ANN-based estimation model for the preconstruction cost of pavement rehabilitation projects

References

• Adel K, Elyamany A, Belal A, Kotb A. 2016. Developing parametric model for conceptual cost estimate of highway projects. Int J Eng Sci. 6(7):1728–1734.
   Google Scholar
• American Society of Civil Engineers [ASCE]. 2022. Report card for America’s infrastructure. [accessed Jun 2022]. http://www.asce.org.
   Google Scholar
• Amin M, Iqtidar A, Khan K, Javed M, Shalabi F, Qadir M. 2021. Comparison of machine learning approaches with traditional methods for predicting the compressive strength of rice husk ash concrete. Crystals. 11(7):779. doi: 10.3390/cryst11070779.
   Web of Science ®Google Scholar
• Antoniou F, Konstantinidis D, Aretoulis G, Xenidis Y. 2018. Preliminary construction cost estimates for motorway underpass bridges. Int J Constr Manage. 18(4):321–330. doi: 10.1080/15623599.2017.1358076.
   Google Scholar
• Asmar M, Hanna A, Whited G. 2011. New approach to developing conceptual cost estimates for highway projects. J Constr Eng Manage. 137(11):942–949. doi: 10.1061/(ASCE)CO.1943-7862.0000355.
   Web of Science ®Google Scholar
• Balazs G. 2009. Cascade-correlation neural networks: a survey. University of Alberta. Technical Report.
   Google Scholar
• Barakchi M, Torp O, Belay A. 2017. Cost estimation methods for transport infrastructure: a systematic literature review. Procedia Eng. 196:270–277. doi: 10.1016/j.proeng.2017.07.199.
   Google Scholar
• Blampied N. 2018. Parametric functions for conceptual and feasibility estimating in public highway project portfolios [Ph.D. dissertation]. UC Berkeley.
   Google Scholar
• Cheng M, Tsai H, Hsieh W. 2009. Web-based conceptual cost estimates for construction projects using Evolutionary Fuzzy Neural Inference Model. Autom Constr. 18(2):164–172. doi: 10.1016/j.autcon.2008.07.001.
   Web of Science ®Google Scholar
• Cheng M, Tsai H, Sudjono E. 2010. Conceptual cost estimates using evolutionary fuzzy hybrid neural network for projects in construction industry. Expert Syst Appl. 37(6):4224–4231. doi: 10.1016/j.eswa.2009.11.080.
   Web of Science ®Google Scholar
• Cho H, Kim K, Kim J, Kim G. 2013. A comparison of construction cost estimation using multiple regression analysis and neural network in elementary school project. J Korea Inst Build Constr. 13(1):66–74. doi: 10.5345/JKIBC.2013.13.1.066.
   Google Scholar
• Choi K, Kim YH, Bae Y, Lee H. 2016. Determining future maintenance cost of low-volume highway rehabilitation projects for incorporation into life-cycle cost analysis. J Comput Civil Eng. 30(4):04015055.
   Web of Science ®Google Scholar
• Choi S, Kim DY, Han SH, Kwak YH. 2014. Conceptual cost-prediction model for public road planning via rough set theory and case-based reasoning. J Constr Eng Manage. 140(1):04013026.
   Web of Science ®Google Scholar
• Chou J. 2009. Web-based CBR system applied to early cost budgeting for pavement maintenance projects. Expert Syst Appl. 36(2):2947–2960. doi: 10.1016/j.eswa.2008.01.025.
   Web of Science ®Google Scholar
• Cirilovic J, Vajdic N, Mladenovic G, Queiroz C. 2014. Developing cost estimation models for road rehabilitation and reconstruction: case study of projects in Europe and Central Asia. J Constr Eng Manage. 140(3):04013065.
   Web of Science ®Google Scholar
• Ciurana J, Quintana G, Garcia-Romeu M. 2008. Estimating the cost of vertical high-speed machining centres, a comparison between multiple regression analysis and the neural networks approach. Int J Prod Econ. 115(1):171–178. doi: 10.1016/j.ijpe.2008.05.009.
   Web of Science ®Google Scholar
• Craigie E, Gransberg D, Jeong H. 2016. Cost and scope breakdown structure for functional level estimating of consultant fees. Transp Res Record. 2573(1):157–163. doi: 10.3141/2573-19.
   Google Scholar
• Del Puerto C, Agosto L, Gransberg D. 2016. A case for incorporating pre-construction cost estimating in construction engineering and management programs. ASCE annual conference and exposition; p. 126–132.
