The Evolution of Classical and Soft Computing Methods in Predicting Road Maintenance and Repair Costs: Approaches in the Literature and Future Perspectives
DOI:
https://doi.org/10.7250/bjrbe.2025-20.662Keywords:
artificial neural networks, hybrid models, LASSO, narrative review, ridge, repair costs, road maintenance, soft computing techniques, sustainable infrastructure managementAbstract
Road infrastructure is critical to the economic, social, and environmental sustainability of modern societies. This study compares classical methods (Multiple Linear Regression, Ridge, and LASSO) with soft computing techniques (Artificial Neural Networks, Fuzzy Logic, Random Forests, Gradient Boosting, Support Vector Machines, and Genetic Algorithms) for predicting road maintenance and repair costs. A comprehensive search has been conducted in Web of Science, and Scopus for studies published between January 2010 and March 2024. Boolean operators and specific key terms such as “road maintenance costs,” “soft computing,” and “classical prediction methods” have been used. The approach has been PRISMA-inspired but adapted for narrative review purposes; hence, no formal quality assessment or meta-analysis has been performed. Peer-reviewed journal articles have been included, while grey literature has been excluded to ensure methodological consistency. While classical methods offer simplicity and computational efficiency, they often fall short in addressing complex data structures such as non-linear relationships and multicollinearity. Conversely, soft computing techniques excel in modelling non-linear systems and managing uncertainties. Hybrid models combining classical and soft computing approaches enhance prediction accuracy by 20–30%, providing improved capabilities in modelling environmental factors. However, further research is required to evaluate their long-term performance and adaptability to diverse geographical conditions. This study highlights the theoretical advantages of hybrid models while offering practical solutions for sustainable infrastructure management. The findings provide policymakers and engineers with actionable insights, promoting efficient public resource use and sustainable development goals. Future research should focus on integrating IoT and big data analytics to address dynamic environmental variables, fostering innovation in infrastructure management.
References
Abdulhafedh, A. (2022). Modeling vehicle crash frequency when multicollinearity exists in vehicle crash data: ridge regression versus ordinary least squares linear regression. Open Access Library Journal, 9(6), 1–17. DOI: https://doi.org/10.4236/oalib.1108873
Abed, Y. G., Hasan, T. M., & Zehawi, R. N. (2022). Cost prediction for roads construction using machine learning models. International Journal of Electrical and Computer Engineering Systems, 13(10), 927–936. https://doi.org/10.32985/ijeces.13.10.8 DOI: https://doi.org/10.32985/ijeces.13.10.8
Adefabi, A., Olisah, S., Obunadike, C., Oyetubo, O., Taiwo, E., & Tella, E. (2023). Predicting accident severity: an analysis of factors affecting accident severity using random forest model. arXiv preprint 2310.05840. https://doi.org/10.48550/arXiv.2310.05840 DOI: https://doi.org/10.5121/ijci.2023.120609
Ahmad, A. (2023). Analyzing the highway failure and their maintenance: A case study. International Journal of Scientific Research in Engineering and Management, 7. https://doi.org/10.55041/IJSREM24615 DOI: https://doi.org/10.55041/IJSREM24615
Alin, A. (2010). Multicollinearity. WIREs Computational Statistics, 2(3), 370–374. https://doi.org/10.1002/wics.84 DOI: https://doi.org/10.1002/wics.84
AlKheder, S. (2022). Environment-friendly recycled asphalt pavement design for road maintenance applications. Environment, Development and Sustainability, 24, 14210–14234. https://doi.org/10.1007/s10668-021-02027-2 DOI: https://doi.org/10.1007/s10668-021-02027-2
Aloraini, A. (2017). On the prediction accuracies of three most known regularizers: Ridge regression, the LASSO estimate, and elastic net regularization methods. International Journal of Artificial Intelligence and Applications, 8(6), 29–36. https://doi.org/10.5121/IJAIA.2017.8603 DOI: https://doi.org/10.5121/ijaia.2017.8603
Altelbany, S. (2021). Evaluation of ridge, elastic net and lasso regression methods in precedence of multicollinearity problem: a simulation study. Journal of Applied Economics and Business Studies, 5(1), 131–142. https://doi.org/10.34260/jaebs.517 DOI: https://doi.org/10.34260/jaebs.517
