Evaluating Deterioration of Tunnels Using Computational Machine Learning Algorithms
Publication: Journal of Construction Engineering and Management
Volume 147, Issue 10
Abstract
Tunnels are an integrated part of the transportation infrastructure. Structural evaluation and inspection of tunnels are vital tasks to assess the deterioration of tunnels and maintain their level of service. Researchers have developed many predictive models that describe the deterioration of various infrastructure systems using data from formal inspections. However, there is a lack of research that developed predictive models of deterioration of tunnels in the US. Therefore, this paper investigated the feasibility of using various machine learning techniques to develop a computational data-driven decision support tool that predicts the deterioration of tunnels in the US. An ex ante framework for predicting the deterioration of tunnels in the US was developed. The research methodology comprised (1) collecting, cleaning, and standardizing data for tunnels in the US from the Federal Highway Administration (FHWA); (2) identifying the best subset of variables that allow predicting the deterioration of tunnels; (3) utilizing existing machine learning algorithms, namely -nearest neighbors (KNN), random forest (RF), artificial neural networks (ANNs), and support vector machine (SVM), to develop classification models that predict the deterioration of tunnels; (4) optimizing the accuracy of the developed models by determining the best set of hyperparameters that result in the most accurate performance; (5) comparing the performance of the developed models and selecting the best performing model to be used as a decision support tool; and (6) evaluating and validating the performance of the selected model. The results identified 18 variables that greatly affect the deterioration of tunnels, with the tunnel width having the greatest impact on the prediction of deterioration of tunnels. Results indicated that the RF algorithm reached an accuracy of 85.38%, which was the highest accuracy, compared with KNN, ANN, and SVM, which reached an accuracy of 80.12%, 56.14%, and 56.73%, respectively. In addition, the entropy criterion function with a maximum of five features and 500 estimators successfully constructed the best hyperparameters for the selected RF model. Therefore, the developed decision support tool can be used by transportation entities to estimate the overall condition of tunnels based on specific tunnel parameters with reasonable prediction accuracy. It also can aid decision makers in developing, optimizing, and prioritizing maintenance plans and allocation of funding.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
References
Adoko, A. C., and L. Wu. 2012. “Estimation of convergence of a high-speed railway tunnel in weak rocks using an adaptive neuro-fuzzy inference system (ANFIS) approach.” J. Rock Mech. Geotech. Eng. 4 (1): 11–18. https://doi.org/10.3724/SP.J.1235.2012.00011.
Agatonovic-Kustrin, S., and R. Beresford. 2000. “Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research.” J. Pharm. Biomed. Anal. 22 (5): 717–727. https://doi.org/10.1016/S0731-7085(99)00272-1.
Al-Aidaroos, K. M., A. A. Bakar, and Z. Othman. 2010. “Naive Bayes variants in classification learning.” In Proc., 2010 Int. Conf. on Information Retrieval & Knowledge Management (CAMP), 276–281. New York: IEEE.
Ali, G., A. Elsayegh, R. Assaad, I. H. El-adaway, and I. Abotaleb. 2019. “Artificial neural network model for bridge deterioration and assessment.” In Proc., Canadian Society for Civil Engineering (CSCE) Annual Conf. Montréal: Canadian Society for Civil Engineering.
Antoniadis, A., S. Lambert-Lacroix, and J. M. Poggi. 2021. “Random forests for global sensitivity analysis: A selective review.” Reliab. Eng. Syst. Saf. 206: 164–179. https://doi.org/10.1016/j.ress.2020.107312.
ASCE. 2021. “2021 report card for America’s infrastructure: A comprehensive assessment of America’s infrastructure.” Accessed March 3, 2021. https://infrastructurereportcard.org/wp-content/uploads/2020/12/National_IRC_2021-report.pdf.
Assaad, R., and I. H. El-adaway. 2020. “Bridge infrastructure asset management system: Comparative computational machine learning approach for evaluating and predicting deck deterioration conditions.” J. Infrastruct. Syst. 26 (3): 04020032. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000572.
