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Smart construction is gaining attention as a solution to improve the construction industry's productivity and safety problems. Although there have been many actions to activate the utilization of smart construction technologies, their effectiveness remains unclear. Diagnosing the current status of the smart construction industry is fundamental, eventually establishing a diagnosis-prescription-improvement virtuous cycle. However, previous attempts for such diagnosis were limited to the confined scope (e.g., major contractors only) and the subjective judgment of technological capacity, primarily caused by the lack of comprehensive background data. Therefore, this study aims to investigate the current status of smart construction in Korea. Specifically, the scope of smart construction technologies was defined. This study then investigated diverse aspects of smart construction, including market trends, technological maturities, research and developments, and site applications. Based on the results, the smart construction technologies were classified into three categories: 1) similar to the global trends; 2) lacking; and 3) leading in the domestic markets. The directions for future studies were also suggested to develop a performance evaluation indicator for smart construction to understand trends and capacities in time. The study results will provide policymakers and practitioners with background information to establish strategies for advancing smart construction in Korea. [ABSTRACT FROM AUTHOR]

Over the past decade, the frequency of building disasters has been concerning. However, search and rescue operations often encounter challenges due to limited information, resulting in delays or inadequate assistance for occupants trapped inside the building. The importance of obtaining real-time occupancy information has been recognized and studied in the field of people counting. While various sensor studies have been proposed, using camera sensors to accurately count occupants in a building has been attempted. Nevertheless, even with highly accurate camera sensors, counting errors (i.e., miscounting) are unavoidable due to factors such as suboptimal accuracy and occlusion issues in single-camera setups as well as the miscounts occurred by the blind spots between multiple cameras. To overcome these limitations, this paper introduces a novel people counting method called the "Queue-Buffer Algorithm", a multi-camera-based error-correction algorithm for real-time people counting that leverages contextual data to correct miscounts. By implementing this multi-camera people counting approach, the accuracy and efficiency of search and rescue operations can be significantly improved, leading to enhanced outcomes in terms of lives saved and timely evacuation. [ABSTRACT FROM AUTHOR]

The construction industry is introducing autonomous heavy equipment to overcome labor shortages and improve productivity. For autonomous heavy equipment to work on earthmoving at sites, the equipment needs to recognize and understand ground surface types. However, the ground surface types are manually inspected in practice, and related studies are lacking. To address this issue, the authors developed and tested models that automatically classify ground surface types from images acquired by an unmanned aerial vehicle using a deep learning-based multi-label classification method that applies Binary Relevance (BR) and Label Powerset (LP) methods with Residual Neural Network (ResNet) and Vision Transformer classification network (VIT). The model performances were comparatively evaluated through experiments conducted on actual construction sites. The results showed that the BR model with ResNet is the best model in terms of automated ground surface type identification during earthmoving. The results are expected to broaden the understanding of complex and expansive construction sites for autonomous vehicles and thus facilitate deployment of autonomous heavy equipment by helping them to understand working areas and any obstacles on construction sites quickly and effectively, which will reduce the cost and time needed for on-site ground surface management. [ABSTRACT FROM AUTHOR]

The primary purpose of this study is to develop a work process for capital projects that can help the practitioners in developing a facility design standardization program. The goals of this paper are three-fold; 1) to identify and establish key work process tasks for facility standardization, 2) to evaluate the value and difficulty of implementation per each key task, and 3) to identify key parties/players responsible for implementing the identified tasks as well as timings of implementation. The study identifies 41 key tasks for the facility design standardization work process in capital projects. Also, the results show Define Scope and Standardization Approach (#10) and Validate Design Criteria to Standardization Design Envelope (#35) are the most valued tasks and Develop a Standardization Process and Work Process (#7) is the most difficult task to achieve. The results also show that the program lead/manager and project manager are the key responsible players for implementing the key tasks. Finally, the owner is the most accountable (85%), and most of the tasks (50%) must be performed early. The research method relies on a survey, extensive literature review, considerable industry experience of the participants and discussions. This study contributes greatly to facility design standardization strategy in capital projects and serves as a foundation for future research. [ABSTRACT FROM AUTHOR]

Internal communication is pivotal to a company's performance during construction projects. Although internal communication is known to be strongly affected by the type of business, it is still unclear which influence factors have a greater impact. This research aimed to investigate the causal relationship between internal communication effectiveness and company performance for the different business types within the construction industry, namely owners, contractors, and architects/engineers. Structural equation modeling was applied to analyze the influence factors of 52 measurement variables and 11 latent variables. Based on the results of 317 completed surveys collected from 15 construction-related firms, the authors successfully investigated crucial factors in company performance according to the different business types. The results showed that vertical information flows are more influential in organizational performance than horizontal information flows due to hierarchical organizational characteristics common to most construction-related firms. The results assist managers in evaluating their internal communication based on the company's business type and developing organizational strategies to mitigate any communication-related issues. This research contributes to the construction industry's body of knowledge by demonstrating the influence factors crucial to internal communication effectiveness for different business types. [ABSTRACT FROM AUTHOR]

Due to limited budgets and professional manpower, predicting possible damage in advance is essential for supporting on-site bridge inspections. This study aims to predict the severity of damage on bridge decks considering the effects of traffic and weather. First, the authors obtained identification, structural, environmental, and inspection data of pre-stressed concrete I-type bridges from the Korean Bridge Management System, and the final dataset of 16,728 tuples and 53 variables was prepared. Next, correlation analysis was performed to remove redundant variables, and random forest identified important factors that caused the more serious condition of damage to the deck. A total of 32 variables were finally used to develop Deep Neural Networks to predict different types of deck damage. The developed model successfully predicted the occurrences of seven different types of damage to bridge decks, that is, linear cracking, map cracking, scaling, breakage, leakage, efflorescence, and corrosion of exposed rebar, with the average weighted F1 score of 91%. Environmental effects on prediction were also determined; for example, traffic, temperature, and precipitation increased the F1 score of linear cracking by 4%. This research was a pioneering attempt to develop a model that enables specific damage-level prediction using both statistics and artificial intelligence techniques. [ABSTRACT FROM AUTHOR]