   Google Scholar
• Dhole V, Patil N. 2014. A review of cascade correlation neural network for software cost estimation. International Journal of Computer Applications (0975–8887). National conference on emerging trends in information technology (NCETIT-2014).
   Google Scholar
• El-Kholy A. 2021. 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. doi: 10.1080/15623599.2019.1580001.
   Google Scholar
• El-Kholy A, Tahwia A, Elsayed M. 2022. Prediction of simulated cost contingency for steel reinforcement in building projects: ANN versus regression-based models. Int J Constr Manage. 22(9):1675–1689. doi: 10.1080/15623599.2020.1741492.
   Google Scholar
• Golizadeh H, Banihashemi S, Sadeghifam A, Preece C. 2017. Automated estimation of completion time for dam projects. Int J Constr Manage. 17(3):197–209. doi: 10.1080/15623599.2016.1192249.
   Google Scholar
• Goodarzizad P, Golafshani E, Arashpour M. 2021. Predicting the construction labour productivity using artificial neural network and grasshopper optimization algorithm. Int J Constr Manage. 21(5):1–17.
   Google Scholar
• Gransberg D, Jeong H, Craigie E, Rueda-Benavides J, Shrestha K. 2016. Estimating highway preconstruction service costs – Volume 1: guidebook. National Cooperative Highway Research Program (NCHRP). Report 826.
   Google Scholar
• Hyari K, Al-Daraiseh A, El-Mashaleh M. 2016. Conceptual cost estimation model for engineering services in public construction projects. J Manage Eng. 32(1):04015021.
   Web of Science ®Google Scholar
• Kim G, An S, Kang K. 2004. Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning. Build Environ. 39(10):1235–1242. doi: 10.1016/j.buildenv.2004.02.013.
   Web of Science ®Google Scholar
• Kim G, Shin J, Kim S, Shin Y. 2013. Comparison of school building construction costs estimation methods using regression analysis, neural network, and support vector machine. JBCPR. 01(01):1–7. doi: 10.4236/jbcpr.2013.11001.
   Google Scholar
• Jaafari A, Pazhouhan I, Bettinger P. 2021. Machine learning modeling of forest road construction cost. Forests. 12(9):1169. doi: 10.3390/f12091169.
   Web of Science ®Google Scholar
• Li C, Ding L, Zhong B. 2019. Highway planning and design in the Qinghai–Tibet Plateau of China: a cost–safety balance perspective. Engineering. 5(2):337–349. doi: 10.1016/j.eng.2018.12.008.
   Web of Science ®Google Scholar
• Liu M, Rasdorf W, Hummer J, Hollar D, Parikh S. 2012. Predicting the preliminary engineering duration of bridge projects. Constr Res Congr. 1:505–514. doi: 10.1061/9780784412329.051.
   Google Scholar
• Liu M, Rasdorf W, Hummer J, Hollar D, Parikh S. 2013. Preliminary engineering cost-estimation strategy assessment for roadway projects. J Manage Eng. 29(2):150–157. doi: 10.1061/(ASCE)ME.1943-5479.0000137.
   Web of Science ®Google Scholar
• Markiz N, Jrade A. 2022. Integrating an expert system with BrIMS, cost estimation, and linear scheduling at conceptual design stage of bridge projects. Int J Constr Manage. 22(5):913–928. doi: 10.1080/15623599.2019.1661572.
   Google Scholar
• Matel E, Vahdatikhaki F, Hosseinyalamdary F, Evers T, Voordijk H. 2022. An artificial neural network approach for cost estimation of engineering services. Int J Constr Manage. 22(7):1274–1287. doi: 10.1080/15623599.2019.1692400.
   Google Scholar
• Mesbahi M, Rahmani A, Hosseinzadeh M. 2017. Highly reliable architecture using 80/20 rule in cloud computing datacenters. Future Gener Comput Syst. 77:77–86. doi: 10.1016/j.future.2017.06.011.
   Web of Science ®Google Scholar
• Mohamed B, Moselhi O. 2022. Conceptual estimating of construction duration and cost of public highway projects. ITcon. 27:595–618. doi: 10.36680/j.itcon.2022.029.
   Google Scholar
• Mohamed S, Mohamed M, Farghally M. 2021. A new cascade-correlation growing deep learning neural network algorithm. Algorithms. 14(5):158. doi: 10.3390/a14050158.
   Web of Science ®Google Scholar
• Nachev A, Hogan M, Stoyanov B. 2012. Cascade-correlation neural networks for breast cancer diagnosis. ICAI’11: The 11th international conference on artificial intelligence, Las Vegas, USA, July 18–21; p. 475–480.