Assaad, R., I. H. El-adaway, A. H. El Hakea, M. J. Parker, T. I. Henderson, C. R. Salvo, and M. O. Ahmed. 2020. “A contractual perspective for BIM utilization in US construction projects.” J. Constr. Eng. Manage. 146 (12): 04020128. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001927.
Aulia, A., D. Jeong, I. M. Saaid, D. Kania, M. T. Shuker, and N. A. El-Khatib. 2019. “A Random Forests-based sensitivity analysis framework for assisted history matching.” J. Pet. Sci. Eng. 181 (Oct): 106237. https://doi.org/10.1016/j.petrol.2019.106237.
Azeez, D., K. B. Gan, M. A. Ali, and M. S. Ismail. 2015. “Secondary triage classification using an ensemble random forest technique.” Technol. Health Care 23 (4): 419–428. https://doi.org/10.3233/THC-150907.
Baji, H., C.-Q. Li, S. Scicluna, and J. Dauth. 2017. “Risk-cost optimised maintenance strategy for tunnel structures.” Tunnelling Underground Space Technol. 69 (Oct): 72–84. https://doi.org/10.1016/j.tust.2017.06.008.
Bergstra, J., and Y. Bengio. 2012. “Random search for hyper-parameter optimization.” J. Mach. Learn. Res. 13 (2): 281–305.
Boubou, R., F. Emeriault, and R. Kastner. 2010. “Artificial neural network application for the prediction of ground surface movements induced by shield tunnelling.” Can. Geotech. J. 47 (11): 1214–1233. https://doi.org/10.1139/T10-023.
Bramer, M. 2007. Principles data mining. 4th ed. Berlin: Springer.
Brownlee, J. 2017. “Why one-hot encode data in machine learning?” Accessed March 1, 2021. https://machinelearningmastery.com/why-one-hot-encode-data-in-machine-learning/.
Chaiyasarn, K. 2014. “Damage detection and monitoring for tunnel inspection based on computer vision.” Ph.D. thesis, Dept. of Engineering, Univ. of Cambridge.
Chakrabarti, S. 2008. “Voltage stability monitoring by artificial neural network using a regression-based feature selection method.” Expert Syst. Appl. 35 (4): 1802–1808. https://doi.org/10.1016/j.eswa.2007.08.059.
Chauhan, N. S. 2020. “Introduction to artificial neural networks (ANN).” Accessed August 20, 2020. https://www.kdnuggets.com/2019/10/introduction-artificial-neural-networks.html.
Chen, J., K. Li, Z. Tang, K. Bilal, S. Yu, C. Weng, and K. Li. 2016. “A parallel random forest algorithm for big data in a spark cloud computing environment.” IEEE Trans. Parallel Distrib. Syst. 28 (4): 919–933. https://doi.org/10.1109/TPDS.2016.2603511.
Cui, S., P. Liu, Z. Li, X. Xu, and J. W. Ju. 2020. “Shotcrete performance-loss due to seepage and temperature coupling in cold-region tunnels.” Constr. Build. Mater. 246 (Jun): 118488. https://doi.org/10.1016/j.conbuildmat.2020.118488.
Dawood, T., Z. Zhu, and T. Zayed. 2018. “Computer vision–based model for moisture marks detection and recognition in subway networks.” J. Comput. Civ. Eng. 32 (2): 04017079. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000728.
Dei, M. 2019. “Catalog of variable transformations to make your model work better.” Accessed September 17, 2020. https://towardsdatascience.com/catalog-of-variable-transformations-to-make-your-model-works-better-7b506bf80b97#:∼:text=%2D%20Categorical%20Variable%20Transformation%3A%20is%20turning,can%20handle%20only%20numeric%20values.
Fernandez-Delgado, M., E. Cernadas, and S. Barro. 2014. “Do we need hundreds of classifiers to solve real world classification problems?” J. Mach. Learn. Res. 15 (90): 3133–3181.
FHWA (Federal Highway Administration). 2019. “Tunnel inspection—Safety—Bridges & structures—Federal Highway Administration.” Accessed September 23, 2019. https://www.fhwa.dot.gov/bridge/inspection/tunnel/inventory/download.cfm.
