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Estimation of Costs and Durations of Construction of Urban Roads Using ANN and SVM

机译:使用ANN和SVM估算城市道路建设成本和工期

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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.
机译:报价准备一直是构建过程的特定组成部分,会对公司业务产生重大影响。由于收入在很大程度上取决于报价的精确度以及计划成本(直接成本和间接成本以及期望利润)之间的平衡,因此有必要在要求的时间内准备精确的报价,而可用资源总是不够的。本文提出了在使用人工智能估算建筑项目的成本和工期时可以实现的精度研究。分析和比较了人工神经网络(ANN)和支持向量机(SVM)。最好的SVM在估算成本时显示出更高的精度,而最精确的ANN的平均绝对百分比误差(MAPE)为7.06%,而后者的精度为25.38%。工作时间的估计被证明更加困难。 SVM和ANN的最佳MAPE分别为22.77%和26.26%。

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