{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:16:37Z","timestamp":1760242597275,"version":"build-2065373602"},"reference-count":64,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2017,12,1]],"date-time":"2017-12-01T00:00:00Z","timestamp":1512086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Cost estimation is an essential issue in feasibility studies in civil engineering. Many different methods can be applied to modelling costs. These methods can be divided into several main groups: (1) artificial intelligence, (2) statistical methods, and (3) analytical methods. In this paper, the multivariate regression (MVR) method, which is one of the most popular linear models, and the artificial neural network (ANN) method, which is widely applied to solving different prediction problems with a high degree of accuracy, have been combined to provide a cost estimate model for a shovel machine. This hybrid methodology is proposed, taking the advantages of MVR and ANN models in linear and nonlinear modelling, respectively. In the proposed model, the unique advantages of the MVR model in linear modelling are used first to recognize the existing linear structure in data, and, then, the ANN for determining nonlinear patterns in preprocessed data is applied. The results with three indices indicate that the proposed model is efficient and capable of increasing the prediction accuracy.<\/jats:p>","DOI":"10.3390\/sym9120298","type":"journal-article","created":{"date-parts":[[2017,12,1]],"date-time":"2017-12-01T12:30:16Z","timestamp":1512131416000},"page":"298","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5594-7726","authenticated-orcid":false,"given":"Abdolreza","family":"Yazdani-Chamzini","sequence":"first","affiliation":[{"name":"Young Researchers and Elite Club, South Tehran Branch, Islamic Azad University, 14115\/344 Tehran, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3201-949X","authenticated-orcid":false,"given":"Edmundas","family":"Zavadskas","sequence":"additional","affiliation":[{"name":"Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Sauletekio al. 11, 10223 Vilnius, Lithuania"},{"name":"Institute of Sustainable Construction, Vilnius Gediminas Technical University, Sauletekio al. 11, 10223 Vilnius, Lithuania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1734-3216","authenticated-orcid":false,"given":"Jurgita","family":"Antucheviciene","sequence":"additional","affiliation":[{"name":"Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Sauletekio al. 11, 10223 Vilnius, Lithuania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6284-0406","authenticated-orcid":false,"given":"Romualdas","family":"Bausys","sequence":"additional","affiliation":[{"name":"Department of Graphical Systems, Vilnius Gediminas Technical University, Sauletekio al. 11, 10223 Vilnius, Lithuania"}]}],"member":"1968","published-online":{"date-parts":[[2017,12,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1016\/j.jclepro.2017.06.208","article-title":"A study on energy use for excavation and transport of soil during building construction","volume":"164","author":"Devi","year":"2017","journal-title":"J. Clean. Prod."},{"key":"ref_2","unstructured":"Atkinson, T. (1992). Selection and sizing of excavating equipment. SME Mining Engineering Handbook, Society for Mining, Metallurgy, and Exploration."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tatiya, R.R. (2005). Surface and Underground Excavations\u2014Methods, Techniques and Equipment, Taylor & Francis Group plc.","DOI":"10.1201\/9781439834220"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.autcon.2015.06.006","article-title":"Development of a dual displacement controlled circuit for hydraulic shovel swing motion","volume":"57","author":"Huang","year":"2015","journal-title":"Autom. Constr."},{"key":"ref_5","first-page":"95","article-title":"Performance measurement of mining equipments by utilizing OEE","volume":"15","author":"Elevli","year":"2010","journal-title":"Acta Montan. Slovaca"},{"key":"ref_6","first-page":"125","article-title":"Equipment selection using fuzzy multi criteria decision making model: Key study of Gole Gohar iron mine","volume":"23","author":"Lashgari","year":"2012","journal-title":"Inz. Econ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"378","DOI":"10.3846\/13923730.2012.692705","article-title":"Estimating capital and operational costs of backhoe shovels","volume":"18","author":"Sayadi","year":"2012","journal-title":"J. Civ. Eng. Manag."},{"key":"ref_8","unstructured":"Hartman, H.L. (1992). Cost comparisons. SME Mining Engineering Handbook, Society for Mining, Metallurgy, and Exploration. [2nd ed.]."