A Case-based Reasoning Approach for Identifying Risks in Construction Projects

2009 ◽  
Author(s):  
Y. Tan ◽  
N.J. Smith ◽  
D.A. Bower
Keyword(s):  
Construction Projects ◽  
Case Based Reasoning ◽  
Case Based

scholarly journals Framework for Risk Identification of Renewable Energy Projects Using Fuzzy Case-Based Reasoning

2020 ◽  
Vol 12 (13) ◽  
pp. 5231 ◽  
Author(s):  
Sahand Somi ◽  
Nima Gerami Seresht ◽  
Aminah Robinson Fayek
Keyword(s):  
Renewable Energy ◽  
Construction Projects ◽  
Wind Farm ◽  
Risk Identification ◽  
Case Based Reasoning ◽  
Onshore Wind ◽  
Energy Projects ◽  
Project Objectives ◽  
Tools And Techniques ◽  
Case Based

Construction projects are highly risk-prone due to both internal factors (e.g., organizational, contractual, project, etc.) and external factors (e.g., environmental, economic, political, etc.). Construction risks can thus have a direct or indirect impact on project objectives, such as cost, time, safety, and quality. Identification of these risks is crucial in order to fulfill project objectives. Many tools and techniques have been proposed for risk identification, including literature review, questionnaire surveys, and expert interviews. However, the majority of these approaches are highly reliant on expert knowledge or prior knowledge of the project. Therefore, the application of such tools and techniques in risk identification for renewable energy projects (e.g., wind farm and solar power plant projects) is challenging due to their novelty and the limited availability of historical data or literature. This paper addresses these challenges by introducing a new risk identification framework for renewable energy projects, which combines case-based reasoning (CBR) with fuzzy logic. CBR helps to solve problems related to novel projects (e.g., renewable energy projects) based on their similarities to existing, well-studied projects (e.g., conventional energy projects). CBR addresses the issue of data scarcity by comparing novel types of construction projects to other well-studied project types and using the similarities between these two sets of projects to solve the different problems associated with novel types of construction projects, such as risk identification of renewable energy projects. Moreover, the integration of fuzzy logic with CBR, to develop fuzzy case-based reasoning (FCBR), increases the applicability of CBR in construction by capturing the subjective uncertainty that exists in construction-related problems. The applicability of the proposed framework was tested on a case study of an onshore wind farm project. The objectives of this paper are to introduce a novel framework for risk identification of renewable energy projects and to identify the risks associated with the construction of onshore wind farm projects at the work package level. The results of this paper will help to improve the risk management of renewable energy projects during the construction phase.

Case-based reasoning and electromagnetism-like method in construction management

Kybernetes ◽  
2014 ◽  
Vol 43 (2) ◽  
pp. 265-280 ◽  
Author(s):  
Aleksandar Kartelj ◽  
Nebojša Šurlan ◽  
Zoran Cekić
Keyword(s):  
Construction Management ◽  
Construction Projects ◽  
General Pattern ◽  
Optimization Technique ◽  
Case Based Reasoning ◽  
Significant Implication ◽  
Case Retrieval ◽  
Content Type ◽  
Feature Weights ◽  
Case Based

