Estimation of Costs and Durations of Construction of Urban Roads Using ANN and SVM
Offer preparation has always been a specific part of a building process which has significant impact on company business. Due to the fact that income greatly depends on offer’s precision and the balance between planned costs, both direct and overheads, and wished profit, it is necessary to prepare a...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/2450370 |
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| _version_ | 1832556688534667264 |
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| author | Igor Peško Vladimir Mučenski Miloš Šešlija Nebojša Radović Aleksandra Vujkov Dragana Bibić Milena Krklješ |
| author_facet | Igor Peško Vladimir Mučenski Miloš Šešlija Nebojša Radović Aleksandra Vujkov Dragana Bibić Milena Krklješ |
| author_sort | Igor Peško |
| collection | DOAJ |
| description | Offer preparation has always been a specific part of a building process which has significant impact on company business. Due to the fact that income greatly depends on offer’s precision and the balance between planned costs, both direct and overheads, and wished profit, it is necessary to prepare a precise offer within required time and available resources which are always insufficient. The paper presents a research of precision that can be achieved while using artificial intelligence for estimation of cost and duration in construction projects. Both artificial neural networks (ANNs) and support vector machines (SVM) are analysed and compared. The best SVM has shown higher precision, when estimating costs, with mean absolute percentage error (MAPE) of 7.06% compared to the most precise ANNs which has achieved precision of 25.38%. Estimation of works duration has proved to be more difficult. The best MAPEs were 22.77% and 26.26% for SVM and ANN, respectively. |
| format | Article |
| id | doaj-art-8fa4eaa0119d4dd2ba8db5f6b41e0f19 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-8fa4eaa0119d4dd2ba8db5f6b41e0f192025-02-03T05:44:40ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/24503702450370Estimation of Costs and Durations of Construction of Urban Roads Using ANN and SVMIgor Peško0Vladimir Mučenski1Miloš Šešlija2Nebojša Radović3Aleksandra Vujkov4Dragana Bibić5Milena Krklješ6University of Novi Sad, Faculty of Technical Sciences, Trg Dositeja Obradovica 6, Novi Sad, SerbiaUniversity of Novi Sad, Faculty of Technical Sciences, Trg Dositeja Obradovica 6, Novi Sad, SerbiaUniversity of Novi Sad, Faculty of Technical Sciences, Trg Dositeja Obradovica 6, Novi Sad, SerbiaUniversity of Novi Sad, Faculty of Technical Sciences, Trg Dositeja Obradovica 6, Novi Sad, SerbiaUniversity of Novi Sad, Faculty of Technical Sciences, Trg Dositeja Obradovica 6, Novi Sad, SerbiaUniversity of Novi Sad, Faculty of Technical Sciences, Trg Dositeja Obradovica 6, Novi Sad, SerbiaUniversity of Novi Sad, Faculty of Technical Sciences, Trg Dositeja Obradovica 6, Novi Sad, SerbiaOffer preparation has always been a specific part of a building process which has significant impact on company business. Due to the fact that income greatly depends on offer’s precision and the balance between planned costs, both direct and overheads, and wished profit, it is necessary to prepare a precise offer within required time and available resources which are always insufficient. The paper presents a research of precision that can be achieved while using artificial intelligence for estimation of cost and duration in construction projects. Both artificial neural networks (ANNs) and support vector machines (SVM) are analysed and compared. The best SVM has shown higher precision, when estimating costs, with mean absolute percentage error (MAPE) of 7.06% compared to the most precise ANNs which has achieved precision of 25.38%. Estimation of works duration has proved to be more difficult. The best MAPEs were 22.77% and 26.26% for SVM and ANN, respectively.http://dx.doi.org/10.1155/2017/2450370 |
| spellingShingle | Igor Peško Vladimir Mučenski Miloš Šešlija Nebojša Radović Aleksandra Vujkov Dragana Bibić Milena Krklješ Estimation of Costs and Durations of Construction of Urban Roads Using ANN and SVM Complexity |
| title | Estimation of Costs and Durations of Construction of Urban Roads Using ANN and SVM |
| title_full | Estimation of Costs and Durations of Construction of Urban Roads Using ANN and SVM |
| title_fullStr | Estimation of Costs and Durations of Construction of Urban Roads Using ANN and SVM |
| title_full_unstemmed | Estimation of Costs and Durations of Construction of Urban Roads Using ANN and SVM |
| title_short | Estimation of Costs and Durations of Construction of Urban Roads Using ANN and SVM |
| title_sort | estimation of costs and durations of construction of urban roads using ann and svm |
| url | http://dx.doi.org/10.1155/2017/2450370 |
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