<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The construction industry in Yemen is currently facing challenges associated with rapid development of technology; thus, cost estimation is considered a key factor that should align with this technological advancement. The main problem in the area of preliminary estimate in Yemen is how to make estimate accurately. The aim of this study is to analyse a modern method of preliminary cost estimation in Yemen to prove its efficiency over the traditional method. Therefore, a wide range of literature sources regarding the preliminary estimates using Artificial Neural Network (ANN) as a modern technique is considered. Both qualitative and quantitative approaches were adopted in this study depending on the theoretical premises discussed in literature and the ANN technique, respectively. The independent variables were chosen in the course of literature review. The collected data were classified and processed regarding the ANN constraints and encoded for building and analysis of the ANN model. NeuroSolution 6 software was used to build, train, and test the network as well as to perform sensitivity analysis. In addition, the results of training, testing, and sensitivity analysis were obtained and discussed showing high effectiveness of accurate estimates with less than 1 % error. The ANN model is a more powerful technique for estimating costs in the preliminary stage that should be used in the developing countries instead of the traditional methods.</p></abstract>

The construction industry in Yemen is currently facing challenges associated with rapid development of technology; thus, cost estimation is considered a key factor that should align with this technological advancement. The main problem in the area of preliminary estimate in Yemen is how to make estimate accurately. The aim of this study is to analyse a modern method of preliminary cost estimation in Yemen to prove its efficiency over the traditional method. Therefore, a wide range of literature sources regarding the preliminary estimates using Artificial Neural Network (ANN) as a modern technique is considered. Both qualitative and quantitative approaches were adopted in this study depending on the theoretical premises discussed in literature and the ANN technique, respectively. The independent variables were chosen in the course of literature review. The collected data were classified and processed regarding the ANN constraints and encoded for building and analysis of the ANN model. NeuroSolution 6 software was used to build, train, and test the network as well as to perform sensitivity analysis. In addition, the results of training, testing, and sensitivity analysis were obtained and discussed showing high effectiveness of accurate estimates with less than 1 % error. The ANN model is a more powerful technique for estimating costs in the preliminary stage that should be used in the developing countries instead of the traditional methods.

Preliminary Construction Cost Estimate in Yemen by Artificial Neural Network

Sudan University of Science and Technology

Baltic Journal of Real Estate Economics and Construction Management

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Preliminary Construction Cost Estimate in Yemen by Artificial Neural Network"}

The construction industry in Yemen is currently facing challenges associated with rapid development of technology; thus, cost estimation is considered a key...

Preliminary Construction Cost Estimate in Yemen by Artificial Neural Network

Pubblicato online: 08 ago 2019

Pagine: 110 - 122

DOI: https://doi.org/10.2478/bjreecm-2019-0007

Parole chiave
Accurately, Performance, Sensitivity analysis, Training, Testing

© 2019 Waled Hakami et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Preliminary Construction Cost Estimate in Yemen by Artificial Neural Network

Pubblicato online: 08 ago 2019

Pagine: 110 - 122

DOI: https://doi.org/10.2478/bjreecm-2019-0007

Parole chiaveAccurately, Performance, Sensitivity analysis, Training, Testing

© 2019 Waled Hakami et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parole chiave
Accurately, Performance, Sensitivity analysis, Training, Testing