Deep Multilayer Perceptron Neural Network for the Prediction of Iranian Dam Project Delay Risks
Publication: Journal of Construction Engineering and Management
Volume 149, Issue 4
Abstract
Construction delays are among the industry’s most significant challenges, especially in the infrastructure sector, where delays can have serious socio-economic consequences. Recently, advances in deep learning (DL) have opened up new possibilities for tackling complex issues more efficiently. This study aims to evaluate the potential of deep neural networks in predicting the level of delay in Iranian dam construction projects. As the first step, 65 delay risk factors were identified through a comprehensive literature review and interviews. Then risk scores for 53 completed dam projects in Iran were determined through a questionnaire survey. Subsequently, the most significant latent features were extracted using principal component analysis (PCA). The resultant variables were combined with two project characteristics to develop the input dataset. Finally, the resulting dataset was used to develop a deep multilayer perceptron neural network (MLP-NN) model to predict project delays. The prediction performance of the deep-MLP model was then evaluated and compared to that of the best delay prediction models found in previous studies. The three-times repeated stratified five-fold cross-validation results demonstrated that the proposed deep-NN model outperformed all previous approaches for delay prediction on all performance metrics. This study also explores the effectiveness of combining delay risk factors with project characteristics to train the predictive model. According to the results, adding project characteristic factors to the training dataset significantly improved the prediction performance of deep-MLP. The work presented here can assist managers of future dam constructions in the early stages of the project in selecting and prioritizing projects within a portfolio and allocating a sufficient buffer to ensure the project’s timely completion.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
The initial and final lists of delay factors, along with the frequency of their occurrence in 38 relevant research articles, as well as the input dataset and Python code for developing the proposed model, are available at https://doi.org/10.5281/zenodo.7194714.
References
Abbasi, O., E. Noorzai, K. Gharouni Jafari, and M. Golabchi. 2020. “Exploring the causes of delays in construction industry using a cause-and-effect diagram: A case study for Iran.” J. Archit. Eng. 26 (3): 05020008. https://doi.org/10.1061/(ASCE)AE.1943-5568.0000431.
Abpeikar, S., M. Ghatee, G. L. Foresti, and C. Micheloni. 2020. “Adaptive neural tree exploiting expert nodes to classify high-dimensional data.” Neural Networks 124 (Apr): 20–38. https://doi.org/10.1016/j.neunet.2019.12.029.
Alameer, Z., M. Abd Elaziz, A. A. Ewees, H. Ye, and Z. Jianhua. 2019. “Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm.” Resour. Policy 61 (Jun): 250–260. https://doi.org/10.1016/j.resourpol.2019.02.014.
Alamri, N., O. Amoudi, and G. Njie. 2017. “Analysis of construction delay causes in dams projects in Oman.” Eur. J. Manage. Bus. Econ. 6 (2): 19–42.
Alenazi, E., Z. Adamu, and A. Al-Otaibi. 2022. “Exploring the nature and impact of client-related delays on contemporary Saudi construction projects.” Buildings 12 (7): 880. https://doi.org/10.3390/buildings12070880.
Alkadri, S., N. Ledwos, N. Mirchi, A. Reich, R. Yilmaz, M. Driscoll, and R. F. Del Maestro. 2021. “Utilizing a multilayer perceptron artificial neural network to assess a virtual reality surgical procedure.” Comput. Biol. Med. 136 (Sep): 104770. https://doi.org/10.1016/j.compbiomed.2021.104770.
Altan, A., S. Karasu, and S. Bekiros. 2019. “Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques.” Chaos Solit. Fractals 126 (Aug): 325–336. https://doi.org/10.1016/j.chaos.2019.07.011.
Asadi, A., M. Alsubaey, and C. Makatsoris. 2015. “A machine learning approach for predicting delays in construction logistics.” Int. J. Adv. Logist. 4 (2): 115–130. https://doi.org/10.1080/2287108X.2015.1059920.
Assaad, R., I. H. El-adaway, and I. S. Abotaleb. 2020. “Predicting project performance in the construction industry.” J. Constr. Eng. Manage. 146 (5): 04020030. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001797.
Awada, M., F. J. Srour, and I. M. Srour. 2020. “Data-driven machine learning approach to integrate field submittals in project scheduling.” J. Manage. Eng. 37 (1): 04020104. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000873.
Aziz, R. F. 2013. “Ranking of delay factors in construction projects after Egyptian revolution.” Alexandria Eng. J. 52 (3): 387–406. https://doi.org/10.1016/j.aej.2013.03.002.