FHWA-NTI (Federal Highway Administration-National Tunnel Inventory). 2015. “Specifications for the National Tunnel Inventory.” Accessed September 23, 2019. https://www.fhwa.dot.gov/bridge/inspection/tunnel/snti/hif15006.pdf.
Gao, C., Z. Zhou, W. Yang, C. Lin, L. Li, and J. Wang. 2019. “Model test and numerical simulation research of water leakage in operating tunnels passing through intersecting faults.” Tunnelling Underground Space Technol. 94 (Dec): 103134. https://doi.org/10.1016/j.tust.2019.103134.
Gavilán, M., F. Sánchez, J. A. Ramos, and O. Marcos. 2013. “Mobile inspection system for high-resolution assessment of tunnels.” In Proc., 6th Int. Conf. on Structural Health Monitoring of Intelligent Infrastructure, 10. Hong Kong: International Society for Structural Health Monitoring of Intelligent Infrastructure.
Ghosal, V., P. Tikmani, and P. Gupta. 2009. “Face classification using Gabor wavelets and random forest.” In Proc., 2009 Canadian Conf. on Computer and Robot Vision, 68–73. New York: IEEE.
Ghosh, D., B. L. Midya, C. Koley, and P. Purkait. 2005. “Wavelet aided SVM analysis of ECG signals for cardiac abnormality detection.” In Proc., 2005 Annual IEEE India Conf. -Indicon, 9–13. New York: IEEE.
Guo, G., H. Wang, D. Bell, Y. Bi, and K. Greer. 2003. “KNN model-based approach in classification.” In Proc., OTM Confederated Int. Conf. “On the Move to Meaningful Internet Systems”, 986–996. New York: Springer.
Gupta, S. 2020. “Pros and cons of various classification Machine Learning algorithms.” Accessed August 20, 2020. https://towardsdatascience.com/pros-and-cons-of-various-classification-ml-algorithms-3b5bfb3c87d6.
Hammerla, N. Y., and T. Plötz. 2015. “Let’s (not) stick together: Pairwise similarity biases cross-validation in activity recognition.” In Proc., 2015 ACM Int. Joint Conf. on Pervasive and Ubiquitous Computing, 1041–1051. New York: Association for Computing Machinery.
Han, X., Y. Xia, F. Ye, and Y. Wang. 2020. “Ageing models and maintenance strategy for road tunnels.” Struct. Infrastruct. Eng. 16 (5): 831–846. https://doi.org/10.1080/15732479.2019.1670680.
Hou, K., G. Shao, H. Wang, L. Zheng, Q. Zhang, S. Wu, and W. Hu. 2018. “Research on practical power system stability analysis algorithm based on modified SVM.” Prot. Control Mod. Power Syst. 3 (1): 1–7. https://doi.org/10.1186/s41601-018-0086-0.
Hu, L.-Y., M.-W. Huang, S.-W. Ke, and C.-F. Tsai. 2016. “The distance function effect on k-nearest neighbor classification for medical datasets.” Springerplus 5 (1): 1304. https://doi.org/10.1186/s40064-016-2941-7.
Huang, W., Y. Nakamori, and S.-Y. Wang. 2005. “Forecasting stock market movement direction with support vector machine.” Comput. Oper. Res. 32 (10): 2513–2522. https://doi.org/10.1016/j.cor.2004.03.016.
Hunter, J. D. 2007. “Matplotlib: A 2D graphics environment.” Comput. Sci. Eng. 9 (3): 90–95. https://doi.org/10.1109/MCSE.2007.55.
Hwang, J.-H., and C.-C. Lu. 2007. “A semi-analytical method for analyzing the tunnel water inflow.” Tunnelling Underground Space Technol. 22 (1): 39–46. https://doi.org/10.1016/j.tust.2006.03.003.
Jiang, Y., X. Zhang, and T. Taniguchi. 2019. “Quantitative condition inspection and assessment of tunnel lining.” Autom. Constr. 102 (Jun): 258–269. https://doi.org/10.1016/j.autcon.2019.03.001.
Kim, C. Y., G. J. Bae, S. W. Hong, C. H. Park, H. K. Moon, and H. S. Shin. 2001. “Neural network based prediction of ground surface settlements due to tunnelling.” Comput. Geotech. J. 28 (6): 517–547. https://doi.org/10.1016/S0266-352X(01)00011-8.
