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Construction Management

A data-driven framework for conceptual cost estimation of infrastructure projects using XGBoost and Bayesian optimization

, , , &
Received 01 Jun 2023, Accepted 11 Dec 2023, Published online: 04 Jan 2024

References

  • AACE, AACE International recommended practices. 2004.
  • Ahn, J., S. H. Ji, S. J. Ahn, M. Park, H. S. Lee, N. Kwon, E. B. Lee, and Y. Kim. 2020. “Performance Evaluation of Normalization-Based CBR Models for Improving Construction Cost Estimation.” Automation in Construction 119. https://doi.org/10.1016/j.autcon.2020.103329.
  • Akanbi, T., and J. S. Zhang. 2021. “Design Information Extraction from Construction Specifications to Support Cost Estimation.” Automation in Construction 131. https://doi.org/10.1016/j.autcon.2021.103835.
  • Almasabha, G., K. F. Al-Shboul, A. Shehadeh, and O. Alshboul. 2023. ”Machine learning-based models for predicting the shear strength of synthetic fiber reinforced concrete beams without stirrups.“ Structures 52. https://doi.org/10.1016/j.istruc.2023.03.170.
  • Alshboul, O., A. Shehadeh, G. Almasabha, R. E. A. Mamlook, and A. S. Almuflih. 2022. “Evaluating the Impact of External Support on Green Building Construction Cost: A Hybrid Mathematical and Machine Learning Prediction Approach.” Buildings 12 (8): 1256. https://doi.org/10.3390/buildings12081256.
  • Antwarg, L., R. M. Miller, B. Shapira, and L. Rokach. 2021. “Explaining Anomalies Detected by Autoencoders Using Shapley Additive Explanations.” Expert Systems with Applications 186:115736. https://doi.org/10.1016/j.eswa.2021.115736.
  • Bai, B., J. Y. Zhang, X. Wu, G. W. Zhu, and X. Y. Li. 2021. “Reliability Prediction-Based Improved Dynamic Weight Particle Swarm Optimization and Back Propagation Neural Network in Engineering Systems.” Expert Systems with Applications 177. https://doi.org/10.1016/j.eswa.2021.114952.
  • Beniwal, M., A. Singh, and N. Kumar. 2023. “Forecasting Long-Term Stock Prices of Global Indices: A Forward-Validating Genetic Algorithm Optimization Approach for Support Vector Regression.” Applied Soft Computing 145:110566. https://doi.org/10.1016/j.asoc.2023.110566.
  • Bhargava, A., S. Labi, S. K. Chen, T. U. Saeed, and K. C. Sinha. 2017. “Predicting Cost Escalation Pathways and Deviation Severities of Infrastructure Projects Using Risk-Based Econometric Models and Monte Carlo Simulation.” Computer-Aided Civil and Infrastructure Engineering 32 (8): 620–640. https://doi.org/10.1111/mice.12279.
  • Cao, Y., B. Ashuri, and M. Baek. 2018. “Prediction of Unit Price Bids of Resurfacing Highway Projects Through Ensemble Machine Learning.” Journal of Computing in Civil Engineering 32 (5). https://doi.org/10.1061/(ASCE)CP.1943-5487.0000788.
  • Chakraborty, D., H. Elhegazy, H. Elzarka, and L. Gutierrez. 2020. “A Novel Construction Cost Prediction Model Using Hybrid Natural and Light Gradient Boosting.” Advanced Engineering Informatics 46:101201. https://doi.org/10.1016/j.aei.2020.101201.
  • Chen, T. Q., C. Guestrin, and M. Assoc Comp. 2016. “XGBoost: A Scalable Tree Boosting System.” In 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 785–794. San Francisco, CA.
  • Diekmann, J. E. 1983. “Probabilistic Estimating: Mathematics and Applications.” Journal of Construction Engineering and Management 109 (3): 297–308. https://doi.org/10.1061/(ASCE)0733-9364(1983)109:3(297).
  • Dong, W., Y. M. Huang, B. Lehane, and G. W. Ma. 2020. “XGBoost Algorithm-Based Prediction of Concrete Electrical Resistivity for Structural Health Monitoring.” Automation in Construction 114. https://doi.org/10.1016/j.autcon.2020.103155.
  • Dursun, O., and C. Stoy. 2016. “Conceptual Estimation of Construction Costs Using the Multistep Ahead Approach.” Journal of Construction Engineering and Management 142 (9). https://doi.org/10.1061/(ASCE)CO.1943-7862.0001150.
