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Biswas, P K, Khan, S M, Piratla, K and Chowdhury, M (2023) Development and evaluation of statistical and machine-learning models for queue-length estimation for lane closures in freeway work zones. Journal of Construction Engineering and Management, 149(05).

Chong, H Y and Cheng, M (2023) Smart contract implementation in building information modeling-enabled projects: Approach to contract administration. Journal of Construction Engineering and Management, 149(05).

Ding, S, Hu, H, Dai, L and Wang, W (2023) Blockchain adoption among multi-stakeholders under government subsidy: From the technology diffusion perspective. Journal of Construction Engineering and Management, 149(05).

Feng, Z, Chao, Q, Tan, C and Yang, Y (2023) Using bargaining model with loss aversion and a risk of breakdown to determine compensation for buyback of early terminating BOT highway projects. Journal of Construction Engineering and Management, 149(05).

Garg, S and Misra, S (2023) Framework for estimating quality-related incentive and disincentive in construction projects. Journal of Construction Engineering and Management, 149(05).

Gonsalves, N, Akanmu, A, Gao, X, Agee, P and Shojaei, A (2023) Industry perception of the suitability of wearable robot for construction work. Journal of Construction Engineering and Management, 149(05).

Han, S, Jiang, Y, Huang, Y, Wang, M, Bai, Y and Spool-White, A (2023) Scan2drawing: Use of deep learning for as-built model landscape architecture. Journal of Construction Engineering and Management, 149(05).

Jung, H, Seo, W and Kang, Y (2023) Differences in workers' safety behavior by project size and risk level of work in South Korea. Journal of Construction Engineering and Management, 149(05).

Labik, O, Nahmens, I, Ikuma, L and Harvey, C (2023) On-site versus in-factory installation of solar-plus-storage in modular construction. Journal of Construction Engineering and Management, 149(05).

Saqib, G, Hassan, M U, Zubair, M U and Choudhry, R M (2023) Investigating the acceptance of an electronic incident reporting system in the construction industry: An application of the technology acceptance model. Journal of Construction Engineering and Management, 149(05).

Shen, K, Zhu, Y, Pan, J and Li, X (2023) An intelligent decision-making model for the design of precast slab joints based on case-based reasoning. Journal of Construction Engineering and Management, 149(05).

Ullal, A (2023) Construction conditions and practices during war in Afghanistan. Journal of Construction Engineering and Management, 149(05).

Wang, C, Zhang, S, Gao, Y, Guo, Q and Zhang, L (2023) Effect of contractual complexity on conflict in construction subcontracting: Moderating roles of contractual enforcement and organizational culture distance. Journal of Construction Engineering and Management, 149(05).

Wang, J, Han, C and Li, X (2023) Modified streamlined optimization algorithm for time-cost tradeoff problems of complex large-scale construction projects. Journal of Construction Engineering and Management, 149(05).

Xue, G, Liu, S, Ren, L and Gong, D (2023) Adaptive cross-scenario few-shot learning framework for structural damage detection in civil infrastructure. Journal of Construction Engineering and Management, 149(05).

  • Type: Journal Article
  • Keywords: intelligent construction; metalearning; probabilistic neural networks; structural damage detection
  • ISBN/ISSN: 0733-9364
  • URL: http://doi.org/10.1061/JCEMD4.COENG-13196
  • Abstract:
    Structural damage detection techniques are gaining widespread attention in construction engineering and management. However, the scarcity of structural damage samples and the cross-task transferability of existing knowledge currently limit this technique in practical applications. Therefore, this paper proposes a novel framework for structural damage detection with large scope of cross-task learning capability that incorporates Bayesian estimation and variational inference into the deep learning backbones and Bayesian weight function into the outer loop process of metalearning. Experimental results demonstrate the superiority of this method for both structural damage image classification and structural damage semantic segmentation. Compared with existing frameworks, the proposed method can alleviate the negative influence of domain bias and reduce computation time and costs due to sample labeling. This paper also discusses how the proposed framework can be used to train a model of the structural damage detection framework in extreme cases. The framework and findings presented in this paper have important theoretical and practical contributions to the literature on vision-based structural damage detection.

Zhang, Y, Minchin, R E, Flood, I and Ries, R J (2023) Preliminary cost estimation of highway projects using statistical learning methods. Journal of Construction Engineering and Management, 149(05).

Zhou, Y, Wang, X, Gosling, J and Naim, M M (2023) The system dynamics of engineer-to-order construction projects: Past, present, and future. Journal of Construction Engineering and Management, 149(05).