Li, G., X. Zhao, K. Du, F. Ru, and Y. Zhang. 2017a. “Recognition and evaluation of bridge cracks with modified active contour model and greedy search-based support vector machine.” Autom. Constr. J. 78 (Jun): 51–61. https://doi.org/10.1016/j.autcon.2017.01.019.
Li, X., X. Lin, H. Zhu, X. Wang, and Z. Liu. 2017b. “Condition assessment of shield tunnel using a new indicator: The tunnel serviceability index.” Tunnelling Underground Space Technol. 67 (Aug): 98–106. https://doi.org/10.1016/j.tust.2017.05.007.
Liu, K., and N. El-Gohary. 2017. “Ontology-based semi-supervised conditional random fields for automated information extraction from bridge inspection reports.” Autom. Constr. 81 (Sep): 313–327. https://doi.org/10.1016/j.autcon.2017.02.003.
Liu, M., M. Wang, J. Wang, and D. Li. 2013. “Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar.” Sens. Actuators, B 177: 970–980. https://doi.org/10.1016/j.snb.2012.11.071.
Lo, W.-S., H.-W. Chiou, S.-C. Hsu, Y.-M. Lee, and L.-C. Cheng. 2019. “Learning based mesh generation for thermal simulation in handheld devices with variable power consumption.” In Proc., 2019 18th IEEE Intersociety Conf. on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm), 7–12. New York: IEEE.
Mahanta, J. 2017. “Introduction to neural networks, advantages and applications.” Accessed August 22, 2020. https://towardsdatascience.com/introduction-to-neural-networks-advantages-and-applications-96851bd1a207.
Mahdevari, S., and S. R. Torabi. 2012. “Prediction of tunnel convergence using artificial neural networks.” Tunnelling Underground Space Technol. 28: 218–228. https://doi.org/10.1016/j.tust.2011.11.002.
Malik, F. 2020. “What is grid search?” Accessed March 1, 2021. https://medium.com/fintechexplained/what-is-grid-search-c01fe886ef0a.
McKinney, W. 2010. “Data structures for statistical computing in Python.” In Proc., 9th Python in Science Conf., 51–56. Austin, TX: Enthought.
Menendez, E., J. G. Victores, R. Montero, S. Martínez, and C. Balaguer. 2018. “Tunnel structural inspection and assessment using an autonomous robotic system.” Autom. Constr. 87 (Mar): 117–126. https://doi.org/10.1016/j.autcon.2017.12.001.
Michael, N. 2005. Artificial intelligence a guide to intelligent systems. London: Pearson Education.
Millman, K. J., and M. Aivazis. 2011. “Python for scientists and engineers.” Comput. Sci. Eng. 13 (2): 9–12. https://doi.org/10.1109/MCSE.2011.36.
Molnar, C. 2020. “Interpretable machine learning.” Accessed March 1, 2021. https://christophm.github.io/interpretable-ml-book/.
Moosazadeh, S., E. Namazi, H. Aghababaei, A. Marto, H. Mohamad, and M. Hajihassani. 2019. “Prediction of building damage induced by tunnelling through an optimized artificial neural network.” Eng. Comput. 35 (2): 579–591. https://doi.org/10.1007/s00366-018-0615-5.
Nair, A., C. S. Cai, and X. Kong. 2019. “Studying failure modes of GFRP laminate coupons using AE pattern-recognition method.” J. Aerosp. Eng. 32 (4): 04019031. https://doi.org/10.1061/(ASCE)AS.1943-5525.0001015.
Nembrini, S., I. R. König, and M. N. Wright. 2018. “The revival of the Gini importance?” Bioinformatics 34 (21): 3711–3718. https://doi.org/10.1093/bioinformatics/bty373.
Nguyen, T. T., and K. Dinh. 2019. “Prediction of bridge deck condition rating based on artificial neural networks.” J. Sci. Technol. Civ. Eng. NUCE 13 (3): 15–25.
Okun, O., and H. Priisalu. 2007. “Random forest for gene expression based cancer classification: Overlooked issues.” In Proc., Iberian Conf. on Pattern Recognition and Image Analysis, 483–490. New York: Springer.