Al-Zwainy, F. M., & Aidan, I. A. A. (2017). Forecasting the cost of structure of infrastructure projects utilizing artificial neural network model (highway projects as case study). Indian Journal of Science and Technology, 10(20), 1–12. DOI: https://doi.org/10.17485/ijst/2017/v10i19/108567
Annapoorna, E., Sujil, S. V., & Gopakumar, G. (2023, November). ADMM: Computational Analysis analysis of Lasso and Ridge on Fraud fraud Detectiondetection. 2023 2nd International Conference on Futuristic Technologies (INCOFT), Belagavi, Karnataka, India, (pp. 1–6). https://doi.org/10.1109/INCOFT60753.2023.10425471
Arshad, A. K., Shaffie, E., Kamal, M. I. A., Hussain, M. Z., Rais, N. M. (2022). Application of pavement evaluation for road maintenance and rehabilitation. R. Hassan, R., et al. (Eds.), Green Infrastructure (pp. 159–177). Springer. https://doi.org/10.1007/978-981-16-6383-3_10 DOI: https://doi.org/10.1007/978-981-16-6383-3_10
Atapattu, C. N., Domingo, N. D., & Sutrisna, M. (2022, November). Statistical cost modelling for preliminary stage cost estimation of infrastructure projects. IOP Conference Series Earth and Environmental Science, 1101(5), 052031. https://doi.org/10.1088/1755-1315/1101/5/052031 DOI: https://doi.org/10.1088/1755-1315/1101/5/052031
Awwad, M. (2021). Studying the effects of roads geometry and design parameters on the pavement drainage system. Civil Engineering Journal, 7(1), 49–58. https://doi.org/10.28991/cej-2021-03091636 DOI: https://doi.org/10.28991/cej-2021-03091636
Bamigboye, G. O., Bassey, D. E., Olukanni, D. O., Ngene, B. U., Adegoke, D., Odetoyan, A. O., Kareem, M. A., ...Enabulele, D. O., & Nworgu, A. T. (2021). Waste materials in highway applications: An overview on generation and utilization implications on sustainability. Journal of Cleaner Production, 283, 124581. https://doi.org/10.1016/j.jclepro.2020.124581 DOI: https://doi.org/10.1016/j.jclepro.2020.124581
Barakat, S. (2023). Review of classical methods and variables selection in case of multicollinearity: A case study with real-data. Trade and financing, 4(43), 225–246. https://doi.org/10.21608/caf.2023.336191 DOI: https://doi.org/10.21608/caf.2023.336191
Barua, L., Zou, B., Noruzoliaee, M., & Derrible, S. (2021). A gradient boosting approach to understanding airport runway and taxiway pavement deterioration. International Journal of Pavement Engineering, 22(13), 1673–1687. https://doi.org/10.1080/10298436.2020.1714616 DOI: https://doi.org/10.1080/10298436.2020.1714616
Biau, G., & Scornet, E. (2016). A random forest guided tour. Test, 25, 197–227. https://doi.org/10.1007/s11749-016-0481-7 DOI: https://doi.org/10.1007/s11749-016-0481-7
Bouabaz, M., & Hamami, M. (2008). A cost estimation model for repair bridges based on artificial neural network. American Journal of Applied Sciences, 5(4), 334–339. https://doi.org/10.3844/ajassp.2008.334.339 DOI: https://doi.org/10.3844/ajassp.2008.334.339
Cabrera, M. S., & Silva, D. L. (2023, June). Neural network-based prediction of the performance budget for road defects and highway maintenance. 2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI), Xiamen, China, (pp. 63–67). https://doi.org/10.1109/SEAI59139.2023.10217472 DOI: https://doi.org/10.1109/SEAI59139.2023.10217472
Chan, J. Y. L., Leow, S. M. H., Bea, K. T., Cheng, W. K., Phoong, S. W., Hong, Z. W., & Chen, Y. L. (2022). Mitigating the multicollinearity problem and its machine learning approach: a review. Mathematics, 10(8), 1283. https://doi.org/10.3390/math10081283 DOI: https://doi.org/10.3390/math10081283
Chen, J. C., & Wang, X. A. (2017, December). Development of fuzzy logic and genetic fuzzy commodity price prediction systems – An industrial case study. 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore, (pp. 432–435). https://doi.org/10.1109/IEEM.2017.8289927 DOI: https://doi.org/10.1109/IEEM.2017.8289927
Costanzi, M., & Cebon, D. (2007). An investigation of the effects of lorry suspension performance on road maintenance costs. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 221(11), 1265–1277. https://doi.org/10.1243/09544062JMES639 DOI: https://doi.org/10.1243/09544062JMES639
Daoud, J. I. (2017, December). Multicollinearity and regression analysis. Journal of Physics: Conference Series, 949(1), 012009. https://doi.org/10.1088/1742-6596/949/1/012009 DOI: https://doi.org/10.1088/1742-6596/949/1/012009
