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Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal. [ABSTRACT FROM AUTHOR]

Man-made disasters are often unexpected events that necessitate a comprehensive investigation to ascertain their cause. These investigations are critical for government agencies, insurance corporations, and other stakeholders. Social media platforms are a rich source of information, but the credibility and vast amount of data available make it challenging to extract useful evidence. Therefore, understanding the credibility of information from different groups of users on social media and its role in retrieving and filtering relevant sources is crucial. To illustrate the potential in this regard, this study examines the Notre Dame fire as a case study, analyzing tweets posted between April 15 and 25, 2019. Using a collection of lexicons, the study establishes a text-parsing and lexicon-based rule model to classify users' opinions regarding the causes of the fire incident. Then the study characterizes the distribution of opinions between verified and nonverified users and investigates the temporal dynamics of reactions from subsets of users commenting on the event. The findings suggest that opinions from verified users were consistent with official reports, which highlights the potential value of the shared knowledge of verified users in the early stages of disaster investigation. The study further suggests that disaster responders should consider opinions from nonverified users, as they may aid investigations by identifying potential causes and providing new directions. In conclusion, this paper explores the potential of utilizing social media data as a means of supporting engineering investigations and emphasizes the importance of developing robust methodologies from crowdsourcing opinions for engineering investigations in the context of man-made disasters. [ABSTRACT FROM AUTHOR]

Women are significantly underrepresented in construction and engineering industry leadership roles despite having comparable qualifications, experience, and degrees. Improvements require a better understanding of the factors leading to the underrepresentation of females in the construction industry. While prior studies report that a male-dominated culture in construction negatively impacts females' career advancement, minimal research explores the role congruity theory and the likeability and competency dilemma among construction leaders. When striving to succeed in traditionally male-dominated fields, women face a unique challenge because they need to defy gender stereotypes created by cultural norms, encapsulated by the likeability versus competency dilemma. Females who accomplish traditionally male tasks and demonstrate independence, assertiveness, self-reliance, and power are no longer seen as likeable. Through analyzing publicly available LinkedIn recommendations, this paper proposed a data-driven approach for examining the likeability and competency dilemma for female construction leaders. Results showed that female leaders in construction were seen as competent in the same way as their male counterparts, but far less likeable (a likeability score of 28% compared to 51% for males). Developing a text mining model to predict the gender of the person receiving a recommendation, this paper also highlights unconscious bias in describing and reacting to leaders' successes. The machine learning model accurately predicted the gender of the person receiving the recommendation with more than 86% accuracy. Despite possessing the sufficient capability to handle traditionally male work successfully, women receive negative judgment from colleagues, creating challenges in their career paths. Finally, the paper contributes to social role theory and role congruity theory by highlighting the differences in gender roles and leadership roles for women in the construction industry. [ABSTRACT FROM AUTHOR]

Project risk is an important part of managing large projects of any sort. This study contributes to the state of knowledge in project risk management by introducing a data-driven approach to measure risk identification performance using historical data. In the early phases of a project, the identification and assessment of risk is based largely on experience and expert judgment. As a project moves through its life cycle, these identified risks and the assessment of them evolve. Some risks become issues, some are mitigated, and some are retired as no longer important. This study investigated the quality of early risk registers and risk assessments on large transportation projects and compared them to how the identified risks evolved on historical projects. The investigation involved the use of textual analysis of archival risk register documents. Finite-state automation methods akin to Markov chain models were used to track the changes in risk attributes on large infrastructure projects as the projects matured. The objective was to be better able to anticipate how project risks will change as projects move forward and to be better able to forecast changes to the risk register from ex ante to ex post conditions. Results from 11 major US transportation projects suggested that, on average, fewer than 65% of ex ante identified risks ultimately occurred in projects and were mitigated, while more than 35% did not occur and were retired. In addition, more than half of the risks emerged during project execution when new information became available. Based on the categorization of risk management styles, we find that identifying risks early in the project life cycle is necessary, but not sufficient to ensure successful project delivery. A project team with positive doer behavior (i.e., actively monitoring and identifying risks during project execution) performed better in delivering projects on time and within budget. [ABSTRACT FROM AUTHOR]

Will women and men follow distinctively different paths to achieve executive engineering leadership positions in the US architecture, engineering, and construction (AEC) industry? Using Engineering News Record's (ENR's) 2019 Top 400 list, this research analyzed LinkedIn profiles for over 2,800 executives to assess career differences between genders. Statistical comparisons of important features, highlighted by number of companies, titles, education, and network size, revealed a significant impact of gender on individual career paths. A key finding was that men ascend to leadership with a single firm throughout their career, outpacing women almost fourfold (37% to 10%). Applying random forest (RF) as an ensemble classifier, researchers successfully predicted profile gender with accuracy of 98.95% for training and 89.53% for testing samples. Collating and categorizing the activities and milestones of individual and collective executives offer insight regarding successful experiences, skills, and choices to reach leadership roles. This creates a roadmap for current and future early and midlevel professionals to model their own vocational journey and accelerate progression up the corporate ladder. From an industry perspective, firms deprive themselves and customers of the proven wide-ranging benefits of diversity. [ABSTRACT FROM AUTHOR]

Risk management is widely recognized as a best practice for public agencies to ensure the successful implementation of major transportation projects. The conventional approach to identify and evaluate project risks is dominated by getting input from subject matter experts at risk workshops. However, the uniqueness of such a risk assessment approach remains unexamined. How different are the risks among various projects? Does the risk register reflect the unique feature of a project? The goal of this study is to measure the similarity of project risks across various groups by evaluating 70 major transportation projects delivered under various methods. The similarity index is calculated at three levels, that is, the entire document of the risk register, individual risk item, and the probability and consequence of each risk using a systematic comparative analysis based on natural language processing (NLP) and a state-of-the-art deep learning algorithm named Word2vec. Our study reports a high similarity of risk registers among different projects at all three levels. The analysis does show a lower similarity of risk registers for public–private partnerships (P3) projects. The primary contributions of this study are (1) develop a new approach to analyze the risk registers at the project level as the main output of risk management practice, and (2) establish the relation of risk uniqueness and project delivery method in transportation projects. Results suggest that a data-driven approach may be possible to help project teams develop a common risk register while allowing the teams to focus on each project's unique risks. [ABSTRACT FROM AUTHOR]

Xu et al. ([10]) developed a rule-based natural language processing (NLP) approach to extracting domain knowledge elements (DKEs) from Chinese text documents in the domain of construction safety management. The approach attempts to solve the problem of construction fire safety management and contributes to guiding project teams in the timely detection of fires, improving safety management efficiency, and reducing fire-related losses. The special collection on Managing Infrastructure in a Data-Rich Era is available in the ASCE Library at https://ascelibrary.org/jmenea/infrastructure data rich era. [Extracted from the article]