Bagaya, O., and J. Song. 2016. “Empirical study of factors influencing schedule delays of public construction projects in Burkina Faso.” J. Manage. Eng. 32 (5): 05016014. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000443.
Barbosa, F., J. Woetzel, J. Mischke, M. J. Ribeirinho, M. Sridhar, M. Parsons, M. Bertram, and S. Brown. 2017. Reinventing construction: A route to higher productivity. New York: McKinsey & Company.
Bilal, M., L. O. Oyedele, J. Qadir, K. Munir, S. O. Ajayi, O. O. Akinade, H. A. Owolabi, H. A. Alaka, and M. Pasha. 2016. “Big data in the construction industry: A review of present status, opportunities, and future trends.” Adv. Eng. Inf. 30 (3): 500–521. https://doi.org/10.1016/j.aei.2016.07.001.
Cheng, M. Y., Y. H. Chang, and D. Korir. 2019. “Novel approach to estimating schedule to completion in construction projects using sequence and nonsequence learning.” J. Constr. Eng. Manage. 145 (11): 04019072. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001697.
Chou, J. S., and J. P. P. Thedja. 2016. “Metaheuristic optimization within machine learning-based classification system for early warnings related to geotechnical problems.” Autom. Constr. 68 (Feb): 65–80. https://doi.org/10.1016/j.autcon.2016.03.015.
Choudhry, R. M., M. A. Aslam, J. W. Hinze, and F. M. Arain. 2014. “Cost and schedule risk analysis of bridge construction in Pakistan: Establishing risk guidelines.” J. Constr. Eng. Manage. 140 (7): 04014020. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000857.
Dargan, S., M. Kumar, M. R. Ayyagari, and G. Kumar. 2020. “A survey of deep learning and its applications: A new paradigm to machine learning.” Arch. Comput. 27 (4): 1071–1092. https://doi.org/10.1007/s11831-019-09344-w.
Doloi, H., A. Sawhney, K. C. Iyer, and S. Rentala. 2012. “Analysing factors affecting delays in Indian construction projects.” Int. J. Project Manage. 30 (4): 479–489. https://doi.org/10.1016/j.ijproman.2011.10.004.
El-Kholy, A. M. 2019. “Exploring the best ANN model based on four paradigms to predict delay and cost overrun percentages of highway projects.” Int. J. Constr. Manage. 21 (7): 694–712. https://doi.org/10.1080/15623599.2019.1580001.
Eshghi, P. 2013. “Dimensionality choice in principal components analysis via cross-validatory methods.” Chemometr. Intell. Lab. Syst. 130 (Jul): 6–13. https://doi.org/10.1016/j.chemolab.2013.09.004.
Fallahnejad, M. H. 2013. “Delay causes in Iran gas pipeline projects.” Int. J. Project Manage. 31 (1): 136–146. https://doi.org/10.1016/j.ijproman.2012.06.003.
Gardezi, S. S. S., I. A. Manarvi, and S. J. S. Gardezi. 2014. “Time extension factors in construction industry of Pakistan.” Procedia Eng. 77 (Aug): 196–204. https://doi.org/10.1016/j.proeng.2014.07.022.
Ghiasi, M. M., S. Zendehboudi, and A. A. Mohsenipour. 2020. “Decision tree-based diagnosis of coronary artery disease: CART model.” Comput. Methods Programs Biomed. 192 (Jul): 105400. https://doi.org/10.1016/j.cmpb.2020.105400.
Gondia, A., A. Siam, W. El-Dakhakhni, and A. H. Nassar. 2019. “Machine learning algorithms for construction projects delay risk prediction.” J. Constr. Eng. Manage. 146 (1): 04019085. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001736.
Heaton, J. 2008. The number of hidden layers. 157–158. Chesterfield, MO: Heaton Research.
Jiang, L., L. Zhang, L. Yu, and D. Wang. 2019. “Class-specific attribute weighted naive Bayes.” Pattern Recognit. 88 (Jan): 321–330. https://doi.org/10.1016/j.patcog.2018.11.032.
Lima-Junior, F. R., and L. C. R. Carpinetti. 2019. “Predicting supply chain performance based on SCOR® metrics and multilayer perceptron neural networks.” Int. J. Prod. Econ. 212 (Jun): 19–38. https://doi.org/10.1016/j.ijpe.2019.02.001.
Liu, B., B. Yang, J. Xiao, D. Zhu, B. Zhang, Z. Wang, and M. Dong. 2021. “Review of Optimization Dynamically Applied in the Construction and the Application Potential of ICT.” Sustainability 13 (10): 5478. https://doi.org/10.3390/su13105478.