},{"key":"ref_9","unstructured":"Anon (1986). Estimating Preproduction and Operating Costs of Small Underground Deposits (CANMET)."},{"key":"ref_10","unstructured":"Mular, A.L., and Poulin, R. (1998). CAPCOSTS: A Handbook for Estimating Mining and Mineral Processing Equipment Costs and Capital Expenditures and Aiding Mineral Project Evaluations, Canadian Institute of Mining and Metallurgy. CIM Bulletin Special, 47."},{"key":"ref_11","unstructured":"Mular, A.L. (1982). Mining and Mineral Processing Equipment Costs and Preliminary Capital Cost Estimation, Canadian Institute of Mining and Metallurgy. CIM Bulletin Special, 25."},{"key":"ref_12","unstructured":"Lanz, T., and Noakes, M. (1993). Cost Estimation Handbook for the Australian Mining Industry, Australasian Institute of Mining and Metallurgy (Aus IMM)."},{"key":"ref_13","unstructured":"USBM (1987). US Bureau of Mines Cost Estimating System Handbook, Mining and Beneficiation of Metallic and Nonmetallic Minerals Expected Fossil Fuels in the United States and Canada."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"789","DOI":"10.17159\/2411-9717\/2015\/v115n8a17","article-title":"Parametric estimation of capital costs for establishing a coal mine: South Africa case study","volume":"115","author":"Mohutsiwa","year":"2015","journal-title":"J. South. Afr. Inst. Min. Metall."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.tust.2011.08.006","article-title":"Hard-rock LHD cost estimation using single and multiple regressions based on principal component analysis","volume":"27","author":"Sayadi","year":"2012","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_16","first-page":"34","article-title":"Quick guide to the evaluation of ore bodies","volume":"88","year":"1998","journal-title":"CIM Bull."},{"key":"ref_17","unstructured":"Hoskins, J.R. (1982). Mine Evaluation, Mineral Industry Costs, Northwest Mining Association."},{"key":"ref_18","unstructured":"O\u2019Hara, T.A. (1987). Quick Guide to Mine Operating Costs and Revenue, CIM Annual General Meeting. Paper No. 186."},{"key":"ref_19","first-page":"405","article-title":"Costs and Cost Estimation","volume":"Volume 1","author":"Suboleski","year":"1992","journal-title":"SME Mining Engineering Handbook"},{"key":"ref_20","first-page":"559","article-title":"Simplified cost models for pre-feasibility mineral evaluations","volume":"46","author":"Camm","year":"1994","journal-title":"Min. Eng."},{"key":"ref_21","unstructured":"Rudenno, V. (1998). The Mining Valuation Handbook: Australian Mining and Energy Valuation for Investors and Management, Australian Print Group."},{"key":"ref_22","unstructured":"Sayadi, A.R., Khademi, J., and Rahimi, M.A. (2015, January 14\u201317). Estimating the energy costs of mine equipment using an information system. Proceedings of the 24th International Mining Congress and Exhibition of Turkey, Antalya, Turkey."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1109\/TCSI.2016.2632718","article-title":"Fixed-order piecewise-affine output feedback controller for fuzzy-affine-model-based nonlinear systems with time-varying delay","volume":"64","author":"Wei","year":"2017","journal-title":"IEEE Trans. Circuits Syst. I Regul. Pap."},{"key":"ref_24","first-page":"1","article-title":"Fuzzy-affine-model-based memory filter design of nonlinear systems with time-varying delay","volume":"PP","author":"Wei","year":"2017","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.engappai.2006.06.017","article-title":"ANN-based estimator for distillation using Levenberg-Marquardt approach","volume":"20","author":"Singh","year":"2007","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1016\/S0169-409X(03)00121-2","article-title":"Clinical application of artificial neural network (ANN) modeling to predict pharmacokinetic parameters of severely ill patients","volume":"55","author":"Yamamura","year":"2003","journal-title":"Adv. Drug Deliv. Rev."},{"key":"ref_27","first-page":"684","article-title":"Combined ANN prediction model for failure depth of coal seam floors","volume":"19","author":"Jian","year":"2009","journal-title":"Min. Sci. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.apenergy.2008.03.018","article-title":"Development and multi-utility of an ANN model for an industrial gas turbine","volume":"86","author":"Fast","year":"2009","journal-title":"Appl. Energy"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1016\/j.autcon.2008.02.008","article-title":"Improving the reliability of a Bridge Management System (BMS) using an ANN-based Backward Prediction Model (BPM)","volume":"17","author":"Lee","year":"2008","journal-title":"Autom. Constr."