Oliphant, T. E. 2006. A guide to NumPy. Spanish Fork, UT: Trelgol.
Oliphant, T. E. 2007. “Python for scientific computing.” Comput. Sci. Eng. 9 (3): 10–20. https://doi.org/10.1109/MCSE.2007.58.
Oshiro, T. M., P. S. Perez, and J. A. Baranauskas. 2012. “How many trees in a random forest?” In Proc., Int. Workshop on Machine Learning and Data Mining in Pattern Recognition, 154–168. New York: Springer.
Parvin, H., H. Alizadeh, and B. Minaei-Bidgoli. 2008 “MKNN: Modified k-nearest neighbor.” In Vol. 1 of Proc., World Congress on Engineering and Computer Science. Hong Kong: International Association of Engineers.
Patle, A., and D. S. Chouhan. 2013. “SVM kernel functions for classification.” In Proc., 2013 Int. Conf. on Advances in Technology and Engineering (ICATE), 1–9. New York: IEEE.
Pedregosa, F., et al. 2011. “Scikit-learn: Machine learning in Python.” J. Mach. Learn. Res. 12: 2825–2830.
Pesantez-Narvaez, J., M. Guillen, and M. Alcañiz. 2019. “Predicting motor insurance claims using telematics data—XGBoost versus logistic regression.” Risks 7 (2): 70. https://doi.org/10.3390/risks7020070.
Prasanna, P., K. J. Dana, N. Gucunski, B. B. Basily, H. M. La, R. S. Lim, and H. Parvardeh. 2016. “Automated crack detection on concrete bridges.” IEEE Trans. Autom. Sci. Eng. 13 (2): 591–599. https://doi.org/10.1109/TASE.2014.2354314.
Protopapadakis, E., A. Voulodimos, A. Doulamis, N. Doulamis, and T. Stathaki. 2019. “Automatic crack detection for tunnel inspection using deep learning and heuristic image post-processing.” Appl. Intell. 49 (7): 2793–2806. https://doi.org/10.1007/s10489-018-01396-y.
Rafai, H., and M. Moosavi. 2012. “An approximate ANN-based solution for convergence of lined circular tunnels in elasto-plastic rock masses with anisotropic stresses.” Tunnelling Underground Space Technol. 27 (1): 52–59.
Rajpoot, K., and N. Rajpoot. 2004. “SVM optimization for hyperspectral colon tissue cell classification.” In Proc., Int. Conf. on Medical Image Computing and Computer-Assisted Intervention, 829–837. New York: Springer.
Reddy, C., P. Balasubramanyam, and M. Subbarayudu. 2013. “An effective approach to resolve multicollinearity in agriculture data.” Int. J. Res. Electron. Comput. Eng. 1 (1): 27–30.
Richards, J. A. 1998. “Inspection, maintenance and repair of tunnels: International lessons and practice.” Tunnelling Underground Space Technol. 13 (4): 369–375. https://doi.org/10.1016/S0886-7798(98)00079-0.
Sargent, D. J. 2001. “Comparison of artificial neural networks with other statistical approaches: Results from medical data sets.” Cancer: Interdiscip. Int. J. Am. Cancer Soc. 91 (S8): 1636–1642. https://doi.org/10.1002/1097-0142(20010415)91:8+%3C1636::AID-CNCR1176%3E3.0.CO;2-D.
Sathyadevan, S., and R. R. Nair. 2015. “Comparative analysis of decision tree algorithms: ID3, C4.5 and random forest.” Comput. Intell. Data Min. 1: 549–562.
Scikit-learn. 2019. “User guide.” Accessed September 13, 2020. https://scikit-learn.org/stable/user_guide.html.
Sharma, V., D. Baruah, D. Chutia, P. Raju, and D. Bhattacharya. 2016. “An assessment of support vector machine kernel parameters using remotely sensed satellite data.” In Proc., IEEE Int. Conf. on Recent Trends in Electronics Information Communication Technology. New York: IEEE.
