Industrialized Construction: Emerging Methods and Technologies

Razkenari, Mohamad; Qi, Bing; Fenner, Andriel Evandro; Hakim, Hamed; Costin, Aaron; Kibert, Charles J.

doi:10.1061/9780784482438.045

Cited by 12 publications

(7 citation statements)

References 9 publications

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“…Our society is currently witnessing the 4th Industrial Revolution, "which is blurring the separation of the physical, cyber, and biological worlds" [29]. Information and communication technologies (ICTs), such as the Internet of Things (IoT), Cyber-Physical-Social Systems (CPSS), Big Data, Artificial Intelligence (AI), and machine learning are starting to be adopted in the construction industry to ultimately increase the efficiency, safety, and quality of construction projects.…”

Section: Future Construction Work: Rise In Automation and Iot Technologymentioning

confidence: 99%

Leading Indicators—A Conceptual IoT-Based Framework to Produce Active Leading Indicators for Construction Safety

Costin

Wehle

Adibfar

2019

Safety

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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.

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Section: Future Construction Work: Rise In Automation and Iot Technologymentioning

confidence: 99%

Leading Indicators—A Conceptual IoT-Based Framework to Produce Active Leading Indicators for Construction Safety

Costin

Wehle

Adibfar

2019

Safety

Self Cite

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“…Despite the growing academic attention, our preliminary study [8] indicated a discrepancy in the needs of emerging technology in industrialized construction between academia and industry. Specifically, the corresponding application in the actual industrialized construction industry is generally at a low level, although more works in academia are focusing on the research of emerging technologies.…”

Section: Introductionmentioning

confidence: 71%

“…This literature is focusing on the discussion of technology itself, without discussing the difference between the use in different countries. Based on the works of previous literature reviewing and the preliminary research conducted by the authors [8], this article classifies the technologies that either have already been applied or have the potential to be applied in the industrialized construction industry into four main areas: (1) business digitalization, (2) computer integrated design and planning, (3) data acquisition and analytics, and (4) robotics and automation. Each of these groups encompasses subclasses of technologies and tools that are necessary to re-engineer the industrialized construction process.…”

Section: Emerging Technology Categorizationmentioning

confidence: 99%

“…The traditional methods include using questionnaire surveys, interviews, or focus groups to collect related data from the industry practitioners. Specifically, our preliminary research distributed a questionnaire survey to collect the practitioners' technology utilization level [8,13]. The questionnaire introduced the concepts and scope of various technology types and then requested the practitioners to rate their utilization level.…”

Section: Introductionmentioning

confidence: 99%

“…These limitations mentioned above might decrease the generalization of the predictive model, and a more efficient and objective method is needed. Our preliminary research [8,13,19] used Analysis of Variance (ANOVA) to find that company background and personal career profiles can significantly affect the practitioners' technology utilization level in the industrialized construction projects. The data about company background and personal career profiles can be easier to access compared to other types of determinants that need to be evaluated subjectively by respondents.…”

Section: Introductionmentioning

confidence: 99%

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A Predictive Analysis on Emerging Technology Utilization in Industrialized Construction in the United States and China

Qian

Costin

2020

Algorithms

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Considering the increasing use of emerging technologies in industrialized construction in recent years, the primary objective of this article is to develop and validate predictive models to predict the emerging technology utilization level of industrialized construction industry practitioners. Our preliminary research results indicate that the company background and personal career profiles can significantly affect practitioners’ technology utilization level. Thus, our prediction model is based on four variables: company size, company type, working experience, and working position. The United States and China are selected as the case studies to validate the prediction model. First, a well-designed questionnaire survey is distributed to the industrialized construction industry practitioners from the two countries, which leads to 81 and 99 valid responses separately. Then, ordinal logistic regression is used to develop a set of models to predict the practitioners’ utilization level of the four main technology types. Finally, the external test dataset consisting of 16 cases indicates the prediction models have a high accuracy. The results also reflect some differences of the technology utilization status in the industrialized construction industry between the United States and China. The major contribution of this research is offering an efficient and accurate method to predict practitioners’ technology utilization level in industrialized construction. Significantly, the models are believed to have a wide application in promoting the emerging technologies in the actual industrialized construction.

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A Double Deep Q-Network-Enabled Two-Layer Adaptive Work Package Scheduling Approach

Zhang

et al. 2023

Proceedings of the 27th International Symposium on Advancement of Construction Management and Real Estate

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Industrialized Construction: Emerging Methods and Technologies

Cited by 12 publications

References 9 publications

Leading Indicators—A Conceptual IoT-Based Framework to Produce Active Leading Indicators for Construction Safety

Leading Indicators—A Conceptual IoT-Based Framework to Produce Active Leading Indicators for Construction Safety

A Predictive Analysis on Emerging Technology Utilization in Industrialized Construction in the United States and China

A Double Deep Q-Network-Enabled Two-Layer Adaptive Work Package Scheduling Approach

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