Against the backdrop of rapid development in China’s construction and infrastructure sectors, discrepancies between project budgets and actual costs have become pronounced, manifesting in project overruns and suspensions, posing significant challenges. To address inaccuracies in investment targets and operational complexities, this study focuses on a beam-bridge construction project in a district of Shijiazhuang city as a case study. Drawing upon historical analogs, the project employs a Work Breakdown Structure (WBS) to decompose the engineering works. Building on theories of Cost Significant (CS) and Whole Life Costing (WLC), the study constructs Cost Significant Items (CSIs) and develops a CNN-BiLSTM-Attention neural network for nonlinear prediction. By identifying significant cost drivers in engineering projects, this paper presents a streamlined cost estimation method that significantly reduces computational burdens, simplifies data collection processes, and optimizes data analysis and forecasting, thereby enhancing prediction accuracy. Finally, validation with real-world cost fluctuation data demonstrates minor errors, meeting predictive requirements across project execution phases.
Duan XC, Zhang XP, Zhang JL, 2008, Discussion on Comprehensive Cost Prediction Method based on CSIs, FIS, WLC, and FC. Journal of the China Railway Society, 2008(03): 104–109.
Liu JY, Chen L, Song N, et al., 2015, Research on CS and BPNN Estimation Method for Environmental Costs of Green High-Speed Railway Construction. Journal of Railway Engineering Society, 32(07): 111–116.
Duan XC, Xu J, 2020, Research on SOM-RBF Neural Network Estimation Model for Road Engineering. Journal of the China Railway Society, 42(01): 9–14.
Wang LL, Xing WH, Hao JJ, et al., 2023, Construction and Application of Dynamic Optimization Control System for the Entire Line Construction Progress of Expressway. China Highway, 2023(06): 95–98. https://doi.org/10.13468/j.cnki.chw.2023.06.014
Zhou YL, Tan YY, Zhao YH, 2024, Soybean Futures Price Prediction based on CEEMDAN-SE-CNN-BiLSTM Model. Journal of Ningbo University of Technology, 36(02): 14–20.
Zhou C, 2024, Research on Quantum Bee Colony Optimization Algorithm for Refined Cost Prediction in BIM Engineering. Journal of Qiqihar University (Natural Science Edition), 2024(03): 1–7.
Liu WJ, Huang ZL, 2023, Prediction of Construction Cost Index in Building Engineering based on Exponential Smoothing and PSO-BP Hybrid Model. Journal of Wuhan University of Technology (Information & Management Engineering), 45(03): 404–409.
Peng JL, Hu K, Wang MY, et al., 2023, Research on Residential Engineering Cost Prediction based on SSA-LSSVM. Journal of Changsha University of Science & Technology (Natural Science Edition), 20(03): 137–145. https://doi.org/10.19951/j.cnki.1672-9331.20220624001
Li XH, Ma G, 2022, Design and Simulation of ARIMA-ES Hybrid Model for Rapid Prediction of Construction Project Cost. Automation Technology and Applications, 41(11): 36–38 + 51. https://doi.org/10.20033/j.1003-7241.(2022)11-0036-04
Yong XZ, Huang S, Yuan W, 2022, Study on Investment Estimation of University Construction Projects based on BAS-SCA-BP Model. Journal of Engineering Management, 36(05): 136–141. https://doi.org/10.13991/j.cnki.jem.2022.05.024
Fan SQ, Chen H, Wang DM, et al., 2023, Engineering Cost Prediction Ensemble Model based on Stacking Fusion. Journal of Yantai University (Natural Science and Engineering Edition), 36(02): 211–216. https://doi.org/10.13951/j.cnki.37-1213/n.211221
Liu SC, Yang QJ, Dong N, et al., 2024, Application of RA-ANN Ensemble Model in Engineering Cost Prediction. Journal of Chongqing University of Technology (Natural Science), 2024: 1–12. http://kns.cnki.net/kcms/detail/50.1205.T.20220104.1956.005.html
Chen YH, Zheng SM, Liu WL, 2021, Research on Industrial Construction Engineering Cost Prediction based on Machine Learning. Journal of Wuhan University of Technology (Information & Management Engineering), 43(04): 314–321.
Zhang BX, 2019, BIM-assisted Analysis of Engineering Cost based on GA Network Model. Journal of Southwest University (Natural Science Edition), 41(11): 120–124. https://doi.org/10.13718/j.cnki.xdzk.2019.11.015
Guo, ZY, Li J, Wang W, et al., 2024, Research and Application of Big Data Platforms for Urban Renewal. Tsinghua Univ. Sci. Technol., 2024: 1–13.