de Sá Ferreira, F. (2019). The impact of fuzzy logic on logistics optimization: A comprehensive review. Ciências Sociais Aplicadas, 23. https://doi.org/10.69849/revistaft/ar10201901011035 DOI: https://doi.org/10.69849/revistaft/ar10201901011035
Dugalam, R., & Prakash, G. (2024). Development of a random forest based algorithm for road health monitoring. Expert Systems with Applications, 251, Article 123940. https://doi.org/10.1016/j.eswa.2024.123940 DOI: https://doi.org/10.1016/j.eswa.2024.123940
Efeizomor, R. O. (2023). A comparative study of methods of remedying multicolinearity. American Journal of Theoretical and Applied Statistics, 14(1), 87–91. https://doi.org/10.11648/j.ajtas.20231204.14 DOI: https://doi.org/10.11648/j.ajtas.20231204.14
El-Sawalhi, N. I. (2015). Support vector machine cost estimation model for road projects. Journal of Civil Engineering and Architecture, 9(9), 1115–1125. https://www.davidpublisher.com/Public/uploads/Contribute/561c6f5db2dff.pdf DOI: https://doi.org/10.17265/1934-7359/2015.09.012
Etemadi, S., & Khashei, M. (2021). Etemadi multiple linear regression. Measurement, 186, 110080. https://doi.org/10.1016/j.measurement.2021.110080 DOI: https://doi.org/10.1016/j.measurement.2021.110080
Fassa, F. (2024, May). Green infrastructure issues 2016–2021: Literature review. AIP Conference Proceedings, 2891(1), 050014. https://doi.org/10.1063/5.0200958 DOI: https://doi.org/10.1063/5.0200958
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451 DOI: https://doi.org/10.1214/aos/1013203451
Garg, A., & Tai, K. (2013). Comparison of statistical and machine learning methods in modelling of data with multicollinearity. International Journal of Modelling, Identification and Control, 18(4), 295–312. https://doi.org/10.1504/IJMIC.2013.053535 DOI: https://doi.org/10.1504/IJMIC.2013.053535
Georgiou, P., Plati, C., & Loizos, A. (2018). Soft computing models to predict pavement roughness: A comparative study. Advances in Civil Engineering, 2018(1), 5939806. https://doi.org/10.1155/2018/5939806 DOI: https://doi.org/10.1155/2018/5939806
Gibby, R., Kitamura, R., & Zhao, H. (1990). Evaluation of truck impacts on pavement maintenance cost. Transportation Research Record, 1262, 48–56. https://onlinepubs.trb.org/Onlinepubs/trr/1990/1262/1262-006.pdf
Goldberg, D. E. (2013). Genetic algorithms. Pearson education India.
Grefenstette, J. J. (Ed). (2014). Proceedings of the First International Conference on Genetic Algorithms and their Applications. Psychology Press. DOI: https://doi.org/10.4324/9781315799674
Guelman, L. (2012). Gradient boosting trees for auto insurance loss cost modeling and prediction. Expert Systems with Applications, 39(3), 3659–3667. https://doi.org/10.1016/j.eswa.2011.09.058 DOI: https://doi.org/10.1016/j.eswa.2011.09.058
Hamdy, A. (2014). Genetic fuzzy system for enhancing software estimation models. International Journal of Modeling and Optimization, 4(3), 227–232. https://doi.org/10.7763/IJMO.2014.V4.378 DOI: https://doi.org/10.7763/IJMO.2014.V4.378
Hapfelmeier, A., & Ulm, K. (2013). A new variable selection approach using Random Forests. Computational Statistics & Data Analysis, 60, 50–69. https://doi.org/10.1016/j.csda.2012.09.020 DOI: https://doi.org/10.1016/j.csda.2012.09.020
Hashem, H. A. (2022). A comparative study of some variables selection methods in high dimensional multiple liner regression via simulation. Journal of Duhok University, 25(2), 601–613. https://doi.org/10.26682/sjuod.2022.25.2.53 DOI: https://doi.org/10.26682/sjuod.2022.25.2.53
Herawati, N. N., Wijayanti, N. A., Sutrisno, N. A., Nusyirwan, N., & Misgiyati, N. (2024). The performance of Ridge Regression, LASSO, and Elastic-Net in controlling multicollinearity: a simulation and application. Journal of Modern Applied Statistical Methods, 23(2). https://doi.org/10.56801/jmasm.v23.i2.4 DOI: https://doi.org/10.56801/Jmasm.V23.i2.4
Hou, Y., Edara, P., & Sun, C. (2012, September). A genetic fuzzy system for modeling mandatory lane changing. 2012 15th International IEEE Conference on Intelligent Transportation Systems (pp. 1044–1048). https://doi.org/10.1109/ITSC.2012.6338877 DOI: https://doi.org/10.1109/ITSC.2012.6338877
Hu, Y. H., Yu, S. C., Qi, X., Zheng, W. J., Wang, Q. Q., & Yao, H. Y. (2019). An overview of multiple linear regression model and its application. Zhonghua yu Fang yi xue za zhi [Chinese Journal of Preventive Medicine], 53(6), 653–656.
