Construction cost prediction on the basis of multiple parameters using case-based reasoning method

Rashid, Ekbal

doi:10.1504/ijstm.2017.088155

Cited by 5 publications

(3 citation statements)

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“…The similarity of these two vectors will increase as Distfalse(false) decreases. Rashid [41] and Mousa and Yusof [42] used the Chebyshev distance for similarity measurement.…”

Section: Related Workmentioning

confidence: 99%

A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System

Zhai

Ortega

Castillejo

et al. 2019

Sensors

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Case-based reasoning has been a widely-used approach to assist humans in making decisions through four steps: retrieve, reuse, revise, and retain. Among these steps, case retrieval plays a significant role because the rest of processes cannot proceed without successfully identifying the most similar past case beforehand. Some popular methods such as angle-based and distance-based similarity measures have been well explored for case retrieval. However, these methods may match inaccurate cases under certain extreme circumstances. Thus, a triangular similarity measure is proposed to identify commonalities between cases, overcoming the drawbacks of angle-based and distance-based measures. For verifying the effectiveness and performance of the proposed measure, case-based reasoning was applied to an agricultural decision support system for pest management and 300 new cases were used for testing purposes. Once a new pest problem is reported, its attributes are compared with historical data by the proposed triangular similarity measure. Farmers can obtain quick decision support on managing pest problems by learning from the retrieved solution of the most similar past case. The experimental result shows that the proposed measure can retrieve the most similar case with an average accuracy of 91.99% and it outperforms the other measures in the aspects of accuracy and robustness.

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“…The similarity of these two vectors will increase as Distfalse(false) decreases. Rashid [41] and Mousa and Yusof [42] used the Chebyshev distance for similarity measurement.…”

Section: Related Workmentioning

confidence: 99%

A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System

Zhai

Ortega

Castillejo

et al. 2019

Sensors

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“…The CBR approach remains again the chosen technique by Rashid (2017) as an effective and efficient method for cost prediction for building construction. The retrieval processes depends on the computation of similarity measures.…”

Section: Project Performance Prediction Modelsmentioning

confidence: 99%

Neural network models for actual cost prediction in Greek public highway projects

Aretoulis

2019

IJPOM

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Selected public Greek highway projects are examined in order to produce models to predict their actual construction cost based on data available at the bidding stage. Twenty highway projects, constructed in Greece, with similar type of available data were examined. Considering each project's attributes and the actual cost, correlation analysis is implemented, with the aid of SPSS. Correlation analysis identified the most predictive project variables. Additionally, the WEKA application, through its attribute selection function, highlighted the most efficient subset of variables. These selected variables through correlation analysis and WEKA are used as input neurons for neural network models. FANN Tool is used to construct neural network models. The optimum neural network model produced a mean squared error with a value of 7.68544E-05 and was based on budgeted cost, lowest awarding bid, technical work cost and electromechanical work cost.

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“…The few applications of CBR in construction contexts reveal that there are similarities among construction projects, or lessons learned, which can be used to solve problems in novel cases [11]. A few examples of the applications of CBR in construction include the model developed for predicting the cost of residential building projects [12], as well as the model developed for safety risk management [13]. However, CBR has not yet been used for construction risk identification.…”

Section: Introductionmentioning

confidence: 99%

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

Somi¹,

Seresht²,

Fayek³

2020

Sustainability

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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.

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Construction cost prediction on the basis of multiple parameters using case-based reasoning method

Cited by 5 publications

References 0 publications

A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System

A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System

Neural network models for actual cost prediction in Greek public highway projects

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

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