  • Elmousalami, H. H. 2020. “Artificial Intelligence and Parametric Construction Cost Estimate Modeling: State-Of-The-Art Review.” Journal of Construction Engineering and Management 146 (1). https://doi.org/10.1061/(ASCE)CO.1943-7862.0001678.
  • ElMousalami, H. H., A. H. Elyamany, and A. H. Ibrahim. 2018. “Predicting Conceptual Cost for Field Canal Improvement Projects.” Journal of Construction Engineering and Management 144 (11). https://doi.org/10.1061/(ASCE)CO.1943-7862.0001561.
  • Gu, Q. H., Y. X. Chang, N. X. Xiong, and L. Chen. 2021. “Forecasting Nickel Futures Price Based on the Empirical Wavelet Transform and Gradient Boosting Decision Trees.” Applied Soft Computing 109. https://doi.org/10.1016/j.asoc.2021.107472.
  • Guo, J. Q., L. Yang, R. F. Bie, J. G. Yu, Y. Gao, Y. Shen, and A. Kos. 2019. “An XGBoost-Based Physical Fitness Evaluation Model Using Advanced Feature Selection and Bayesian Hyper-Parameter Optimization for Wearable Running Monitoring.” Computer Networks 151:166–180. https://doi.org/10.1016/j.comnet.2019.01.026.
  • Hashemi, S. T., E. O. M. Ebadati, and H. Kaur. 2019. “A Hybrid Conceptual Cost Estimating Model Using ANN and GA for Power Plant Projects.” Neural Computing & Applications 31 (7): 2143–2154. https://doi.org/10.1007/s00521-017-3175-5.
  • He, X., R. Liu, and C. J. Anumba. 2021. “Data-Driven Insights on the Knowledge Gaps of Conceptual Cost Estimation Modeling.” Journal of Construction Engineering and Management 147 (2). https://doi.org/10.1061/(ASCE)CO.1943-7862.0001963.
  • Huang, J. L., Y. F. Li, and M. Xie. 2015. “An Empirical Analysis of Data Preprocessing for Machine Learning-Based Software Cost Estimation.” Information and Software Technology 67:108–127. https://doi.org/10.1016/j.infsof.2015.07.004.
  • Huang, Y. F., and S. W. Meng. 2019. “Automobile Insurance Classification Ratemaking Based on Telematics Driving Data.” Decision Support Systems 127. https://doi.org/10.1016/j.dss.2019.113156.
  • Hyari, K. H., A. Al-Daraiseh, and M. El-Mashaleh. 2016. “Conceptual Cost Estimation Model for Engineering Services in Public Construction Projects.” Journal of Management in Engineering 32 (1). https://doi.org/10.1061/(ASCE)ME.1943-5479.0000381.
  • IM Almadi, A., R. E. Al Mamlook, I. Ullah, O. Alshboul, N. Bandara, and A. Shehadeh. 2022. “Vehicle Collisions Analysis on Highways Based on Multi-User Driving Simulator and Multinomial Logistic Regression Model on US Highways in Michigan.” International Journal of Crashworthiness 28 (6): 770–785. https://doi.org/10.1080/13588265.2022.2130608.
  • Juszczyk, M. 2020. “On the Search of Models for Early Cost Estimates of Bridges: An SVM-Based Approach.” Buildings 10 (1): 2. https://doi.org/10.3390/buildings10010002.
  • Karaca, I., D. D. Gransberg, and H. D. Jeong. 2020. “Improving the Accuracy of Early Cost Estimates on Transportation Infrastructure Projects.” Journal of Management in Engineering 36 (5). https://doi.org/10.1061/(ASCE)ME.1943-5479.0000819.
  • Kim, D., K. Kwon, K. Pham, J. Y. Oh, and H. Choi. 2022. “Surface Settlement Prediction for Urban Tunneling Using Machine Learning Algorithms with Bayesian Optimization.” Automation in Construction 140:104331. https://doi.org/10.1016/j.autcon.2022.104331.
  • Koc, K., O. Ekmekcioglu, and A. P. Gurgun. 2021. “Integrating Feature Engineering, Genetic Algorithm and Tree-Based Machine Learning Methods to Predict the Post-Accident Disability Status of Construction Workers.” Automation in Construction 131:103896. https://doi.org/10.1016/j.autcon.2021.103896.
