Abstracts – Browse Results

Search or browse again.

Click on the titles below to expand the information about each abstract.
Viewing 18 results ...

Albeaino, G, Brophy, P, Jeelani, I, Gheisari, M and Issa, R R A (2023) Impact of drone presence on construction individuals working at heights. Journal of Construction Engineering and Management, 149(11).

Chadee, A A, Martin, H, Chadee, X T, Bahadoorsingh, S and Olutoge, F (2023) Root cause of cost overrun risks in public sector social housing programs in sids: Fuzzy synthetic evaluation. Journal of Construction Engineering and Management, 149(11).

Duan, P, Zhou, J and Goh, Y M (2023) Safety risk diagnosis based on motion trajectory for construction workers: An integrated approach. Journal of Construction Engineering and Management, 149(11).

Erk, E Y, Budayan, C, Koc, K and Tokdemir, O B (2023) Value creation in PPP projects undertaken in the Turkish healthcare industry. Journal of Construction Engineering and Management, 149(11).

Hsu, C L, Wang, J T and Hou, H Y (2023) A blockchain-based parametric model library for knowledge sharing in building information modeling collaboration. Journal of Construction Engineering and Management, 149(11).

Lim, H W and Francis, V (2023) A conceptual model of cognitive and behavioral processes affecting mental health in the construction industry: A systematic review. Journal of Construction Engineering and Management, 149(11).

Mostofi, F, Toǧan, V, Başaǧa, H B, Çltlpltloǧlu, A and Tokdemir, O B (2023) Multiedge graph convolutional network for house price prediction. Journal of Construction Engineering and Management, 149(11).

  • Type: Journal Article
  • Keywords: construction cost management; graph convolutional network; house price prediction; informed decision-making; multiedge graph
  • ISBN/ISSN: 0733-9364
  • URL: http://doi.org/10.1061/JCEMD4.COENG-13559
  • Abstract:
    Accurate house price prediction allows construction investors to make informed decisions about the housing market and understand the growth opportunities for development and the risks and rewards of different construction projects. Machine learning (ML) models have been utilized as house price predictors, reducing decision-making costs, and increasing reliability. To further improve the reliability of the existing predictors, this study develops a hybrid multiedge graph convolutional network (GCN) that considers the various relationships between house price records. The developed hybrid multiedge GCN receives richer input from the multidependency information and thus provides a more reliable prediction that accounts for price changes based on the neighborhood, building age, and number of bedrooms. Compared to other ML approaches, the developed multiedge GCN house price predictor displayed good prediction accuracy while providing valuable insights into the factors that affect the house price, such as the desirability of different neighborhoods and building age.

Tang, Y and Yao, H (2023) Watch out for the hidden costs of subcontracting in construction projects: The impacts of subcontractor dispersion. Journal of Construction Engineering and Management, 149(11).

Wang, D, Huang, R, Qiao, Y, Sheng, Z, Li, K and Zhao, L (2023) How perceived leader-member exchange differentiation affects construction workers' safety citizenship behavior: Organizational identity and felt safety responsibility as mediators. Journal of Construction Engineering and Management, 149(11).

Wang, S, Kim, M, Hae, H, Cao, M and Kim, J (2023) The development of a rebar-counting model for reinforced concrete columns: Using an unmanned aerial vehicle and deep-learning approach. Journal of Construction Engineering and Management, 149(11).

Wang, Z, He, Q, Locatelli, G, Wang, G and Li, Y (2023) Exploring environmental collaboration and greenwashing in construction projects: Integrative governance framework. Journal of Construction Engineering and Management, 149(11).

Watton, J, Unterhitzenberger, C, Locatelli, G and Invernizzi, D C (2023) The cost drivers of infrastructure projects: Definition, classification, and conceptualization. Journal of Construction Engineering and Management, 149(11).

Wu, H, Han, Y, Zhang, M, Abebe, B D, Legesse, M B and Jin, R (2023) Identifying unsafe behavior of construction workers: A dynamic approach combining skeleton information and spatiotemporal features. Journal of Construction Engineering and Management, 149(11).

Wu, L, Mohamed, E, Jafari, P and Abourizk, S (2023) Machine learning-based Bayesian framework for interval estimate of unsafe-event prediction in construction. Journal of Construction Engineering and Management, 149(11).

Wu, S, Yu, L, Cao, T, Yuan, C and Du, Y (2023) How dependence asymmetry and explicit contract shape contractor-subcontractor collaboration: A psychological perspective of fairness. Journal of Construction Engineering and Management, 149(11).

You, H, Xu, F and Du, J (2023) Improved boundary identification of stacked objects with sparse lidar augmentation scanning. Journal of Construction Engineering and Management, 149(11).

Zheng, X, Chen, J, Xia, B, Skitmore, M and Zeng, S (2023) Understanding the megaproject social responsibility network among stakeholders: A reciprocal-exchange perspective. Journal of Construction Engineering and Management, 149(11).

Zhou, Q, Deng, X, Hwang, B G, Mahmoudi, A and Liu, Y (2023) Integrating the factors affecting knowledge transfer within international construction projects: Individual and team perspectives. Journal of Construction Engineering and Management, 149(11).