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.

Every year power outages affect millions of people. In the US, historical data show that between 2000 and 2016, 75% of outages (in terms of duration) were caused by severe weather events. The National Association of Regulatory Commissioners has recently emphasized the importance of building electricity sector resilience in order to ensure long-term reliability and economic benefits for stakeholders. This study established a disaster impact assessment model to estimate economic losses due to severe weather–induced power outages in the US in terms of the nation’s gross domestic product (GDP). Economic losses were estimated the using extended version of Leontief’s input–output model with historical data from the US Bureau of Economic Analysis between 1997 and 2019. The study showed an estimated GDP loss of $11.6 billion (in 2019 values) due to 1% inoperability in the utility sector. These results can be used to (1) provide a range of investment and (2) justify the need for investment in long-term resilience planning in the utility sector. Furthermore, the results can be used to identify industries that are vulnerable due to inoperability in the utility sector.

This research proposes a data-driven approach to identify possible disparities in a utility’s outage management practices. The approach has been illustrated for an Investor-Owned Utility located in the Midwest region in the U.S. Power outage data for approximately 5 years between March 2017 and January 2022 was collected for 36 ZIP/postal codes located within the utility’s service territory. The collected data was used to calculate the total number of outages, customers affected, and the duration of outages during those 5 years for each ZIP code. Next, each variable was normalized with respect to the population density of the ZIP code. After normalizing, a K-means clustering algorithm was implemented that created five clusters out of those 36 ZIP codes. The difference in the outage parameters was found to be statistically significant. This indicated differential experience with power outages in different ZIP codes. Next, three Generalized Linear Models were developed to test if the presence of critical facilities such as hospitals, 911 centers, and fire stations, as socioeconomic and demographic characteristics of the ZIP codes, can explain their differential experience with the power outage. It was found that the annual duration of outages is lower in the ZIP codes where critical facilities are located. On the other hand, ZIP codes with lower median household income have experienced more power outages, i.e., higher outage counts in those 5 years. Lastly, the ZIP codes with a higher percentage of the White population have experienced more severe outages that have affected more customers.

The Purdue Index for Construction (Pi-C) was developed to gauge the health of the construction industry. It is a composite index consisting of five dimensions: economic, stability, social, development, and quality. This research conducts a data-driven analysis to provide prediction and time-series forecasting models for Pi-C to (1) monitor; and (2) provide guidance on how to improve the future health trajectory for the US construction industry. The seasonal autoregressive integrated moving average (SARIMA) technique is applied for the future trend analysis; multiple linear regression (MLR) and random forests (RF) are applied for prediction models of Pi-C data analytics. It is expected that the proposed prediction and time-series forecasting models will help decision-makers, including policy developers and construction practitioners, to take necessary action in a timely manner, as well as open the discourse on the advanced application of analytics and data-driven decision-making in the construction industry.

In February 2021, Texas was hit by a severe winter storm. The extreme cold weather triggered an unprecedented increase of electricity demand and simultaneous failure of multiple electricity grid components. The dual effect caused a widespread power outage that left millions of Texans without power for days. This paper estimates the indirect economy-wide cascading impact of that power outage. It utilized historical make and use tables between 1997 and 2019 in an inoperability input-output analysis–based economic impact assessment model to quantify the expected indirect cost of the power outage in terms of the loss of gross domestic product of Texas. This research found that expected loss to be approximately $664 million in 2019 values. It also found that in Texas industries such as oil and gas extraction, petroleum, coal products, and so on are particularly vulnerable to such weather-related prolonged power outages. The outcomes of this paper can be used in planning for strategies to make the grid robust, reliable, and resilient to severe weather events. The identification of vulnerable industries can also help in planning for auxiliary capacities so the cascading economic impacts of the weather-related power outages can be reduced.