Predictive risk modeling for major transportation projects using historical data

Erfani, Abdolmajid; Cui, Qingbin

doi:10.1016/j.autcon.2022.104301

Cited by 18 publications

(4 citation statements)

References 63 publications

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“…A literature review shows that AI is also used for predictive risk identification and risk assessment. In their work, Erfani and Cui (2022) stated that traditional expert-based approaches in the field of risk identification create difficulties in projects due to their timeconsuming and expensive aspects [27]. In order to overcome these constraints, the authors introduced a data-oriented framework that utilizes historical data and artificial intelligence methods, specifically word-embedding models, to identify risks.…”

Section: Literature Review Of Ai Use For Risk Management In Construct...mentioning

confidence: 99%

Assessing the Accuracy of ChatGPT Use for Risk Management in Construction Projects

Aladağ

2023

Sustainability

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Artificial Intelligence (AI) is considered promising digital technology that has important opportunities for enhancing project oversight and delivering improved decision-making in the risk management domain. However, there is a limited amount of research that has evaluated AI tools’ performance in risk management. Therefore, with the intention of sustaining more accurate risk-based decision-making process in the construction industry, this paper investigates the accuracy of ChatGPT in risk management for different project types. In this context, Key Performance Indicators (KPIs) related to each risk management sub-process were determined, and then a questionnaire that consisted of prompt templates was prepared for collecting data from ChatGPT. Afterwards, ChatGPT’s responses were evaluated by experts with focus group sessions. The findings indicate that ChatGPT has a moderate level of performance in managing risks. It provides more accurate knowledge in risk response and risk monitoring rather than risk identification and risk analysis sub-processes. This research paves the way for future studies by demonstrating an implication of ChatGPT use for risk-based decision making. In addition, gaining insight into the precision of ChatGPT in the risk-based decision-making process will empower decision-makers to establish resilience in business operations through technology-driven risk management.

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Section: Literature Review Of Ai Use For Risk Management In Construct...mentioning

confidence: 99%

Assessing the Accuracy of ChatGPT Use for Risk Management in Construction Projects

Aladağ

2023

Sustainability

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“…Nevertheless, statistically significant correlations reflect the probability of such a correlation occurring rather than its strength. Correlation coefficient strengths can be interpreted differently across scientific fields, and authors should avoid overinterpreting associations [ 51 , 66 , 67 ]. Based on prior work utilizing spearman rank correlation in the context of medicine and big data analysis, we have selected a correlation coefficient of 0.3 as the threshold between high and low correlation [ 51 ], or weak and moderate correlation [ 52 ].…”

Section: Limitationsmentioning

confidence: 99%

A fairness assessment of mobility-based COVID-19 case prediction models

Erfani,

Frias-Martinez

2023

PLoS ONE

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In light of the outbreak of COVID-19, analyzing and measuring human mobility has become increasingly important. A wide range of studies have explored spatiotemporal trends over time, examined associations with other variables, evaluated non-pharmacologic interventions (NPIs), and predicted or simulated COVID-19 spread using mobility data. Despite the benefits of publicly available mobility data, a key question remains unanswered: are models using mobility data performing equitably across demographic groups? We hypothesize that bias in the mobility data used to train the predictive models might lead to unfairly less accurate predictions for certain demographic groups. To test our hypothesis, we applied two mobility-based COVID infection prediction models at the county level in the United States using SafeGraph data, and correlated model performance with sociodemographic traits. Findings revealed that there is a systematic bias in models’ performance toward certain demographic characteristics. Specifically, the models tend to favor large, highly educated, wealthy, young, and urban counties. We hypothesize that the mobility data currently used by many predictive models tends to capture less information about older, poorer, less educated and people from rural regions, which in turn negatively impacts the accuracy of the COVID-19 prediction in these areas. Ultimately, this study points to the need of improved data collection and sampling approaches that allow for an accurate representation of the mobility patterns across demographic groups.

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“…As technology advances, building engineering [13,14] and construction [15][16][17] challenges are being addressed in a variety of ways, particularly with regard to project management [18][19][20], scheduling [21,22], and safety concerns [23,24]. Accordingly, emerging technologies such as virtual reality (VR) [25][26][27], augmented reality (AR) [28,29], wearable sensors [23,30,31], drones [32][33][34][35][36], and BIM [37][38][39][40][41][42] have recently gained increasing attention from the AECO industry because it improves the efficiency, productivity, and safety of projects throughout their life cycle.…”

Section: Literature Review and Context Backgroundmentioning

confidence: 99%

Underlying Factors and Strategies for Organizational BIM Capabilities: The Case of Iran

Rajabi

Rezaeiashtiani

Radzi

et al. 2022

ASI

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Building information modeling (BIM) has a significant role in the architecture, engineering, construction, and operation (AECO) industries. Most BIM benefits have not been grasped due to the lack of organizational BIM capabilities (OBIMCs). Accordingly, organizations must develop intuitive strategies to support BIM implementation and to fulfill the promised benefits. This study investigates the impact of different capability factors on OBIMC and the underlying strategies to improve OBIMC in Iran. Particularly, this study builds a structural equation model to explain the links between the capability factors and strategies linked to OBIMC in Iran. A systematic literature review of twenty-six papers and semi-structured interviews with fifteen BIM specialists identified nineteen capability factors and fourteen strategies. A survey of 126 BIM professionals was used to assess the importance of the capability factors and strategies. To analyze the collected data, first, an Exploratory Factor Analysis (EFA) was performed. Then, Partial Least-Squares Structural Equation Modeling (PLS-SEM) was employed. The EFA generated two constructs for the capability factors: OBIMC and organizational capabilities (OCA). Furthermore, it categorized the strategies into two constructs: BIM capability requirement (BIMCR) and organizational culture (OCU). The structural equation model demonstrates that BIMCR and OCU enhance OCA and OBIMC. These two elements are also positively impacted by BIMCR. Industry professionals and policymakers can use these findings to develop strategic plans and to prioritize efforts. The significant contribution of this study is to illuminate the interrelationship between capability factors and strategies related to OBIMC in Iran.

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Predictive risk modeling for major transportation projects using historical data

Cited by 18 publications

References 63 publications

Assessing the Accuracy of ChatGPT Use for Risk Management in Construction Projects

Assessing the Accuracy of ChatGPT Use for Risk Management in Construction Projects

A fairness assessment of mobility-based COVID-19 case prediction models

Underlying Factors and Strategies for Organizational BIM Capabilities: The Case of Iran

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