Inyim, P., Pereyra, J., Bienvenu, M., & Mostafavi, A. (2016). Environmental assessment of pavement infrastructure: A systematic review. Journal of Environmental Management, 176, 128–138. https://doi.org/10.1016/j.jenvman.2016.03.042 DOI: https://doi.org/10.1016/j.jenvman.2016.03.042
Kilinc, B., Aşikgil, B., Erar, A., & Yazici, B. (2016). Variable selection with genetic algorithm and multivariate adaptive regression splines in the presence of multicollinearity. International Journal of Advanced and Applied Sciences, 3(12), 26–31. https://doi.org/10.21833/ijaas.2016.12.004 DOI: https://doi.org/10.21833/ijaas.2016.12.004
Koukol, M., Zajíčková, L., Marek, L., & Tuček, P. (2015). Fuzzy logic in traffic engineering: a review on signal control. Mathematical Problems in Engineering, 2015(1), Article 979160. https://doi.org/10.1155/2015/979160 DOI: https://doi.org/10.1155/2015/979160
Kumar, P., & Imam, B. (2013). Footprints of air pollution and changing environment on the sustainability of built infrastructure. Science of the Total Environment, 444, 85–101. https://doi.org/10.1016/j.scitotenv.2012.11.056 DOI: https://doi.org/10.1016/j.scitotenv.2012.11.056
Lagari, P. L., Tsoukalas, L. H., Safarkhani, S., & Lagaris, I. E. (2021). Eliminating multicollinearity issues in neural network ensembles: Incremental, negatively correlated, optimal convex blending. arXiv preprint 2104.14715. https://doi.org/10.48550/arXiv.2104.14715
Li, J., Yin, G., Wang, X., & Yan, W. (2022). Automated decision making in highway pavement preventive maintenance based on deep learning. Automation in Construction, 135, 104111. https://doi.org/10.1016/j.autcon.2021.104111 DOI: https://doi.org/10.1016/j.autcon.2021.104111
Li, Z., & Burgueño, R. (2010). Using soft computing to analyze inspection results for bridge evaluation and management. Journal of Bridge Engineering, 15(4), 430–438. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000072 DOI: https://doi.org/10.1061/(ASCE)BE.1943-5592.0000072
Li, Z., Lang, L., Sun, G., Cai, Z., & Luo, Z. (2023, December). Enhancing multiple linear regression for price prediction: A PCA-integrated approach. 2023 4th International Conference on Computer, Big Data and Artificial Intelligence (ICCBD+ AI), Guiyang, China, (pp. 337–341). https://doi.org/10.1109/ICCBD-AI62252.2023.00063 DOI: https://doi.org/10.1109/ICCBD-AI62252.2023.00063
Li, Z., Zheng, Z., & Washington, S. (2019). Short-term traffic flow forecasting: A component-wise gradient boosting approach with hierarchical reconciliation. IEEE Transactions on Intelligent Transportation Systems, 21(12), 5060–5072. https://doi.org/10.1109/TITS.2019.2948381 DOI: https://doi.org/10.1109/TITS.2019.2948381
Liljenström, C., Björklund, A., & Toller, S. (2022). Including maintenance in life cycle assessment of road and rail infrastructure— a literature review. The International Journal of Life Cycle Assessment, 27(2), 316–341. https://doi.org/10.1007/s11367-021-02012-x DOI: https://doi.org/10.1007/s11367-021-02012-x
Lu, P., Zheng, Z., Ren, Y., Zhou, X., Keramati, A., Tolliver, D., & Huang, Y. (2020). A gradient boosting crash prediction approach for highway-rail grade crossing crash analysis. Journal of Advanced Transportation, 2020(1), 6751728. https://doi.org/10.1155/2020/6751728 DOI: https://doi.org/10.1155/2020/6751728
Mahmood, S. H. (2024). Estimating models and evaluating their efficiency under multicollinearity in multiple linear regression: A comparative study. Zanco Journal of Human Sciences, 28(5), 264–277. https://doi.org/10.21271/zjhs.28.5.17 DOI: https://doi.org/10.21271/zjhs.28.5.17
