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Investigation of Factors Impacting Construction Cost Estimate to Develop Construction-Driven Artificial Neural Network (ANN).

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Investigation of Factors Impacting Construction Cost Estimate to Develop Construction-Driven Artificial Neural Network (ANN)./
Author:	Al Saber, Salem.
Description:	1 online resource (143 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-02.
Contained By:	Masters Abstracts International85-02.
Subject:	Civil engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798380110112

Investigation of Factors Impacting Construction Cost Estimate to Develop Construction-Driven Artificial Neural Network (ANN).
Al Saber, Salem.

Investigation of Factors Impacting Construction Cost Estimate to Develop Construction-Driven Artificial Neural Network (ANN). - 1 online resource (143 pages)

Source: Masters Abstracts International, Volume: 85-02.

Thesis (M.S.)--Arizona State University, 2023.

Includes bibliographical references

Construction industry is the backbone of any country's economy. It is a primary source of foreign investments, creates new jobs, and maintains the economy flowing in various trades. Accurate cost estimation is a critical aspect for the construction industry, directly impacting project success and profitability. This master's thesis focuses on comprehensively identifying the key factors that influence cost estimation and provides valuable recommendations for constructing an optimized Artificial Neural Network (ANN) model. Through an extensive research methodology encompassing literature review, surveys, and interviews with industry professionals, this study uncovers significant factors that exert a substantial impact on cost estimation practices. The findings emphasize the importance of seamlessly integrating project delivery systems, meticulously considering project duration, and incorporating diverse perspectives from global regions. By incorporating these insights, stakeholders can make informed decisions, enhance project planning, and elevate overall project performance. This study successfully bridges the gap between theory and practice, presenting invaluable insights for stakeholders within the construction industry.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380110112Subjects--Topical Terms:

561339
Civil engineering.
Subjects--Index Terms:

Artificial Neural NetworkIndex Terms--Genre/Form:

554714
Electronic books.

Investigation of Factors Impacting Construction Cost Estimate to Develop Construction-Driven Artificial Neural Network (ANN).
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