  • Lee, J. G., H. S. Lee, M. Park, and J. Seo. 2022. “Early-stage cost estimation model for power generation project with limited historical data.” Engineering, Construction & Architectural Management 29 (7): 2599–2614. https://doi.org/10.1108/ECAM-04-2020-0261.
  • Lim, S., and S. Chi. 2019. “Xgboost application on bridge management systems for proactive damage estimation.” Advanced Engineering Informatics 41. https://doi.org/10.1016/j.aei.2019.100922.
  • Liu, Y. M., L. Liu, L. Yang, L. Hao, and Y. Bao. 2021. “Measuring Distance Using Ultra-Wideband Radio Technology Enhanced by Extreme Gradient Boosting Decision Tree (XGBoost).” Automation in Construction 126:103678. https://doi.org/10.1016/j.autcon.2021.103678.
  • Lundberg, S. and S. I. Lee. A Unified Approach to Interpreting Model Predictions. in Nips. 2017.
  • Luong, P., D. Nguyen, S. Gupta, S. Rana, and S. Venkatesh. 2021. “Adaptive Cost-Aware Bayesian Optimization.” Knowledge-Based Systems 232:107481. https://doi.org/10.1016/j.knosys.2021.107481.
  • Lv, F., J. J. Wang, B. Cui, J. Yu, J. E. Sun, and J. Zhang. 2020. “An Improved Extreme Gradient Boosting Approach to Vehicle Speed Prediction for Construction Simulation of Earthwork.” Automation in Construction 119:103351. https://doi.org/10.1016/j.autcon.2020.103351.
  • Mahmoodzadeh, A., H. R. Nejati, and M. Mohammadi. 2022. “Optimized Machine Learning Modelling for Predicting the Construction Cost and Duration of Tunnelling Projects.” Automation in Construction 139. https://doi.org/10.1016/j.autcon.2022.104305.
  • Meharie, M. G., W. J. Mengesha, Z. A. Gariy, and R. N. N. Mutuku. 2022. “Application of Stacking Ensemble Machine Learning Algorithm in Predicting the Cost of Highway Construction Projects.” Engineering, Construction & Architectural Management 29 (7): 2836–2853. https://doi.org/10.1108/ECAM-02-2020-0128.
  • Mohsenijam, A., M. F. F. Siu, and M. Lu. 2017. “Modified Stepwise Regression Approach to Streamlining Predictive Analytics for Construction Engineering Applications.” Journal of Computing in Civil Engineering 31 (3). https://doi.org/10.1061/(ASCE)CP.1943-5487.0000636.
  • Rao, H., X. Shi, A. K. Rodrigue, J. Feng, Y. Xia, M. Elhoseny, X. Yuan, and L. Gu. 2019. “Feature Selection Based on Artificial Bee Colony and Gradient Boosting Decision Tree.” Applied Soft Computing 74:634–642. https://doi.org/10.1016/j.asoc.2018.10.036.
  • Rodriguez-Calvo, A., P. Frias, J. Reneses, R. Cossent, and C. Mateo. 2014. “Optimal Investment in Smart MV/LV Substations to Improve Continuity of Supply.” International Journal of Electrical Power & Energy Systems 62:410–418. https://doi.org/10.1016/j.ijepes.2014.04.062.
  • Shahriari, B., K. Swersky, Z. Y. Wang, R. P. Adams, and N. de Freitas. 2016. “Taking the Human Out of the Loop: A Review of Bayesian Optimization.” Proceedings of the IEEE 104 (1): 148–175. https://doi.org/10.1109/JPROC.2015.2494218.
  • Shehadeh, A., O. Alshboul, R. E. Al Mamlook, and O. Hamedat. 2021. “Machine Learning Models for Predicting the Residual Value of Heavy Construction Equipment: An Evaluation of Modified Decision Tree, LightGbm, and XGBoost Regression.” Automation in Construction 129. https://doi.org/10.1016/j.autcon.2021.103827.
  • Shin, S., Y. Lee, M. Kim, J. Park, S. Lee, and K. Min. 2020. “Deep Neural Network Model with Bayesian Hyperparameter Optimization for Prediction of NOx at Transient Conditions in a Diesel Engine.” Engineering Applications of Artificial Intelligence 94. https://doi.org/10.1016/j.engappai.2020.103761.