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and machine learning forecasting methods: Concerns and ways forward. PlOS One, 13(3), e0194889. https://doi.org/10.1371/journal.pone.0194889 DOI: https://doi.org/10.1371/journal.pone.0194889
Manogaran, G., Rodrigues, J. J., Kozlov, S. A., & Manokaran, K. (20212022). Conditional sup- port-vector-machine-based shared adaptive computing model for smart city traffic management. IEEE Transactions on Computational Social Systems, 9(1), 174–183. https://doi.org/10.1109/TCSS.2021.3051330 DOI: https://doi.org/10.1109/TCSS.2021.3051330
Menguc, K., Aydin, N., & Yilmaz, A. (2023). A data driven approach to forecasting traffic speed classes using extreme gradient boosting algorithm and graph theory. Physica A: Statistical Mechanics and its Applications, 620, 128738. https://doi.org/10.1016/j.physa.2023.128738 DOI: https://doi.org/10.1016/j.physa.2023.128738
Morris, J. D., & Lieberman, M. G. (2018). Multicollinearity’s effect on regression prediction accuracy with real data structures. General Linear Model Journal, 44(1), 29–34. https://doi.org/10.31523/glmj.044001.004 DOI: https://doi.org/10.31523/glmj.044001.004
Nabil, M., Aly, M., & Atiya, A. (2014). LABR: A large scale arabic sentiment analysis benchmark. arXiv preprint 1411.6718. https://doi.org/10.48550/arXiv.1411.6718.
Narayanan, R. M., Annapoorna, E., Sujil, S. V., & Gopakumar, G. (2023, November). ADMM: Computational analysis of Lasso and Ridge on fraud detection. 2023 2nd International Conference on Futuristic Technologies (INCOFT), Belagavi, Karnataka, India, (pp. 1–6). https://doi.org/10.1109/INCOFT60753.2023.10425471 DOI: https://doi.org/10.1109/INCOFT60753.2023.10425471
Naveed, S. A., Farooq, U., Raza, M. A., Rehman, Z. U., Saleem, M., & Ghazal, T. M. (2024). Enhancing traffic flow and congestion management in smart cities utilizing SVM-based linear regression approach. International Journal of Advanced and Applied Sciences, 11(10), 166–175. https://doi.org/10.21833/ijaas.2024.10.019 DOI: https://doi.org/10.21833/ijaas.2024.10.019
Nyame, M. (2024). Impact of geotechnical engineering on infrastructure lifespan and maintenance costs. Journal of Scientific Research and Reports, 30(9), 217–226. https://doi.org/10.9734/jsrr/2024/v30i92346 DOI: https://doi.org/10.9734/jsrr/2024/v30i92346
Obite, C. P., Olewuezi, N. P., Ugwuanyim, G. U., & Bartholomew, D. C. (2020). Multicollinearity effect in regression analysis: A feed forward artificial neural network approach. Asian Journal of Probability and Statistics, 6(1), 22–33. https://doi.org/10.9734/ajpas/2020/v6i130151 DOI: https://doi.org/10.9734/ajpas/2020/v6i130151
Omer, P. A. (2022). Improving prediction accuracy of Lasso and Ridge regression as an alternative to LS regression to identify variable selection problems. https://www.researchgate.net/publication/385137550_Improving_Prediction_Accuracy_of_Lasso_and_Ridge_Regression_as_an_Alternative_to_LS_Regression_to_Identify_Variable_Selection_Problems
Owoyemi, Q. A., & Bolakale, A. (2024). Comparative analysis of some linear predictive models in the presence of multicollinearity. International Journal of Advanced Statistics and Probability, 11(1). https://doi.org/10.14419/r2bqgv16 DOI: https://doi.org/10.14419/r2bqgv16
Oyeyemi, G. M., Ogunjobi, E. O., & Folorunsho, A. I. (2015). On performance of shrinkage methods – a A Monte Carlo Studystudy. International Journal of Statistics and Applications, 5(2), 72–76. https://www.researchgate.net/publication/313814555_On_Performance_of_Shrinkage_Methods_-_A_Monte_Carlo_Study
Pais, J. C., Figueiras, H., Pereira, P., & Kaloush, K. (2019). The pavements cost due to traffic overloads. International Journal of Pavement Engineering, 20(12), 1463–1473. https://doi.org/10.1080/10298436.2018.1435876 DOI: https://doi.org/10.1080/10298436.2018.1435876
