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Article
Publication date: 1 April 2004

Hashem M. Al‐Tabtabai and Varghese P. Thomas

The subject of Conflict‐Analysis and Resolution has received considerable attention in construction management. Providing measurement to tangibles and intangibles issues, involved…

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Abstract

The subject of Conflict‐Analysis and Resolution has received considerable attention in construction management. Providing measurement to tangibles and intangibles issues, involved in a conflict is not attempted often. The quantification of the perception of gains and losses for the parties involved in a conflict helps to analyze the issues scientifically, in a more logical manner. This paper presents the application of a decision‐making methodology, the analytical hierarchy process (AHP), to conflict management. The objective of this paper is analyzing and resolving conflict aided by the quantification of gains and losses using the AHP. This methodology was applied to a real conflict situation between two government agencies in Kuwait.

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Engineering, Construction and Architectural Management, vol. 11 no. 2
Type: Research Article
ISSN: 0969-9988

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Article
Publication date: 1 April 1997

HASHEM AL‐TABTABAI, NABIL KARTAM, IAN FLOOD and ALEX P. ALEX

Construction projects are susceptible to cost and time overruns. Variations from planned schedule and cost estimates can result in huge losses for owners and contractors. In…

509

Abstract

Construction projects are susceptible to cost and time overruns. Variations from planned schedule and cost estimates can result in huge losses for owners and contractors. In extreme cases, the viability of the project itself is jeopardised as a result of variations from baseline plans. Hence new methods and techniques which assist project managers in forecasting the expected variance in schedule and cost should be developed. This paper proposes a judgment‐based forecasting approach which will identify schedule variances from a baseline plan for typical construction projects. The proposed forecasting approach adopts multiple regression techniques and further utilises neural networks to capture the decision‐making procedure of project experts involved in schedule monitoring and prediction. The models developed were applied to a multistorey building project under construction and were found feasible for use in similar construction projects. The advantages and limitations of these two modelling process for prediction of schedule variance are discussed. The developed models were integrated with existing project management computer systems for the convenient and realistic generation of revised schedules at appropriate junctures during the progress of the project.

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Engineering, Construction and Architectural Management, vol. 4 no. 4
Type: Research Article
ISSN: 0969-9988

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Article
Publication date: 1 February 1999

HASHEM AL‐TABTABAI and ALEX P. ALEX

Genetic algorithm (GA) is a model of machine learning. The algorithm can be used to find sub‐optimum, if not optimum, solution(s) to a particular problem. It explores the solution…

516

Abstract

Genetic algorithm (GA) is a model of machine learning. The algorithm can be used to find sub‐optimum, if not optimum, solution(s) to a particular problem. It explores the solution space in an intelligent manner to evolve better solutions. The algorithm does not need any specific programming efforts but requires encoding the solution as strings of parameters. The field of application of genetic algorithms has increased dramatically in the last few years. A large variety of possible GA application tools now exist for non‐computer specialists. Complicated problems in a specific optimization domain can be tackled effectively with a very modest knowledge of the theory behind genetic algorithms. This paper reviews the technique briefly and applies it to solve some of the optimization problems addressed in construction management literature. The lessons learned from the application of GA to these problems are discussed. The result of this review is an indication of how the GA can contribute in solving construction‐related optimization problems. A summary of general guidelines to develop solutions using this optimization technique concludes the paper.

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Engineering, Construction and Architectural Management, vol. 6 no. 2
Type: Research Article
ISSN: 0969-9988

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Article
Publication date: 27 November 2023

Abdulrahman Sati and Hashem Al-Tabtabai

Lack of trust and poor quality of construction deliverables have become a serious matter nowadays. This is due to the absence of a uniform and decentralized system for managing…

178

Abstract

Purpose

Lack of trust and poor quality of construction deliverables have become a serious matter nowadays. This is due to the absence of a uniform and decentralized system for managing quality information. In Kuwait’s industry, many incidents have been recorded as a lack of confidence in the authenticity and integrity of the documented data in the system. This paper aims to shed the light on a framework that would tackle this matter.

Design/methodology/approach

A designed framework using Blockchain technology (Hyperledger Fabric) has been used to create a transparent and decentralized environment between the parties. A digitalized informative checklist referred to as “Smart Construction Inspection Checklist (SCIC)” has been initiated to enhance the poor information recorded between the parties.

Findings

The framework has provided a transparent, immutable, traceable and decentralized environment in which all parties are involved in transactions. In addition, the integration of the SCIC in the blockchain environment provided an advantage in which all the necessary criteria of inspection will be stated, checked by the consultant and validated by the client to approve the transaction. A preliminary testing has been conducted to support the proposed framework.

Originality/value

This study fulfils the gap in the state of art for further studies to practically apply the framework that will enhance the quality of information management in Kuwait’s industry.

Details

Construction Innovation , vol. 24 no. 1
Type: Research Article
ISSN: 1471-4175

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Article
Publication date: 1 February 2016

Ayedh Alqahtani and Andrew Whyte

The purpose of this paper is to compare the performance of regression and artificial-neural-networks (ANNs) methods to estimate the running cost of building projects towards…

940

Abstract

Purpose

The purpose of this paper is to compare the performance of regression and artificial-neural-networks (ANNs) methods to estimate the running cost of building projects towards improved accuracy.

Design/methodology/approach

A data set of 20 building projects is used to test the performance of these two (ANNs/regression) models in estimating running cost. The concept of cost-significant-items is identified as important in assisting estimation. In addition, a stepwise technique is used to eliminate insignificant factors in regression modelling. A connection weight method is applied to determine the importance of cost factors in the performance of ANNs.

Findings

The results illustrate that the value of the coefficient of determination=99.75 per cent for ANNs model(s), with a value of 98.1 per cent utilising multiple regression (MR) model(s); second, the mean percentage error (MPE) for ANNs at a testing stage is 0.179, which is less than that of the MPE gained through MR modelling of 1.28; and third, the average accuracy is 99 per cent for ANNs model(s) and 97 per cent for MR model(s). On the basis of these results, it is concluded that an ANNs model is superior to a MR model when predicting running cost of building projects.

Research limitations/implications

A means for continuous improvement for the performance of the models accuracy has been established; this may be further enhanced by future extended sample.

Originality/value

This work extends the knowledge base of life-cycle estimation where ANNs method has been found to reduce preparation time consumed and increasing accuracy improvement of the cost estimation.

Details

Built Environment Project and Asset Management, vol. 6 no. 1
Type: Research Article
ISSN: 2044-124X

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