Liu, R., and D. F. Gillies. 2016. “Overfitting in linear feature extraction for classification of high-dimensional image data.” Pattern Recognit. 53 (Feb): 73–86. https://doi.org/10.1016/j.patcog.2015.11.015.
Liu, Z., and S. Li. 2020. “A sound monitoring system for prevention of underground pipeline damage caused by construction.” Autom. Constr. 113 (May): 103125. https://doi.org/10.1016/j.autcon.2020.103125.
Madani, K. 2014. “Water management in Iran: What is causing the looming crisis?” J. Environ. Stud. Sci. 4 (4): 315–328. https://doi.org/10.1007/s13412-014-0182-z.
Madani, K., A. AghaKouchak, and A. Mirchi. 2016. “Iran’s socio-economic drought: Challenges of a water-bankrupt nation.” Iran. Stud. 49 (6): 997–1016. https://doi.org/10.1080/00210862.2016.1259286.
McKinney, W. 2011. “Pandas: A foundational Python library for data analysis and statistics.” Python High. Perform. Sci. Comput. 14 (9): 1–9.
Moraes, R., J. F. Valiati, and W. P. G. Neto. 2012. “Document-level sentiment classification: An empirical comparison between SVM and ANN.” Expert Syst. Appl. 40 (2): 621–633. https://doi.org/10.1016/j.eswa.2012.07.059.
MR, G. R., N. Somu, and A. P. Mathur. 2020. “A multilayer perceptron model for anomaly detection in water treatment plants.” Int. J. Crit. Infrastruct. Prot. 31 (Dec): 100393. https://doi.org/10.1016/j.ijcip.2020.100393.
Mukuka, M., C. Aigbavboa, and W. Thwala. 2015. “Effects of construction projects schedule overruns: A case of the Gauteng Province, South Africa.” Procedia Manuf. 3 (Feb): 1690–1695. https://doi.org/10.1016/j.promfg.2015.07.989.
Ng, S. S., Y. Xing, and K. L. Tsui. 2014. “A naive Bayes model for robust remaining useful life prediction of lithium-ion battery.” Appl. Energy 118 (Apr): 114–123. https://doi.org/10.1016/j.apenergy.2013.12.020.
Nhu, V. H., et al. 2020. “Shallow landslide susceptibility mapping by random forest base classifier and its ensembles in a semi-arid region of Iran.” Forests 11 (4): 421. https://doi.org/10.3390/f11040421.
Noorzai, E. 2020. “Performance analysis of alternative contracting methods for highway construction projects: A case study for Iran.” J. Infrastruct. Syst. 26 (2): 04020003. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000528.
Park, Y. S., and S. Lek. 2016. “Artificial neural networks: Multilayer perceptron for ecological modeling.” Dev. Environ. Modell. 28 (Jun): 123–140. https://doi.org/10.1016/B978-0-444-63623-2.00007-4.
Pedregosa, F., et al. 2011. “Scikit-learn: Machine learning in Python.” J. Mach. Learn. Res. 12 (Nov): 2825–2830.
Poh, C. Q., C. U. Ubeynarayana, and Y. M. Goh. 2018. “Safety leading indicators for construction sites: A machine learning approach.” Autom. Constr. 93 (Sep): 375–386. https://doi.org/10.1016/j.autcon.2018.03.022.
Polit, D. F., and C. T. Beck. 2006. “The content validity index: Are you sure you know what’s being reported? Critique and recommendations.” Res. Nurs. Health 29 (5): 489–497. https://doi.org/10.1002/nur.20147.
Qi, J., G. Jiang, G. Li, Y. Sun, and B. Tao. 2020. “Surface EMG hand gesture recognition system based on PCA and GRNN.” Neural Comput. Appl. 32 (10): 6343–6351. https://doi.org/10.1007/s00521-019-04142-8.
Saccenti, E., and J. Camacho. 2015. “Determining the number of components in principal components analysis: A comparison of statistical, crossvalidation and approximated methods.” Chemometr. Intell. Lab. Syst. 149 (Aug): 99–116. https://doi.org/10.1016/j.chemolab.2015.10.006.
Sanni-Anibire, M. O., R. Mohamad Zin, and S. O. Olatunji. 2020a. “Causes of delay in the global construction industry: A meta analytical review.” Int. J. Constr. Manage. 22 (8): 1395–1407. https://doi.org/10.1080/15623599.2020.1716132.
Sanni-Anibire, M. O., R. M. Zin, and S. O. Olatunji. 2020b. “Machine learning model for delay risk assessment in tall building projects.” Int. J. Constr. Manage. 22 (11): 2134–2143. https://doi.org/10.1080/15623599.2020.1768326.