Location data of construction resources are important in understanding on the context of a construction site, yet most sites still rely on people's observations to localize their resources. Among then various localization technologies, radio frequency identification (RFID) is considered as a good solution. However, RFID either provides limited location data when fixed receivers are used, or it requires considerable manpower for scanning the tagged resources when hand-held receivers are used. These requirements result in inefficiency and impractical demands on time and cost, particularly in the case of complex or large-scale sites. This study attempted to overcome the limitations by proposing an integrated unmanned aerial vehicle-RFID (UAV-RFID) platform to replace the considerable manpower with the UAV and to enable identifying tags on a site. It applies deep learning algorithms to localize an RFID tag position within an acceptable range of accuracy, thereby demonstrating the feasibility of the integrated platform for construction resource localization. [ABSTRACT FROM AUTHOR]

Acquiring timely and proper information of host countries is a crucial element to lead successful and lucrative delivery of international construction projects. This information, however, commonly exists in forms of unstructured text data such as news articles and reports, which calls for the need of text mining. The aim of this research is to develop a prototype of construction document management system for global contract, which provides the user-needed information in a timely manner. The system named UNI (User Needed Information)-Tacit collects text data containing the recent information of the global construction market by using the web crawling algorithm, automatically allocates tags for each document with the most representative keywords based on Natural Language Processing, and eventually visualizes the results in the form of word clouds. The developed system, the survey validated its usefulness, is expected to contribute to collecting and organizing the latest issues on the global construction market, which provides better understanding of the target countries for decision makers. [ABSTRACT FROM AUTHOR]

Large-scale urban construction comprised of multiple construction projects of different types poses a challenge to progress management. While the progress of a construction project can be measured as the percentage of a facility’s physical completion, this standard is less reliable an indicator in gauging the progress of an urban construction project, as the function of urban facilities determines their role in the completion of an urban system. This paper suggests a new measurement framework consisting of five hierarchically structured models in order to better assess construction progress for large-scale urban projects. To test the validity of the proposed framework, it has been applied to a real-life urban development project carried out in Sejong Special Self-Governing City, South Korea. The weight set on functional importance was quantified according to the analytic hierarchy process method. This study also conducted a survey of 23 practitioners who managed the case project. The contribution of this research is to propose a framework that redefines the progress of urban construction projects by focusing on functional importance in urban systems and formulating a qualitative method of calculating progress for urban construction projects. Such achievements are expected to provide useful information to project managers concerning the extent to which an urban construction project has progressed and the extent to which the progress of an individual project affects the progress of an urban project as a whole. [ABSTRACT FROM AUTHOR]

This paper presents an analysis method that quantifies the relationship between project management practices and the cost performance of construction projects. The analysis method includes a series of analysis procedures and methods: data measurement and transformation, contingency table analysis, logistic regression, and a composite index. Step-by-step analysis procedures and methods are described along with the data obtained from 143 owner projects to examine the effect of nineteen project management practices on cost performance. The analysis method uncovers the minimum implementation levels of project management practices for better cost performance, and quantifies the combined effects of project management practices on cost performance varied by their implementation levels. The analysis results generate specific and quantitative information used in implementing project management practices for better cost performance. This information will help practitioners achieve improved cost performance in the most effective and efficient way. The analysis method designed with a series of statistical methods makes it possible to produce useful quantitative information for the implementation of project management practices. [ABSTRACT FROM AUTHOR]

In recent years, the Texas Department of Transportation (TxDOT) has been facing a shortage of Construction Engineering (CE) staff due to workforce challenges such as staff turnover, retirement of experienced staff, and hiring freezes. The primary purpose of this study is to investigate CE staff needs for TxDOT construction projects. To accomplish this, projects constructed during the 10-year period of 2001-2011 were analyzed. The results indicated that large and metro projects tend to require more CE staff hours compared to smaller and/or rural projects. In particular, capacity-adding projects such as interchanges and widening projects require up to 4.45 times the CE hours needed for pavement projects. This study's findings provide a basis for estimating TxDOT's CE staffing, and ultimately contribute to efficiency improvements by providing an appropriate level of CE staff based on project type, size, and location. [ABSTRACT FROM AUTHOR]

Strategic negotiation of subcontracts appears to contain many benefits regarding cost savings, quality improvement, better project time management, and project value enhancement. The primary purpose of the study presented in this paper is to investigate the current and prospective concepts of negotiable subcontract conditions in the Australian construction industry that can alleviate elongated negotiations and attain a win/win result for both contractor and subcontractor. The study first discussed legislative regulations and opinions concerning subcontract negotiation through extensive review on academic literature and Australian Standards including AS 4903 General conditions of subcontract for design and construct and AS 4300 General conditions of contract for design and construct. The study was further developed in the form of a survey, collecting data from 71 respondents (i.e., contractors and subcontractors) from the Queensland, New South Wales and Northern Territory construction industries in Australia. Cronbach's alpha assessed the reliability of survey responses. The research then statistically analyzed not only critical negotiation factors contributing to project performance based on Fisher's exact test but also significant differences between contractors' and subcontractors' viewpoints to subcontract negotiation by ANOVA testing. The survey results revealed that contractors and subcontractors attain different opinions regarding the fairness of subcontract conditions; however, both parties believed that there were genuine opportunities to improve project performance and value via the negotiation of subcontract conditions using an integrative approach. [ABSTRACT FROM AUTHOR]