Paul, R. K. (2006). Multicollinearity: Causes, effects and remedies. DOI: https://doi.org/10.1515/9783110186604.187
Petrusheva, S., Car-Pušić, D., & Zileska-Pancovska, V. (2019). Support vector machine based hybrid model for prediction of road structures construction costs. IOP Conference Series Earth and Environmental Science, 222(1), 012010. https://doi.org/10.1088/1755-1315/222/1/012010 DOI: https://doi.org/10.1088/1755-1315/222/1/012010
Portnoy, S., & Koenker, R. (1997). The Gaussian hare and the Laplacian tortoise: computability of squared-error versus absolute-error estimators. Statistical Science, 12(4), 279–300. https://doi.org/10.1214/ss/1030037960 DOI: https://doi.org/10.1214/ss/1030037960
Radhika, K., Valarmathy, S., Selvarasu, S., Bashkaran, K., & Srinivasan, C. (2023, November). Predictive Road Sign Maintenance using Random Forest Regression and IoT Data. 2023 International Conference on Sustainable Communication Networks and Application (ICSCNA) (pp. 348–353). https://doi.org/10.1109/ICSCNA58489.2023.10370728 DOI: https://doi.org/10.1109/ICSCNA58489.2023.10370728
Rahmawati, F., & Suratman, R. Y. (2022). Performa Regresi Ridge dan Regresi Lasso pada Data dengan Multikolinearitas. Leibniz: Jurnal Matematika, 2(2), 1–10. https://doi.org/10.59632/leibniz.v2i2.176 DOI: https://doi.org/10.59632/leibniz.v2i2.176
Raman, R., Vekariya, V., Gurpur, S., Narayana, K. E., & Murugan, S. (2024, November). Real-time road surface assessment through IoT data analysis and random forest. 2024 8th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, (pp. 290–295). https://doi.org/10.1109/ICECA63461.2024.10800879 IEEE. DOI: https://doi.org/10.1109/ICECA63461.2024.10800879
Ravishankar, S., & Latha, P. (2012). Software cost estimation using fuzzy logic. International Conference on Recent Trends in Computational Methods, Communication and Controls (ICON3C). https://scispace.com/pdf/software-cost-estimation-using-fuzzy-logic-3d786gzwf8.pdf
Rehman, G., Zhang, G., Rahman, M.U., Rahman, N.U., Usman, T. and Imraz, M. (2020) The Engineering Assessments and Potential Aggregate Analysis of Mesozoic Carbonates of Kohat Hills Range, KP, Pakistan. Acta Geodaetica et Geophysica, 55, 477–493. DOI: https://doi.org/10.1007/s40328-020-00301-9
Reznychenko, T., Uglickich, E., & Nagy, I. (2024, May). Accuracy comparison of logistic regression, random forest, and neural networks applied to real MaaS data. 2024 Smart City Symposium Prague (SCSP), Prague, Czech Republic, (pp. 1–5). https://doi.org/10.1109/SCSP61506.2024.10552715 DOI: https://doi.org/10.1109/SCSP61506.2024.10552715
Rohani, A., Abbaspour-Fard, M. H., & Abdolahpour, S. (2011). Prediction of tractor repair and maintenance costs using Artificial Neural Network. Expert Systems with Applications, 38(7), 8999–9007. https://doi.org/10.1016/j.eswa.2011.01.118 DOI: https://doi.org/10.1016/j.eswa.2011.01.118
Ryu, N., Kim, H., & Kang, P. (2016). Evaluating variable selection techniques for multivariate linear regression. Journal of Korean Institute of Industrial Engineers, 42(5), 314–326. https://doi.org/10.7232/JKIIE.2016.42.5.314 DOI: https://doi.org/10.7232/JKIIE.2016.42.5.314
Sağlık, A., & Güngör, G. (2008). Flexible pavement design guide for highways, (Topic #625-010-002). General Directorate of Highways, Department of Technical Research, Pavement Branch, Ankara, Turkey. https://fdotwww.blob.core.windows.net/sitefinity/ docs/default-source/roadway/pm/publications/fpdm201801.pdf?sfvrsn=149199e9_2
