The coronavirus disease 2019 (COVID-19) pandemic has brought unprecedented impacts (e.g., labor shortage, suspension and cancellation of projects, and disrupted supply and logistics) on the US construction industry. To address challenges caused by the pandemic, it is critical for the construction industry to develop a clear understanding of how the pandemic has affected the industry and how it will change in the future. However, assessing the impacts of COVID-19 on the construction industry is challenging due to the broad influence of the pandemic and the dynamic nature of the industry. The Purdue Index for Construction (Pi-C), which was developed as an indicator based on five dimensions and corresponding metrics to measure the health status of the construction industry, offers an opportunity to understand the impact of the pandemic. In this context, this paper presents a study to reveal the relationship between COVID-19 and the health status of the industry as measured through Pi-C and predict the future trend of the construction industry. This study achieves the objective via the three steps. First, the relationship between the pandemic and Pi-C metrics is identified using the Granger causality test and structural equation modeling (SEM) analysis. Second, multivariable prediction models are developed based on a long short-term memory (LSTM) network—a deep learning algorithm—to predict Pi-C metrics in the future. Third, forecasted Pi-C metrics are integrated into the existing Pi-C structure to analyze the impacts of the COVID-19 pandemic and predict its trends in 2021–2022. The results revealed that the impacts of the pandemic were conspicuous in two Pi-C dimensions (economy and stability), whereas no significant impacts were observed in the remaining Pi-C dimension (social). In addition, the Pi-C forecasted that there would be no significant adverse impacts on the US construction industry caused by the pandemic until the end of 2022.

Planned Preventive Maintenance (PPM) and Unplanned Maintenance (UPM) are the most common types of facility maintenance. This paper analyzes current trends and status of Facility Management (FM) practice at higher education institutions by proposing a systematic data-driven methodology using Natural Language Process (NLP) approaches, statistical analysis, risk-profile analysis, and outlier analysis. This study utilizes a descriptive database entitled “Facility Management Unified Classification Database (FMUCD)” to conduct the systematic data-driven analyses. The 5-year data from 2015 to 2019 was collected from eight universities in North America. A preprocessing step included but was not limited to identifying common data attributes, cleaning noisy data, and removing unnecessary data. The outcomes of this study can facilitate the decision-making process by providing an understanding of various aspects of educational facility management trends and risks. The methodology developed gives decision makers of higher education institutions, including facility managers and institution administrators, effective strategies to establish long-term budgetary goals, which will lead to the enhancement of the asset value of the institutions.

Accurately mapping and labeling buried utilities is critical to managing massive urban underground infrastructure and preventing utility incidents. However, current spatial information regarding underground utilities is inconsistent, inaccurate, and uncertain, which is a root cause of numerous utility incidents. Information about underground utilities is usually provided by multiple sources, such as existing records and nondestructive testing technologies [e.g., ground-penetrating radar (GPR)]. A key challenge is the integration of imperfect data obtained from heterogeneous sources to create accurate underground utility maps. Aiming at accurately confirming the presence or absence of underground pipes, this study presents an information fusion method based on Dempster-Shafer (D-S) evidence theory to integrate both sensing and nonsensing data. The GPR and existing utility records serve as two independent information sources that provide evidence regarding the existence and configuration of buried utilities. The trust level of evidence provided by different sources is modeled as mass value, and practical guidance is proposed to quantitatively assign the mass value of individual data sources by considering various factors that affect the accuracy of each data source. By fusing the evidence from heterogeneous sources, the joint mass value is computed, based on which the state of pipes is inferred. Indoor experiments on three different scenarios demonstrate the efficacy of the proposed method. The results show that the proposed fusion approach leads to higher accuracy, precision, and recall in detecting pipes than using an individual data source. Furthermore, the robustness of the proposed mass value selection guidance is validated through sensitivity analysis, namely the fusion result remains stable when the mass value for an individual data source varies slightly.

The construction industry is one of the most significant contributors to the growth of the US economy as well as the global market. The Purdue Index for Construction (Pi-C) was developed in the form of a composite index consisting of five dimensions (Economy, Stability, Social, Development, and Quality) to monitor the health status of the construction industry and facilitate data-driven decision making. Despite its great potential, metrics under the Development and Quality dimensions are still missing, which limits our understanding of the health status of the construction industry. A promising approach to identify the missing metrics is to apply the latent Dirichlet allocation (LDA), which supports the discovery of latent topics from a large set of textual data. In this regard, this work introduces an LDA-based method to identify new metrics for the Development and Quality dimensions of the Pi-C. A total of 10,466 abstracts of research papers relevant to Development and Quality were collected from academic search engines using a web crawler. The LDA analysis was conducted to identify metrics and corresponding variables. As a result, two new metrics—Technology and Education—in the Development dimension and one new metric—Sustainability—in the Quality dimension were identified for Pi-C. Results revealed that the updated Pi-C improves our understanding of the construction industry in terms of technology, education, and sustainability. The updated Pi-C is expected to assist decision makers in data-driven decision-making and strategy development in the construction industry.

Enhancing safety communication within the construction industry is of paramount importance due to its potential in curtailing occupational injuries and improving the overall well-being of construction field workers. While the importance of improved communication is apparent, few studies have been focused on identifying the factors that positively influence communication, particularly in the context of safety. Especially in the case of foreign construction field workers (FCFWs), who often face communication challenges stemming from language and cultural differences, performing labor tasks in harsh and constantly changing environments is contributing significantly to the increasing rate of industrial accidents. Therefore, this study is aimed at investigating the vital factors that impact safety communication among FCFWs. A phenomenological qualitative method was applied to determine the vital factors influencing the safety communication among FCFWs. On applying the analytical hierarchy process, the factors and their importance were determined, and the vital factors were identified. Quantitative assessment through the analytic hierarchy process (AHP) established that extrinsic motivation (weight: 0.513), management communication style (0.264), and visible safety information (0.127) significantly overshadow other factors in safety communication effectiveness, validating their pivotal roles. However, a major limitation is that interview data were collected from workers of only four nationalities. Future studies should address this by expanding the range of nationalities included to enhance the diversity of experiences and perspectives from a broader variety of foreign construction field workers.