• International conference on construction engineering and project management
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• 2022.06a
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• pp.579-586
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• 2022

Modular building is a fast-growing construction method, mainly due to its ability to drastically reduce the amount of time it takes to construct a building and produce higher-quality buildings at a more consistent rate. However, while modular construction is relatively safer than traditional construction methods, workers are still exposed to hazards that lead to injuries and fatalities, and these hazards could be controlled using emerging smart technologies. Currently, limited information is available at the intersection of modular construction, safety risk, and smart safety technologies. This paper aims to investigate what aspects of modular construction are most dangerous for its workers, highlight specific risks in its processes, and propose ways to utilize smart technologies to mitigate these safety risks. Findings from the archival analysis of accident reports in Occupational Safety and Health Administration (OSHA) Fatality and Catastrophe Investigation Summaries indicate that 114 significant injuries were reported between 2002 and 2021, of which 67 were fatalities. About 72% of fatalities occurred during the installation phase, while 57% were caused by crushing and 85% of crash-related incidents were caused by jack failure/slippage. IoT-enabled wearable sensing devices, computer vision, smart safety harness, and Augment and Virtual Reality were identified as potential solutions for mitigating identified safety risks. The present study contributes to knowledge by identifying important safety trends, critical safety risk factors and proposing practical emerging methods for controlling these risks.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.712-720
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• 2022

The use of social media to disseminate news and interact with project stakeholders is increasing over time in the construction industry. Such social media data can be analyzed to get useful insights of the industry such as demands of new housing construction and satisfaction of construction workers. However, there has been a limited attempts to analyze social media data related to the construction industry. The objective of this study is to collect and analyze construction related tweets to understand the overall sentiments of individuals and organizations about the construction industry. The study collected 87,244 tweets from April 6, 2020, to April 13, 2020, which had hashtags relevant to the construction industry. The tweets were then analyzed to evaluate its sentiments polarity (positive or negative) and sentiment intensity or scores (-1 to +1). Descriptive statistics were produced for the tweets and the sentiment scores were visualized in a scatterplot to show the trend of the sentiment scores over time. The results shows that the overall sentiment score of all the tweets was slightly positive (0.0365). Negative tweets were retweeted and marked as favorite by more users on average than the positive ones. More specifically, the tweets with negative sentiments were retweeted by 2,802 users on average compared to the tweets with positive sentiments (247 average retweet count). This study can potentially be expanded in the future to produce a real time indicator of the construction market industry such as the increased availability of construction jobs, improved wage rates, and recession.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.61-68
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• 2007

There has been a lot of research on and release of commercial systems that enable evaluation and visualization of construction methods. These have enabled the selection of good construction plans. However, the process in which engineers build 3D geometry, formulate a schedule and eventually synchronize them is still a time-consuming process. Changing any aspect of the geometry or the schedule and re-linking them is also time-consuming. Therefore, the engineers may compromise on getting the best solution. This paper describes a technique to automate the generation of multiple sets of schedules, quantity takeoffs and 4D visualization from a single 3D model.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.1007-1012
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• 2005

Although the design-build (DB) delivery system has been taking great strides in the world and relevant researches have also been quite abundant, few studies have dealt with its potential impacts on the construction industry as a whole. This research first identified the potential entry barrier factors, which may hinder the market access, based on the theory of industrial economics and characteristics of DB project. Then through a nation-wide questionnaire survey involving 103 construction contractors and engineering consultants, the influences of each factor on company's competitiveness and corporate strategies were scrutinized, and consequently, the evolution of the construction industry was examined. It is found that as opposed to the traditional design-bid-build delivery system, the DB delivery system elevates competitive advantages of large organizations in terms of the financial capability, working experience, human resource, and administrative strength, and among them, the financial strength was concluded to be the most significant force in differentiating corporate strategies and widening the gap of competitiveness between companies. It is inferred if the government extensively adopts DB delivery system, large organizations that already possess the competitive advantage tend to obtain both design and construction abilities, and dominate the DB market. Small and medium sized companies will find little room to maneuver and be forced to become specialty sub-contractors.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.450-457
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• 2022

The construction industry is demanding, dynamic, and complex making it difficult for workers to recognize hazards. The nature of construction tasks exposes workers to several critical risk factors, such as a high rate of exertion and fatigue. Recent studies suggest that fatigue may impact hazard recognition in the construction industry. However, most studies rely on subjective measures when assessing the relationship between physical fatigue and hazard recognition, limiting such studies' efficacy. Thus, this study examined the relationship between physical fatigue and hazard recognition using a controlled experiment. Worker fatigue levels were captured using physiological data and a subjective exertion scale. The findings confirmed that physical exertion plays a significant role in hazard recognition skills (p < 0.05). This research contributes to theory and practice by providing a process for objectively assessing the influence of physical fatigue on worker safety and providing construction professionals with some critical insight needed to improve workplace safety.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.744-751
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• 2022

Construction is among the most dangerous industries with numerous accidents occurring at job sites. Following an accident, an investigation report is issued, containing all of the specifics. Analyzing the text information in construction accident reports can help enhance our understanding of historical data and be utilized for accident prevention. However, the conventional method requires a significant amount of time and effort to read and identify crucial information. The previous studies primarily focused on analyzing related objects and causes of accidents rather than the construction activities. This study aims to extract construction activities taken by workers associated with accidents by presenting an automated framework that adopts a deep learning-based approach and natural language processing (NLP) techniques to automatically classify sentences obtained from previous construction accident reports into predefined categories, namely TRADE (i.e., a construction activity before an accident), EVENT (i.e., an accident), and CONSEQUENCE (i.e., the outcome of an accident). The classification model was developed using Convolutional Neural Network (CNN) showed a robust accuracy of 88.7%, indicating that the proposed model is capable of investigating the occurrence of accidents with minimal manual involvement and sophisticated engineering. Also, this study is expected to support safety assessments and build risk management systems.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.639-640
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• 2015

Traditional construction planning based on historical data and heuristic adjustment can no longer incorporate all the operational details and guarantee the expected performance. The variation between the expected and the actual production leads to cost overruns or delay. Although predicting reliable productivity on construction site is getting more important, the difficulty of this increases. In this regard, this paper suggested to develop BIM-integrated simulation framework. This framework could predict productivity dynamics by considering factors affecting on construction productivity at operational phase. We developed the following processes; 1) enabling a BIM model to produce input data for simulation; 2) developing the construction operation simulation; 3) running simulation using BIM data and obtaining productivity results. The BIM-integrated simulation framework was tested with structural steel erection model because steel erection work is one of the most critical process influencing on the whole construction budget and duration. We could improve to predict more dynamic productivity from this framework, and this reliable productivity helps construction managers to optimize resource allocation, increase schedule reliability, save storage cost, and reduce material loss.

• Construction Engineering and Management
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• v.11 no.3
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• pp.59-60
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• 2010

• Journal of the Korea Construction Safety Engineering Association
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• s.39
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• pp.80-85
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• 2006

• Construction Engineering and Management
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• v.18 no.2
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• pp.31-43
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• 2017