Salih, A. M. (2024). Explainable artificial intelligence and multicollinearity: A Mini mini review of current approaches. arXiv preprint 2406.11524. https://doi.org/10.48550/arXiv.2406.11524
Salman, H. A., Kalakech, A., & Steiti, A. (2024). Random Forest algorithm overview. Babylonian Journal of Machine Learning, 2024, 69–79. https://doi.org/10.58496/BJML/2024/007 DOI: https://doi.org/10.58496/BJML/2024/007
Sandri, M., & Zuccolotto, P. (2006, August). Variable selection using random forests. In S. Zani, A. Cerioli, M. Riani, & M. Vichi, (Eds.) Data Analysis, Classification and the Forward Search. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg (pp. 263–270). https://doi.org/10.1007/3-540-35978-8_30 DOI: https://doi.org/10.1007/3-540-35978-8_30
Santos, J., Ferreira, A., & Flintsch, G. (2019). An adaptive hybrid genetic algorithm for pavement management. International Journal of Pavement Engineering, 20(3), 266–286. https://doi.org/10.1080/10298436.2017.1293260 DOI: https://doi.org/10.1080/10298436.2017.1293260
Santos, G. S., Marotta, L. I. M., Clemente, G. A. B. F., Lima, G. de M., Scarano, D., & Oliveira, C. R. de. (2024). Concrete roads: Sustainable paving. Environmental & Social Management Journal, Revista de Gestão - RGSA, 18(10), e08596. https://doi.org/10.24857/rgsa.v18n10-095 DOI: https://doi.org/10.24857/rgsa.v18n10-095
Sapkota, K., Yaghoubi, E., Wasantha, P. L. P., Van Staden, R., & Fragomeni, S. (2023). Mechanical characteristics and durability of HMA made of recycled aggregates. Sustainability, 15(6), 5594. https://doi.org/10.3390/su15065594 DOI: https://doi.org/10.3390/su15065594
Schreiber-Gregory, D. N. (2018). Ridge Regression and multicollinearity: An in-depth review. Model Assisted Statistics and Applications, 13(4), 359–365. https://doi.org/10.3233/MAS-180446 DOI: https://doi.org/10.3233/MAS-180446
Smith, A. S., Odolinski, K., Hossein-Nia, S., Jönsson, P. A., Stichel, S., Iwnicki, S., & Wheat, P. (2021). Estimating the marginal maintenance cost of different vehicle types on rail infrastructure. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 235(10), 1191–1202. https://doi.org/10.1177/0954409721991309 DOI: https://doi.org/10.1177/0954409721991309
Soni, J., & Gujar, R. (2024). Classifying flexible pavement defects using hybrid machine learning approach. International Journal of Electrical and Computer Engineering (IJECE), 34(2), 1072–1080. http://doi.org/10.11591/ijeecs.v34.i2.pp1072-1080 DOI: https://doi.org/10.11591/ijeecs.v34.i2.pp1072-1080
Srisa-An, C. (2021, August). Guideline of collinearity-avoidable regression models on time-series analysis. 2021 2nd International Conference on Big Data Analytics and Practices (IBDAP), Bangkok, Thailand, (pp. 28–32). https://doi.org/10.1109/IBDAP52511.2021.9552165 DOI: https://doi.org/10.1109/IBDAP52511.2021.9552165
Thevaraja, M., & Rahman, A. (2020). Assessing robustness of regularized regression models with applications. In Xu, J., Ahmed, S., Cooke, F., Duca, G. (eds) Proceedings of the Thirteenth International Conference on Management Science and Engineering Management. ICMSEM 2019. Advances in Intelligent Systems and Computing, 1001. https://doi.org/10.1007/978-3-030-21248-3_30 DOI: https://doi.org/10.1007/978-3-030-21248-3_30
Thorat, M., & Birajdar, B. V. (2015). Cost overrun assessment model in highway construction projects using fuzzy uncertainty analysis. International Journal of Engineering Research & Technology (IJERT), 6(4), 568–574. https://www.ijert.org/cost-overrun-assessment-model-in-highway-construction-projects-using-fuzzy-uncertainty-analysis