Shi, Y., L. Cui, Z. Qi, F. Meng, and Z. Chen. 2016. “Automatic road crack detection using random structured forests.” IEEE Trans. Intell. Transp. Syst. 17 (12): 3434–3445. https://doi.org/10.1109/TITS.2016.2552248.
Showkati, A., H. Salari-rad, and M. H. Aghchai. 2021. “Predicting long-term stability of tunnels considering rock mass weathering and deterioration of primary support.” Tunnelling Underground Space Technol. 107 (Jan): 103670. https://doi.org/10.1016/j.tust.2020.103670.
Sözen, A., T. Menlik, and S. Ünvar. 2008. “Determination of efficiency of flat-plate solar collectors using neural network approach.” Expert Syst. Appl. 35 (4): 1533–1539. https://doi.org/10.1016/j.eswa.2007.08.080.
Speiser, J. L., M. E. Miller, J. Tooze, and E. Ip. 2019. “A comparison of random forest variable selection methods for classification prediction modeling.” Expert Syst. Appl. 134 (Nov): 93–101. https://doi.org/10.1016/j.eswa.2019.05.028.
Svetnik, V., A. Liaw, C. Tong, J. C. Culberson, R. P. Sheridan, and B. P. Feuston. 2003. “Random forest: A classification and regression tool for compound classification and QSAR modeling.” J. Chem. Inf. Comput. Sci. 43 (6): 1947–1958. https://doi.org/10.1021/ci034160g.
Syarif, I., A. Prugel-Bennett, and G. Wills. 2016. “SVM parameter optimization using grid search and genetic algorithm to improve classification performance.” Telkomnika 14 (4): 1502–1509. https://doi.org/10.12928/telkomnika.v14i4.3956.
Taghaddos, M., and Y. Mohamed. 2019. “Predicting bridge conditions in Ontario: A case study.” In Vol. 36 of Proc., ISARC, Proc., Int. Symp. on Automation and Robotics in Construction, 166–171. London: International Association for Automation and Robotics in Construction.
Topak, F., M. K. Pekeriçli, and A. M. Tanyer. 2018. “Technological viability assessment of Bluetooth low energy technology for indoor localization.” J. Comput. Civ. Eng. 32 (5): 04018034. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000778.
van der Walt, S., S. C. Colbert, and G. Varoquaux. 2011. “The NumPy array: A structure for efficient numerical computation.” Comput. Sci. Eng. 13 (2): 22. https://doi.org/10.1109/MCSE.2011.37.
Vincent, D. R., N. Deepa, D. Elavarasan, K. Srinivasan, S. H. Chauhdary, and C. Iwendi. 2019. “Sensors driven AI-based agriculture recommendation model for assessing land suitability.” Sensors 19 (17): 3667. https://doi.org/10.3390/s19173667.
Wang, S., Y. Wang, D. Wang, Y. Yin, Y. Wang, and Y. Jin. 2020. “An improved random forest-based rule extraction method for breast cancer diagnosis.” Appl. Soft Comput. 86 (Jan): 105941. https://doi.org/10.1016/j.asoc.2019.105941.
Wang, W., P. H. A. J. M. Gelder, J. K. Vrijling, and J. Ma. 2006. “Forecasting daily streamflow using hybrid ANN models.” J. Hydrol. 324 (1): 383–399. https://doi.org/10.1016/j.jhydrol.2005.09.032.
Xue, B., M. Zhang, W. N. Browne, and X. Yao. 2016. “A survey on evolutionary computation approaches to feature selection.” IEEE Trans. Evol. Comput. 20 (4): 606–626. https://doi.org/10.1109/TEVC.2015.2504420.
Yang, M., H. Xu, D. Zhu, and H. Chen. 2012. “Visualizing the random forest by 3D techniques.” In Internet of things, 639–645. Berlin: Springer.
Yao, B.-Z., C.-Y. Yang, J.-B. Yao, and J. Sun. 2010. “Tunnel surrounding rock displacement prediction using support vector machine.” Int. J. Comput. Intell. Syst. 3 (6): 843–852. https://doi.org/10.1080/18756891.2010.9727746.
Ye, F., N. Qin, X. Liang, A. Ouyang, Z. Qin, and E. Su. 2021. “Analyses of the defects in highway tunnels in China.” Tunnelling Underground Space Technol. 107 (Jan): 103658. https://doi.org/10.1016/j.tust.2020.103658.