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1016\/j.jpba.2009.07.009","article-title":"Shuffling multivariate adaptive regression splines and adaptive neuro-fuzzy inference system as tools for QSAR study of SARS inhibitors","volume":"50","year":"2009","journal-title":"J. Pharm. Biomed. Anal."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1016\/j.ijpe.2007.02.004","article-title":"Cost estimation for sheet metal parts using multiple regression and artificial neural networks: A case study","volume":"111","author":"Verlinden","year":"2008","journal-title":"Int. J. Prod. Econ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.coal.2009.04.002","article-title":"Estimation of gross calorific value based on coal analysis using regression and artificial neural networks","volume":"79","author":"Mesroghli","year":"2009","journal-title":"Int. J. Coal Geol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.jhydrol.2009.09.037","article-title":"Forecasting stream water temperature using regression analysis, artificial neural network, and chaotic non-linear dynamic models","volume":"378","author":"Sahoo","year":"2009","journal-title":"J. Hydrol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1007\/978-3-540-87734-9_60","article-title":"A fuzzy neutral-network-driven weighting system for electric shovel","volume":"Volume 5264","author":"Sun","year":"2008","journal-title":"Advances in Neural Networks\u2014ISNN 2008"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1727","DOI":"10.1177\/1369433217693629","article-title":"Model updating of suspended-dome using artificial neural networks","volume":"20","author":"Guo","year":"2017","journal-title":"Adv. Struct. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1007\/s11704-016-5113-6","article-title":"Research on axial bearing capacity of rectangular concrete-filled steel tubular columns based on artificial neural networks","volume":"11","author":"Du","year":"2017","journal-title":"Front. Comput. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Gardner, B.J., Gransberg, D.D., and Rueda, J.A. (2017). Stochastic conceptual cost estimating of highway projects to communicate uncertainty using bootstrap sampling. ASCE-ASME J. Risk Uncertain. Eng. Part A Civ. Eng., 3.","DOI":"10.1061\/AJRUA6.0000895"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"El-Gohary, K.M., Aziz, R.F., and Abdel-Khalek, H.A. (2017). Engineering approach using ANN to improve and predict construction labor productivity under different influences. J. Constr. Eng. Manag., 143.","DOI":"10.1061\/(ASCE)CO.1943-7862.0001340"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1007\/s00521-015-2132-4","article-title":"Neural network ensemble-based parameter sensitivity analysis in civil engineering systems","volume":"28","author":"Cao","year":"2017","journal-title":"Neural Comput. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.jsv.2016.10.043","article-title":"Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks","volume":"388","author":"Abdeljaber","year":"2017","journal-title":"J. Sound Vib."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"393","DOI":"10.3846\/13923730.2016.1144643","article-title":"Prediction of axial capacity of piles driven in non-cohesive soils based on neural networks approach","volume":"23","author":"Benali","year":"2017","journal-title":"J. Civ. Eng. Manag."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"967","DOI":"10.3846\/13923730.2016.1205510","article-title":"Proposing a neural network model to predict time and cost claims in construction projects","volume":"22","author":"Yousefi","year":"2016","journal-title":"J. Civ. Eng. Manag."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"173","DOI":"10.3846\/13923730.2016.1247748","article-title":"A model for spatial planning of site and building using BIM methodology","volume":"23","author":"Ustinovichius","year":"2017","journal-title":"J. Civ. Eng. Manag."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3846\/13923730.2014.948908","article-title":"Conceptual cost estimations using neuro-fuzzy and multi-factor evaluation methods for building projects","volume":"23","author":"Wang","year":"2017","journal-title":"J. Civ. Eng. Manag."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1016\/j.ijrmms.2009.05.005","article-title":"Prediction of rock fragmentation due to blasting in Gol-E-Gohar iron mine using fuzzy logic","volume":"46","author":"Monjezi","year":"2009","journal-title":"Int. J. Rock Mech. Min. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/S0928-0987(97)10028-8","article-title":"Advantages of Artificial Neural Networks (ANNs) as alternative modelling technique for data sets showing non-linear relationships using data from a galenical study on a solid dosage form","volume":"7","author":"Bourquin","year":"1998","journal-title":"Eur. J. Pharm. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"21","DOI":"10.3846\/13923730.2006.9636368","article-title":"Analysis of district heating network monitoring by neural networks classification","volume":"12","author":"Malinowski","year":"2006","journal-title":"J. Civ. Eng. Manag."},{"key":"ref_48","unstructured":"He, X., and Xu, S. (2007). Process Neural Networks Theory and Applications, Springer."},{"key":"ref_49","unstructured":"Engelbrecht, A.P. (2002). Computational Intelligence: An Introduction, John Wiley & Sons, Ltd."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Sumathi, S., and Paneerselvam, S. (2010). Computational Intelligence Paradigms: Theory & Applications Using MATLAB, Taylor and Francis Group, LLC.","DOI":"10.1201\/9781439809037"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2664","DOI":"10.1016\/j.asoc.2010.10.015","article-title":"A novel hybridization of artificial neural networks and ARIMA models for time series forecasting","volume":"11","author":"Khashei","year":"2011","journal-title":"Appl. Soft Comput."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/S0925-2312(01)00702-0","article-title":"Time series forecasting using a hybrid ARIMA and neural network model","volume":"50","author":"Zhang","year":"2003","journal-title":"Neurocomputing"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/s00521-012-1271-0","article-title":"Developing a new model based on neuro-fuzzy system for predicting roof fall in coal mines","volume":"23","author":"Farid","year":"2013","journal-title":"Neural Comput. Appl."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/0022-1694(70)90255-6","article-title":"River flow forecasting through conceptual models, I. A discussion of principles","volume":"10","author":"Nash","year":"1970","journal-title":"J. Hydrol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.ssci.2012.11.008","article-title":"A novel fuzzy inference system for predicting roof fall rate in underground coal mines","volume":"55","author":"Razani","year":"2013","journal-title":"Saf. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"994","DOI":"10.3846\/16111699.2012.683808","article-title":"Forecasting gold price changes by using adaptive network fuzzy inference system","volume":"13","author":"Yakhchali","year":"2012","journal-title":"J. Bus. Econ. Manag."},{"key":"ref_57","unstructured":"(2015, June 26). Info Mine: Mining Cost Service Indexes. Available online: http:\/\/www.infomine.com."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.jhydrol.2005.09.032","article-title":"Forecasting daily streamflow using hybrid ANN models","volume":"324","author":"Wang","year":"2006","journal-title":"J. Hydrol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1016\/j.gsf.2012.02.003","article-title":"A robust behavior of Feed forward back propagation algorithm of Artificial Neural Networks in the application of vertical electrical sounding data inversion","volume":"3","author":"Srinivas","year":"2012","journal-title":"Geosci. Front."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0167-7012(00)00201-3","article-title":"Artificial neural networks: Fundamentals, computing, design, and application","volume":"43","author":"Basheer","year":"2000","journal-title":"J. Microbiol. Methods"},{"key":"ref_61","first-page":"1","article-title":"A novel memory filtering design for semi-Markovian jump time-delay systems","volume":"PP","author":"Wei","year":"2017","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_62","first-page":"1491","article-title":"Reliable Control of Discrete-Time Piecewise-Affine Time-Delay Systems via Output Feedback","volume":"57","author":"Qiu","year":"2017","journal-title":"IEEE Trans. Reliab."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Ross, T.J. (2010). Fuzzy Logic with Engineering Applications, John Wiley & Sons Ltd.. [3rd ed.].","DOI":"10.1002\/9781119994374"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.autcon.2013.04.001","article-title":"Developing a fuzzy model based on subtractive clustering for road header performance prediction","volume":"35","author":"Razani","year":"2013","journal-title":"Autom. Constr."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/9\/12\/298\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:52:14Z","timestamp":1760208734000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/9\/12\/298"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12,1]]},"references-count":64,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2017,12]]}},"alternative-id":["sym9120298"],"URL":"https:\/\/doi.org\/10.3390\/sym9120298","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2017,12,1]]}}}