Upendra, S., Abbaiah, R., & Balasiddamuni, P. (2023). Multicollinearity in multiple linear regression: Detection, consequences, and remedies. International Journal for Research in Applied Science and Engineering Technology, 11(9), 1047–1061. https://doi.org/10.22214/ijraset.2023.55786 DOI: https://doi.org/10.22214/ijraset.2023.55786
Vardon, P. J. (2015). Climatic influence on geotechnical infrastructure: a review. Environmental Geotechnics, 2(3), 166–174. https://doi.org/10.1680/envgeo.13.00055 DOI: https://doi.org/10.1680/envgeo.13.00055
Wang, C., Yao, C., Zhao, S., Zhao, S., & Li, Y. (2022). A comparative study of a fully- connected artificial neural network and a convolutional neural network in predicting bridge maintenance costs. Applied Sciences, 12(7), 3595. https://doi.org/10.3390/app12073595 DOI: https://doi.org/10.3390/app12073595
Woldemariam, W., Murillo-Hoyos, J., & Labi, S. (2016). Estimating annual maintenance expenditures for infrastructure: Artificial neural network approach. Journal of Infrastructure Systems, 22(2), 04015025. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000280 DOI: https://doi.org/10.1061/(ASCE)IS.1943-555X.0000280
Xi, L. J., Guo, Z. Y., Yang, X. K., & Ping, Z. G. (2023). Application of LASSO and its extended method in variable selection of regression analysis. Zhonghua yu fang yi xue za zhi [Chinese Journal of Preventive Medicine], 57(1), 107–111.
Yıldırım, H. (2024). The multicollinearity effect on the performance of machine learning algorithms: Case examples in healthcare modelling. Academic Platform Journal of Engineering and Smart Systems, 12(3), 68–80. https://doi.org/10.21541/apjess.1371070 DOI: https://doi.org/10.21541/apjess.1371070
Ying, J., Zhang, X., Zhang, Y., & Bilan, S. (2022). Green infrastructure: Systematic literature review. Economic research-Ekonomska istraživanja, 35(1), 343–366. https://doi.org/10.1080/1331677X.2021.1893202 DOI: https://doi.org/10.1080/1331677X.2021.1893202
Yu, B., Chen, Q., Li, N., Wang, Y., Li, L., Cai, M., ... Zhang, W., Gu, T., Zhu, R., Zeng, H., Zhang, Y., & Jiang, J. (2024). Life cycle assessment of urban road networks: Quantifying carbon footprints and forecasting future material stocks. Construction and Building Materials, 428, 136280. https://doi.org/10.1016/j.conbuildmat.2024.136280 DOI: https://doi.org/10.1016/j.conbuildmat.2024.136280
Zeng, L., & Xie, J. (2012). Group variable selection for data with dependent structures. Journal of Statistical Computation and Simulation, 82(1), 95–106. https://doi.org/10.1080/00949655.2010.529812 DOI: https://doi.org/10.1080/00949655.2010.529812
Zhang, Y., Cai, Y., & Wu, H. (2015, April). Research on cost estimation of highway project based on the GA-BP algorithm. Proceedings of the 19th International Symposium on Advancement of Construction Management and Real Estate (pp. 451–462). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-46994-1_37 DOI: https://doi.org/10.1007/978-3-662-46994-1_37
Zhang, Y., Minchin Jr, R. E., & Agdas, D. (2017). Forecasting completed cost of highway construction projects using LASSO regularized regression. Journal of Construction Engineering and Management, 143(10), 04017071. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001378 DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001378
Zhou, X., Lu, P., Zheng, Z., Tolliver, D., & Keramati, A. (2020). Accident prediction accuracy assessment for highway-rail grade crossings using random forest algorithm compared with decision tree. Reliability Engineering & System Safety, 200, 106931. https://doi.org/10.1016/j.ress.2020.106931 DOI: https://doi.org/10.1016/j.ress.2020.106931
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Haydar Gundogdu, Dr. Omer Faruk Cansiz, Prof. Dr. Mehmet Fatih Can
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