Zhang, Z. 2016. “A gentle introduction to artificial neural networks.” Ann. Transl. Med. 4 (19): 1–6. https://doi.org/10.21037/atm.2016.06.20.
Zhou, Z.-H., Y. Jiang, Y.-B. Yang, and S.-F. Chen. 2002. “Lung cancer cell identification based on artificial neural network ensembles.” Artif. Intell. Med. 24 (1): 25–36. https://doi.org/10.1016/S0933-3657(01)00094-X.
Zhu, M., H. Zhu, F. Guo, X. Chen, and J. W. Ju. 2021. “Tunnel condition assessment via cloud model-based random forests and self-training approach.” Comput.-Aided Civ. Infrastruct. Eng. 36 (2): 164–179. https://doi.org/10.1111/mice.12601.
Information & Authors
Information
Published In
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Oct 26, 2020
Accepted: Jun 8, 2021
Published online: Aug 3, 2021
Published in print: Oct 1, 2021
Discussion open until: Jan 3, 2022
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.
Cited by
- Islam H. El-adaway, Gasser G. Ali, Muaz O. Ahmed, Radwa Eissa, Mohamad Abdul Nabi, Tamima Elbashbishy, Ramy Khalef, Predicting High-Impact Research in the Construction Engineering and Management Domain Using Computational Machine Learning, Computing in Civil Engineering 2023, 10.1061/9780784485231.079, (663-671), (2024).
- Ramy Khalef, Islam El-adaway, Understanding of Risky Determinants Affecting the Success of Design-Build Airport Projects Using Computational Clustering Techniques, Computing in Civil Engineering 2023, 10.1061/9780784485224.065, (539-546), (2024).
- Islam H. El-adaway, Gasser G. Ali, Radwa Eissa, Mohamad Abdul Nabi, Muaz O. Ahmed, Tamima Elbashbishy, Ramy Khalef, Journal of construction management and economics 40th anniversary: investigating knowledge structure and evolution of research trends, Construction Management and Economics, 10.1080/01446193.2022.2164598, (1-23), (2023).
- Kerim Koc, Role of National Conditions in Occupational Fatal Accidents in the Construction Industry Using Interpretable Machine Learning Approach, Journal of Management in Engineering, 10.1061/JMENEA.MEENG-5516, 39, 6, (2023).
- Ramy Khalef, Islam H. El-adaway, Advancing Airport Project Delivery: A Comparison of Design–Build and Traditional Methods in Terms of Schedule and Cost Performance, Journal of Management in Engineering, 10.1061/JMENEA.MEENG-5490, 39, 6, (2023).
- Huu Tran, Dilan Robert, Prageeth Gunarathna, Sujeeva Setunge, Stochastic Prediction of Road Network Degradation Based on Field Monitoring Data, Journal of Construction Engineering and Management, 10.1061/JCEMD4.COENG-13293, 149, 10, (2023).
- Ramy Khalef, Islam H. El-adaway, Identifying Design-Build Decision-Making Factors and Providing Future Research Guidelines: Social Network and Association Rule Analysis, Journal of Construction Engineering and Management, 10.1061/(ASCE)CO.1943-7862.0002431, 149, 1, (2023).
- Wei Lin, Pan Li, Xiongyao Xie, A Novel Detection and Assessment Method for Operational Defects of Pipe Jacking Tunnel Based on 3D Longitudinal Deformation Curve: A Case Study, Sensors, 10.3390/s22197648, 22, 19, (7648), (2022).
- Muaz O. Ahmed, Islam H. El-adaway, An integrated game-theoretic and reinforcement learning modeling for multi-stage construction and infrastructure bidding, Construction Management and Economics, 10.1080/01446193.2022.2124528, 41, 3, (183-207), (2022).
- Muaz O. Ahmed, Islam H. El-Adaway, Kalyn Coatney, Mitigating the Winner’s Curse Dilemma in Multi-Stage Construction Bidding, Construction Research Congress 2022, 10.1061/9780784483978.085, (835-844), (2022).
- See more