   Google Scholar
• Persad K, O’Connor J, Varghese K. 1995. Forecasting engineering manpower requirements for highway preconstruction activities. J Manage Eng. 11(3):41–47.
   Web of Science ®Google Scholar
• Roxas C, Roxas N, Cristobal J, Hao S, Rabino R. 2019. Modeling road construction project cost in the Philippines using the artificial neural network approach. IEEE 11th international conference on humanoid nanotechnology, information technology, communication and control, environment, and management (HNICEM), Nov 29, 2019; p. 1–5.
   Google Scholar
• Sahoo S, Jha M. 2013. Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment. Hydrol J. 21(8):1865–1887.
   Google Scholar
• Shehab T. 2009. Cost impact factors for utility replacement and repair projects. Cost Eng (AACE). 51(1):22–29.
   Google Scholar
• Shehab T, Farooq M. 2013. Neural network cost estimating model for utility rehabilitation projects. J Eng Constr Architect Manag. 20(2):118–126. doi: 10.1108/09699981311302991.
   Google Scholar
• Shehab T, Farooq M, Sandhu S, Nguyen T, Nasr E. 2010. Cost estimating models for utility rehabilitation projects: neural networks versus regression. J Pipeline Syst Eng Pract. 1(3):104–110. doi: 10.1061/(ASCE)PS.1949-1204.0000058.
   Web of Science ®Google Scholar
• Shehab T, Nasr E, Farooq M. 2014. Conceptual cost-estimating model for water and sewer projects. Pipelines 2014: from underground to the forefront of innovation and sustainability, Portland, Oregon; p. 367–373. doi: 10.1061/9780784413692.033.
   Google Scholar
• Shehab T, Meisami-Fard I. 2013. Cost-estimating model for rubberized asphalt pavement rehabilitation projects. J Infrastruct Syst. 19(4):496–502. doi: 10.1061/(ASCE)IS.1943-555X.0000146.
   Web of Science ®Google Scholar
• Shrestha P, Mani N. 2014. Impact of design cost on project performance of design-bid-build road projects. J Manage Eng. 30(3):04014007. doi: 10.1061/(ASCE)ME.1943-5479.0000220.
   Web of Science ®Google Scholar
• Tanabe K. 2018. Pareto’s 80/20 rule and the Gaussian distribution. Physica A. 510:635–640. doi: 10.1016/j.physa.2018.07.023.
   Web of Science ®Google Scholar
• Topalović D, Davidović M, Jovanović M, Bartonova A, Ristovski Z, Jovašević-Stojanović M. 2019. In search of an optimal in-field calibration method of low-cost gas sensors for ambient air pollutants: comparison of linear, multilinear and artificial neural network approaches. Atmos Environ. 213:640–658. doi: 10.1016/j.atmosenv.2019.06.028.
   Web of Science ®Google Scholar
• Vahdani B, Mousavi S, Mousakhani M, Sharifi M, Hashemi H. 2011. A neural network model based on support vector machine for conceptual cost estimation in construction projects. J Optim Ind Eng. 5(10):11–18.
   Google Scholar
• Volovski M, Murillo-Hoyos J, Saeed T, Labi S. 2017. Estimation of routine maintenance expenditures for highway pavement segments: accounting for heterogeneity using random-effects models. J Transp Eng A Syst. 143(5):04017006.
   Web of Science ®Google Scholar
• Wang W, Bilozerov T, Dzeng R, Hsiao F, Wang K. 2017. Conceptual cost estimations using neuro-fuzzy and multi-factor evaluation methods for building projects. J Constr Eng Manage. 23(1):1–14.
   Web of Science ®Google Scholar
• Wang Y, Elhag T. 2007. A comparison of neural network, evidential reasoning and multiple regression analysis in modelling bridge risks. Expert Syst Appl. 32(2):336–348. doi: 10.1016/j.eswa.2005.11.029.
   Web of Science ®Google Scholar
• Ward Systems Group. 2022. NeuroShell predictor reference manual. Fredrick (MD): Ward Systems Group.
   Google Scholar
• Yu WD, Skibniewski M. 2010. Integrating neurofuzzy system with conceptual cost estimation to discover cost-related knowledge from residential construction projects. J Comput Civ Eng. 24(1):35–44. doi: 10.1061/(ASCE)0887-3801(2010)24:1(35).
   Web of Science ®Google Scholar
• Zhang Y, Minchin E, Agdas D. 2017. Forecasting completed cost of highway projects using LASSO regularized regression. J Constr Eng Manage. 143(10):04017071.
   Web of Science ®Google Scholar