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Abbasi, S, Taghizade, K and Noorzai, E (2020) BIM-Based Combination of Takt Time and Discrete Event Simulation for Implementing Just in Time in Construction Scheduling under Constraints. Journal of Construction Engineering and Management, 146(12).
Assaad, R, El-adaway, I H, El Hakea, A H, Parker, M J, Henderson, T I, Salvo, C R and Ahmed, M O (2020) Contractual Perspective for BIM Utilization in US Construction Projects. Journal of Construction Engineering and Management, 146(12).
Assaad, R, El-adaway, I H, Hastak, M and Needy, K L (2020) Commercial and Legal Considerations of Offsite Construction Projects and their Hybrid Transactions. Journal of Construction Engineering and Management, 146(12).
Bangaru, S S, Wang, C, Zhou, X, Jeon, H W and Li, Y (2020) Gesture Recognition–Based Smart Training Assistant System for Construction Worker Earplug-Wearing Training. Journal of Construction Engineering and Management, 146(12).
Chang, S, Castro-Lacouture, D and Yamagata, Y (2020) Estimating Building Electricity Performance Gaps with Internet of Things Data Using Bayesian Multilevel Additive Modeling. Journal of Construction Engineering and Management, 146(12).
Erol, H, Dikmen, I, Atasoy, G and Birgonul, M T (2020) Exploring the Relationship between Complexity and Risk in Megaconstruction Projects. Journal of Construction Engineering and Management, 146(12).
Jallan, Y and Ashuri, B (2020) Text Mining of the Securities and Exchange Commission Financial Filings of Publicly Traded Construction Firms Using Deep Learning to Identify and Assess Risk. Journal of Construction Engineering and Management, 146(12).
- Type: Journal Article
- Keywords:
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001932
- Abstract:
Risk factor identification is a critical topic in the construction industry. It is vital for the various construction firms and industry stakeholders to understand the different types of risks that affect their businesses and financial bottom lines. This research created a systematic methodology implementing a new set of text mining methods to identify and classify risk types affecting the publicly traded construction companies, by leveraging their 10-K reports filed with the Securities and Exchange Commission (SEC). A structured procedure was developed to apply advancements from text mining and natural language processing (NLP) to extract information from textual disclosures. A state-of-the-art deep learning algorithm named FastText was implemented to identify risk patterns and classify the text into appropriate risk types. Key findings showed that operational and financial risks associated with doing business most commonly are disclosed in the risk disclosures filed by the publicly traded construction firms. A steady monotonic increase was found in the average number of total risk disclosures from 2006 to 2018. Over the same period, growth occurred in the proportion of technology risks, reputation/intangible assets risks, financial markets risk, and third-party risks. The primary contributions of this research are (1) the development of a new methodology which serves as a risk thermometer for identification and quantification of risk at an individual company level, subindustry level, and the overall industry level; and (2) minimization of any existing information asymmetry in risk studies by utilization of a source of data that previously has not been used by construction researchers. It is anticipated that the developed methodology and its results can be used by (1) publicly traded construction companies to understand risks affecting themselves and their peers; and (2) surety bond companies and insurance providers to supplement their risk pricing models; and (3) equity investors and capital financial institutions to make more-informed risk-based decisions for their investments in the construction business.
Kong, F and Dou, D (2020) RCPSP with Combined Precedence Relations and Resource Calendars. Journal of Construction Engineering and Management, 146(12).
Li, H, Luo, X and Skitmore, M (2020) Intelligent Hoisting with Car-Like Mobile Robots. Journal of Construction Engineering and Management, 146(12).
Loosemore, M, Sunindijo, R Y and Zhang, S (2020) Comparative Analysis of Safety Climate in the Chinese, Australian, and Indonesian Construction Industries. Journal of Construction Engineering and Management, 146(12).
Mahdavian, A and Shojaei, A (2020) Hybrid Genetic Algorithm and Constraint-Based Simulation Framework for Building Construction Project Planning and Control. Journal of Construction Engineering and Management, 146(12).
Nguyen, P H D, Tran, D Q and Lines, B C (2020) Empirical Inference System for Highway Project Delivery Selection Using Fuzzy Pattern Recognition. Journal of Construction Engineering and Management, 146(12).
Roupé, M, Johansson, M, Maftei, L, Lundstedt, R and Viklund-Tallgren, M (2020) Virtual Collaborative Design Environment: Supporting Seamless Integration of Multitouch Table and Immersive VR. Journal of Construction Engineering and Management, 146(12).
Sarihi, M, Shahhosseini, V and Banki, M T (2020) Multiskilled Project Management Workforce Assignment across Multiple Projects Regarding Competency. Journal of Construction Engineering and Management, 146(12).
Shi, J, Liu, B, Tan, J, Dai, J, Chen, J and Ji, R (2020) Experimental Studies and Microstructure Analysis for Rapid-Hardening Cement Emulsified Asphalt Mortar. Journal of Construction Engineering and Management, 146(12).
Signor, R, Love, P E D, Marchiori, F F and Felisberto, A D (2020) Underpricing in Social Infrastructure Projects: Combating the Institutionalization of the Winner’s Curse. Journal of Construction Engineering and Management, 146(12).
Wang, D, Wang, Y and Lu, Y (2020) Impact of Regulatory Focus on Uncertainty in Megaprojects: Mediating Role of Trust and Control. Journal of Construction Engineering and Management, 146(12).
Wu, W, Sandoval, A, Gunji, V, Ayer, S K, London, J, Perry, L, Patil, K and Smith, K (2020) Comparing Traditional and Mixed Reality-Facilitated Apprenticeship Learning in a Wood-Frame Construction Lab. Journal of Construction Engineering and Management, 146(12).
Xu, X, Chen, K and Cai, H (2020) Automating Utility Permitting within Highway Right-of-Way via a Generic UML/OCL Model and Natural Language Processing. Journal of Construction Engineering and Management, 146(12).
Yin, X, Bouferguene, A and Al-Hussein, M (2020) Data-Driven Sewer Pipe Data Random Generation and Validation. Journal of Construction Engineering and Management, 146(12).