  • Shi, R., X. Y. Xu, J. M. Li, and Y. Q. Li. 2021. “Prediction and Analysis of Train Arrival Delay Based on XGBoost and Bayesian Optimization.” Applied Soft Computing 109. https://doi.org/10.1016/j.asoc.2021.107538.
  • Son, H., C. Kim, and C. Kim. 2012. “Hybrid Principal Component Analysis and Support Vector Machine Model for Predicting the Cost Performance of Commercial Building Projects Using Pre-Project Planning Variables.” Automation in Construction 27:60–66. https://doi.org/10.1016/j.autcon.2012.05.013.
  • Sovacool, B. K., A. Gilbert, and D. Nugent. 2014. “An International Comparative Assessment of Construction Cost Overruns for Electricity Infrastructure.” Energy Research & Social Science 3:152–160. https://doi.org/10.1016/j.erss.2014.07.016.
  • Tayefeh Hashemi, S., O. M. Ebadati, and H. Kaur. 2020. “Cost Estimation and Prediction in Construction Projects: A Systematic Review on Machine Learning Techniques.” SN Applied Sciences 2 (10). https://doi.org/10.1007/s42452-020-03497-1.
  • Tijanic, K., D. Car-Pusic, and M. Sperac. 2020. “Cost estimation in road construction using artificial neural network.” Neural Computing & Applications 32 (13): 9343–9355. https://doi.org/10.1007/s00521-019-04443-y.
  • Vasconcelos, T. D., D. de Souza, G. C. D. Virgolino, C. L. C. Mattos, and J. P. P. Gomes. 2022. “Self-Tuning Portfolio-Based Bayesian Optimization.” Expert Systems with Applications 188:115847. https://doi.org/10.1016/j.eswa.2021.115847.
  • Wang, R., V. Asghari, C. M. Cheung, S. C. Hsu, and C. J. Lee. 2022. “Assessing Effects of Economic Factors on Construction Cost Estimation Using Deep Neural Networks.” Automation in Construction 134. https://doi.org/10.1016/j.autcon.2021.104080.
  • Wang, J. H., Y. T. Ren, T. H. Shu, L. Y. Shen, X. Liao, N. Yang, and H. M. He. 2020. “Economic Perspective-Based Analysis on Urban Infrastructures Carrying Capacity — a China Study.” Environmental Impact Assessment Review 83:83. https://doi.org/10.1016/j.eiar.2020.106381.
  • Wu, J., X. Y. Chen, H. Zhang, L. D. Xiong, H. Lei, and S. H. Deng. 2019. “Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization.” Journal of Electronic Science and Technology 17 (1): 26–40.
  • Xue, P. N., Y. Jiang, Z. G. Zhou, X. Chen, X. M. Fang, and J. Liu. 2019. “Multi-Step Ahead Forecasting of Heat Load in District Heating Systems Using Machine Learning Algorithms.” Energy 188:116085. https://doi.org/10.1016/j.energy.2019.116085.
  • Xu, X. M., L. Y. Peng, Z. S. Ji, S. P. Zheng, Z. X. Tian, and S. P. Geng. 2021. “Research on Substation Project Cost Prediction Based on Sparrow Search Algorithm Optimized BP Neural Network.” Sustainability 13 (24).
  • Yun, K. K., S. W. Yoon, and D. Won. 2021. “Prediction of Stock Price Direction Using a Hybrid GA-XGBoost Algorithm with a Three-Stage Feature Engineering Process.” Expert Systems with Applications 186:115716. https://doi.org/10.1016/j.eswa.2021.115716.
  • Zhang, C. X., and J. B. Fan. 2018. “Chapter 5 - UHVAC Substation and Main Electrical Equipment.” In UHV Transmission Technology, edited by T. C. E. P. R. Institute, 165–236, China: Academic Press.
  • Zhang, H. B., D. D. Qiu, R. Z. Wu, Y. X. Deng, D. H. Ji, and T. Li. 2019. “Novel Framework for Image Attribute Annotation with Gene Selection XGBoost Algorithm and Relative Attribute Model.” Applied Soft Computing 80:57–79. https://doi.org/10.1016/j.asoc.2019.03.017.
  • Zhou, H., S. Huang, P. Zhang, B. Ma, P. Ma, and X. Feng. 2023. “Prediction of Jacking Force Using PSO-BPNN and PSO-SVR Algorithm in Curved Pipe Roof.” Tunnelling and Underground Space Technology138:105159. https://doi.org/10.1016/j.tust.2023.105159.