is the Hong Kong Polytechnic University. The results revealed a limited intellectual exchange and lack of cohesion characterising the two concepts (ethics and innovation), resulting in a situation whereby innovation-related researchers tend to follow personal trajectories in isolation from ethics-related researchers in the construction field.

This is probably the most comprehensive scientometric analysis ever conducted to examine the theoretical relationship between ethics and innovation in construction. This study adds to the so far limited knowledge in the field and provides insights for future research. Overall, this review may spur future research on dyad investigation of ethics and innovative related themes in construction such as ethics and sustainability, ethics and sensor-based technology and ethics and innovative safety approach.

This paper aims to report upon the further development of a hybrid application programming interface (API) plug-in to building information modelling (BIM) entitled confined spaces safety monitoring system “CoSMoS”. Originally designed to engineer-out environmental hazards associated with working in a building’s confined spaces (during the construction phase of a building’s life-cycle), this second generation version is expanded upon to use archival records to proactively learn from data generated within a sensor network during the building’s operations and maintenance (O&M) phase of asset management (AM).

An applied research methodological approach adopted used a two-phase process. In phase one, a conceptual model was created to provide a “blueprint map” to integrate BIM, sensor-based networks and data analytics (DA) into one integral system. A literature review provided the basis for the conceptual model’s further development. In phase two, the conceptual model was transposed into the prototype’s development environment as a proof of concept using primary data accrued from a large educational building.

An amalgamation of BIM, historical sensor data accrued and the application of DA demonstrate that CoSMoS provides an opportunity for the facilities management (FM) team to monitor pertinent environmental conditions and human behaviour within buildings that may impact upon occupant/worker safety. Although working in confined spaces is used to demonstrate the inherent potential of CoSMoS, the system could readily be expanded to analyse sensor-based network’s historical data of other areas of building performance, maintenance and safety.

This novel prototype has automated safety applications for FM during the asset lifecycle and maintenance phase of a building’s O&M phase of AM. Future work is proposed in several key areas, namely, develop instantaneous indicators of current safety performance within a building; and develop lead indicators of future safety performance of buildings.