Shiue, Y. R., K. C. Lee, and C. T. Su. 2018. “Real-time scheduling for a smart factory using a reinforcement learning approach.” Comput. Ind. Eng. 125 (Jun): 604–614. https://doi.org/10.1016/j.cie.2018.03.039.
Singh, D., and B. Singh. 2019. “Investigating the impact of data normalization on classification performance.” Appl. Soft Comput. 97 (Jun): 105524. https://doi.org/10.1016/j.asoc.2019.105524.
Son, B. Y., and E. B. Lee. 2019. “Using text mining to estimate schedule delay risk of 13 offshore oil and gas EPC case studies during the bidding process.” Energies 12 (10): 1956. https://doi.org/10.3390/en12101956.
Thakur, F., and F. Easmin. 2020. “Impact of supply chain risks on the completion for time of rural road projects in Bangladesh.” J. Project Manage. 5 (1): 17–26. https://doi.org/10.5267/j.jpm.2019.8.002.
Uddin, M. P., M. A. Mamun, and M. A. Hossain. 2020. “PCA-based feature reduction for hyperspectral remote sensing image classification.” IETE Tech. Rev. 38 (4): 377–396. https://doi.org/10.1080/02564602.2020.1740615.
Vapnik, V. N. 1999. “An overview of statistical learning theory.” IEEE Trans. Neural Network 10 (5): 988–999. https://doi.org/10.1109/72.788640.
Wang, C., Q. Shu, X. Wang, B. Guo, P. Liu, and Q. Li. 2018. “A random forest classifier based on pixel comparison features for urban LiDAR data.” ISPRS J. Photogramm. 148 (Feb): 75–86. https://doi.org/10.1016/j.isprsjprs.2018.12.009.
Xu, Y., Y. Zhou, P. Sekula, and L. Ding. 2021. “Machine learning in construction: From shallow to deep learning.” Dev. Built Environ. 6 (May): 100045. https://doi.org/10.1016/j.dibe.2021.100045.
Xue, H., and S. J. Zhang. 2018. “Relationships between engineering construction standards and economic growth in the construction industry: The case of China’s construction industry.” KSCE J. Civ. Eng. 22 (5): 1606–1613. https://doi.org/10.1007/s12205-017-1990-y.
Yang, J. B., M. Y. Chu, and K. M. Huang. 2013. “An empirical study of schedule delay causes based on Taiwan’s litigation cases.” Project Manage. J. 44 (3): 21–31. https://doi.org/10.1002/pmj.21337.
Yap, J. B. H., P. L. Goay, Y. B. Woon, and M. Skitmore. 2020. “Revisiting critical delay factors for construction: Analysing projects in Malaysia.” Alexandria Eng. J. 60 (1): 1717–1729. https://doi.org/10.1016/j.aej.2020.11.021.
Yaseen, Z. M., Z. H. Ali, S. Q. Salih, and N. Al-Ansari. 2020. “Prediction of risk delay in construction projects using a hybrid artificial intelligence model.” Sustainability 12 (4): 1514. https://doi.org/10.3390/su12041514.
Zhang, H., L. Jiang, and L. Yu. 2020a. “Class-specific attribute value weighting for Naive Bayes.” Inf. Sci. 508 (Jun): 260–274.
Zhang, T., and B. Yang. 2016. “November. Big data dimension reduction using PCA.” In Proc., IEEE Int. Conf. on Smart Cloud (SmartCloud), 152–157. New York: IEEE.
Zhang, Z., H. Han, X. Cui, and Y. Fan. 2020b. “Novel application of multi-model ensemble learning for fault diagnosis in refrigeration systems.” Appl. Therm. Eng. 164 (Jan): 114516. https://doi.org/10.1016/j.applthermaleng.2019.114516.
Zhao, H., J. Zheng, J. Xu, and W. Deng. 2019. “Fault diagnosis method based on principal component analysis and broad learning system.” IEEE Access 7 (Jul): 99263–99272. https://doi.org/10.1109/ACCESS.2019.2929094.
Zhao, X., R. Lovreglio, and D. Nilsson. 2020. “Modelling and interpreting pre-evacuation decision-making using machine learning.” Autom. Constr. 113 (May): 103140. https://doi.org/10.1016/j.autcon.2020.103140.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 149 • Issue 4 • April 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Feb 18, 2022
Accepted: Dec 7, 2022
Published online: Feb 3, 2023
Published in print: Apr 1, 2023
Discussion open until: Jul 3, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.