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Akin, F D, Damci, A and Arditi, D (2024) Optimum finance-based scheduling. Journal of Construction Engineering and Management, 150(09).

Assaad, R H, Omran, A, Soliman, N and Assaf, G (2024) Prediction of the lateral pressure of self-consolidating concrete on construction formwork systems using machine-learning algorithms. Journal of Construction Engineering and Management, 150(09).

Assaf, G and Assaad, R H (2024) Analyzing the critical success factors affecting project bundling performance of infrastructure projects. Journal of Construction Engineering and Management, 150(09).

Chammout, B, El-Adaway, I H, Abdul Nabi, M and Assaad, R H (2024) Price escalation in construction projects: Examining national and international contracts. Journal of Construction Engineering and Management, 150(09).

Dorignon, L, Oswald, D, Kempton, L, Boehme, T, Iyer-Raniga, U, Moore, T and Dalton, T (2024) Investigating residential building materials in a circular economy: An Australian perspective. Journal of Construction Engineering and Management, 150(09).

Görsch, C, Seppänen, O, Peltokorpi, A and Lavikka, R (2024) Unlocking productivity: Revealing waste and hidden disturbances impacting mep workers. Journal of Construction Engineering and Management, 150(09).

Gao, M Y, Han, J, Yang, Y, Tiong, R L K, Zhao, C and Han, C (2024) BIM-based and IoT-driven smart tracking for precast construction dynamic scheduling. Journal of Construction Engineering and Management, 150(09).

Jiang, Y, Yang, G, Li, H, Zhang, T and Khudhair, A (2024) Physics-informed knowledge-driven decision-making framework for holistic bridge maintenance. Journal of Construction Engineering and Management, 150(09).

Koo, H J, Kelly, D and Deman, D (2024) Risk assessment of the challenges in the adaptive reuse of historic buildings. Journal of Construction Engineering and Management, 150(09).

Namian, M, Nabil, F R, Al-Mhdawi, M K S, Kermanshachi, S S and Nnaji, C (2024) Postpandemic era: Investigating the impact of COVID-19 on construction workers' situational awareness. Journal of Construction Engineering and Management, 150(09).

Ning, X, Yang, Y, Liu, C and Han, Y (2024) Construction workers' unsafe behavior contagion under government-contractor dual influence. Journal of Construction Engineering and Management, 150(09).

Olugboyega, O (2024) Diverse forms of greed and self-interest that contribute to corruption among construction stakeholders. Journal of Construction Engineering and Management, 150(09).

Ouyang, Y and Luo, X (2024) Effects of physical fatigue on construction workers' visual search patterns during hazard identification. Journal of Construction Engineering and Management, 150(09).

Paneru, S, Suh, S, Seo, W and Rausch, C (2024) Evaluating the decarbonization potential of industrialized construction: A review of the current state, opportunities, and challenges. Journal of Construction Engineering and Management, 150(09).

Sadoughi, A, Kouhirostami, M, Kouhirostamkolaei, M, Qi, B and Costin, A (2024) Autonomous building design for manufacturing and assembly: A systematic review of design application, challenges, and opportunities. Journal of Construction Engineering and Management, 150(09).

Shahedi, F, Etemadfard, H, Omrani, F and Ghalehnovi, M (2024) Cost performance modeling for steel fabrication shops with machine learning algorithms. Journal of Construction Engineering and Management, 150(09).

Shuang, Q, Liu, X, Wang, Z and Xu, X (2024) Automatically categorizing construction accident narratives using the deep-learning model with a class-imbalance treatment technique. Journal of Construction Engineering and Management, 150(09).

  • Type: Journal Article
  • Keywords: class imbalance; deep learning; natural language processing; text classification; text embedding
  • ISBN/ISSN: 0733-9364
  • URL: http://doi.org/10.1061/JCEMD4.COENG-14515
  • Abstract:
    Learning from prior incidents is crucial for improving safety, particularly in the construction industry where fatalities and injuries are frequent. High-precision classification of construction accident narratives is a laborious, time-consuming process that requires substantial domain expertise. However, automatic text classification had fallen short of expectations due to a lack of high-quality data sets, inadequate semantic interpretation, and primitive model architecture. To address these issues, this study developed a state-of-the-art text classification (TC) model to extract construction knowledge and classify construction accident narratives into predefined categories. The architecture of the TC deep-learning model was built based on the pretrained instruction-based omnifarious representations (INSTRUCTOR). A class-imbalance treatment (CIT) technique incorporating focal loss and weighted random sampling was embedded to make the model concentrate on hard samples and minority classes. The retrained and fine-tuned INSTRUCTOR-CIT model achieved an F1 score of 82.22% for the benchmark data set containing 1,000 accident narratives from the Occupational Health and Safety Administration (OSHA). Impressively, on a larger benchmark data set of 4,770 OSHA accident narratives labeled by another official system, the model achieved an F1 score of 94.84%, highlighting its generality. Furthermore, the experimental results demonstrated that our model was superior to existing methods with less preprocessing and higher accuracy. Finally, the contribution to construction project management was discussed to enhance unstructured data management in the construction industry. The findings of this study contribute to effective management practices and assist construction professionals focus on value-added tasks such as decision making and corrective action planning.

Wang, R D, Zayed, T, Eltoukhy, A E E and Wu, H (2024) Integrated planning approach for optimizing tower crane and truck locations in modular integrated construction. Journal of Construction Engineering and Management, 150(09).

Wu, H, Chang, Y and Chen, Y (2024) Transitioning work arrangements in the construction industry: Changes in time-use patterns and individual greenhouse gas emissions. Journal of Construction Engineering and Management, 150(09).

Yang, J, Wu, Y and Li, D (2024) Block planning based on grid's topology for the dismantling of long-span spatial lattice structures. Journal of Construction Engineering and Management, 150(09).

Zhang, Y, Chang, R, Mao, W, Zuo, J, Liu, L and Han, Y (2024) Challenges of automating interior construction progress monitoring. Journal of Construction Engineering and Management, 150(09).

Zhang, Y, Liu, L, Song, Z, Zhao, Y and He, S (2024) Enhancing tunnel boring machine penetration rate predictions through particle swarm optimization and elman neural networks. Journal of Construction Engineering and Management, 150(09).