Purpose – The presented research proposes a method aimed to improve a case retrieval phase of the case-based reasoning (CBR) system through optimization of feature relevance parameters, i.e. feature weights. Design/methodology/approach – The improvement is achieved by applying the metaheuristic optimization technique, called electromagnetism-like algorithm (EM), in order to appropriately adjust the feature weights used in k-NN classifier. The usability of the proposed EM k-NN algorithm is much broader since it can also be used outside the CBR system, e.g. for solving general pattern recognition tasks. Findings – It is showed that the proposed EM k-NN algorithm improves the baseline k-NN model and outperforms the appropriately tuned artificial neural network (ANN) in the task of predicting the case (data record) output values. The results are verified by performing statistical analysis. Research limitations/implications – The proposed method is currently adjusted to deal with numerical features, so, as a direction for future work, the variant of EM k-NN algorithm that deals with symbolic or some more complex types of features should be considered. Practical implications – EM k-NN algorithm can be incorporated as a case retrieval component inside a general CBR system. This is the future direction of the investigation since the authors intend to build a complete specialized CBR system for construction project management. The overall CBR with incorporated EM k-NN will have significant implication in the construction management as it will be able to produce more accurate prediction of viability and the life cycle of new construction projects. Originality/value – The electromagnetism-like algorithm is applied to the problem of finding feature weights for the first time. EM potential for solving the problem of weighting features lies in its internal structure because it is based on the real-valued EM vectors. The overall EM k-NN algorithm is applied on data sets generated from real construction projects data corpus. The proposed algorithm proved its efficiency as it outperformed baseline k-NN model and ANN. Its applicability in more complex and specialized CBR systems is high since it can be easily added due to its modular (black-box) design.

scholarly journals Construction Tender Subcontract Selection using Case-based Reasoning

2012 ◽  
Vol 6 (2) ◽  
pp. 32-43 ◽  
Author(s):  
Due Luu ◽  
Wily Sher
Keyword(s):  
Financial Stability ◽  
Construction Projects ◽  
Construction Project ◽  
Case Based Reasoning ◽  
Past Performance ◽  
Track Record ◽  
Daunting Task ◽  
Competitive Pricing ◽  
Case Based ◽  
Selection Of

Obtaining competitive quotations from suitably qualified subcontractors at tender tim n significantly increase the chance of w1nmng a construction project. Amidst an increasingly growing trend to subcontracting in Australia, selecting appropriate subcontractors for a construction project can be a daunting task requiring the analysis of complex and dynamic criteria such as past performance, suitable experience, track record of competitive pricing, financial stability and so on. Subcontractor selection is plagued with uncertainty and vagueness and these conditions are difficul_t o represent in generalised sets of rules. DeciSIOns pertaining to the selection of subcontr:act?s tender time are usually based on the mtu1t1onand past experience of construction estimators. Case-based reasoning (CBR) may be an appropriate method of addressing the chal_lenges of selecting subcontractors because CBR 1s able to harness the experiential knowledge of practitioners. This paper reviews the practicality and suitability of a CBR approach for subcontractor tender selection through the development of a prototype CBR procurement advisory system. In this system, subcontractor selection cases are represented by a set of attributes elicited from experienced construction estimators. The results indicate that CBR can enhance the appropriateness of the selection of subcontractors for construction projects.

scholarly journals Safety Early Warning Research for Highway Construction Based on Case-Based Reasoning and Variable Fuzzy Sets

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Yan Liu ◽  
Ting-Hua Yi ◽  
Zhen-Jun Xu
Keyword(s):  
Early Warning ◽  
Construction Projects ◽  
Highway Construction ◽  
Early Warning Systems ◽  
Construction Safety ◽  
Support Vector ◽  
Case Based Reasoning ◽  
Ability To Act ◽  
Case Based ◽  
Early Warnings

As a high-risk subindustry involved in construction projects, highway construction safety has experienced major developments in the past 20 years, mainly due to the lack of safe early warnings in Chinese construction projects. By combining the current state of early warning technology with the requirements of the State Administration of Work Safety and using case-based reasoning (CBR), this paper expounds on the concept and flow of highway construction safety early warnings based on CBR. The present study provides solutions to three key issues, index selection, accident cause association analysis, and warning degree forecasting implementation, through the use of association rule mining, support vector machine classifiers, and variable fuzzy qualitative and quantitative change criterion modes, which fully cover the needs of safe early warning systems. Using a detailed description of the principles and advantages of each method and by proving the methods’ effectiveness and ability to act together in safe early warning applications, effective means and intelligent technology for a safe highway construction early warning system are established.

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