Covariance effect analysis of similarity measurement methods for early construction cost estimation using case-based reasoning

Ahn, Joo‐Myung; Park, Moonseo; Lee, Hyunsoo; Ahn, Sung Joon; Ji, Sae-Hyun; Song, Kwonsik; Son, Bo-Sik

doi:10.1016/j.autcon.2017.04.009

Cited by 43 publications

(40 citation statements)

References 30 publications

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“…The distance measurement has received considerable concerns because it affects the performance and case retrieval of CBR [40,49,55]. Much research has been performed to confirm the effect of the distance measurement and improve the accuracy of the derived outcomes, as described in Table 1.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation of Distance Measurement Methods for Construction Noise Prediction Using Case-Based Reasoning

et al. 2019

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Concerns over environmental issues have recently increased. Particularly, construction noise in highly populated areas is recognized as a serious stressor that not only negatively affects humans and their environment, but also construction firms through project delays and cost overruns. To deal with noise-related problems, noise levels need to be predicted during the preconstruction phase. Case-based reasoning (CBR) has recently been applied to noise prediction, but some challenges remain to be addressed. In particular, problems with the distance measurement method have been recognized as a recurring issue. In this research, the accuracy of the prediction results was examined for two distance measurement methods: The weighted Euclidean distance (WED) and a combination of the Jaccard and Euclidean distances (JED). The differences and absolute error rates confirmed that the JED provided slightly more accurate results than the WED with an error ratio of approximately 6%. The results showed that different methods, depending on the attribute types, need to be employed when computing similarity distances. This research not only contributes an approach to achieve reliable prediction with CBR, but also contributes to the literature on noise management to ensure a sustainable environment by elucidating the effects of distance measurement depending on the attribute types.

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Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation of Distance Measurement Methods for Construction Noise Prediction Using Case-Based Reasoning

et al. 2019

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“…rough its development of more than 30 years, the application of CBR has continuously evolved from a single field to multiple fields. Ahn et al [8] used the weighted Mahalanobis distance to measure the similarity of case-based reasoning and established a cost estimation model based on case-based reasoning. Jiang et al [9] used rough sets to reduce the dimension of case features and determine the weight of case features.…”

Section: Case-based Reasoning Technology and Its Applicationmentioning

confidence: 99%

“…According to the CI and RI of the judgment matrix of each layer calculated above, the total consistency ratio CR is calculated as 0.0267 by formula (9), which is less than 0.1. e case features of the scheme layer pass the consistency test for the total weight of the target layer. Formula (8) calculates the total weight vector of each case feature to the target layer as W � {0.0267, 0.0485, 0.0882, 0.2475, 0.1645, 0.0773, 0.0504, 0.1223, 0.0847, 0.0436, 0.0283, 0.0180}. In practical applications, according to the different processing requirements of different parts, experts can change the scale value in the comparison judgment matrix to realize the calculation of the similarity degree with variable weight, which makes the optimization model more adaptable.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…To determine the values of σ and w th , in this paper, the three type cases "existing cases," "similar cases," and "large-different cases" in the case base are used to perform the simulation researches. Due to space constraints, only the partial simulation results of the gear 8 Mathematical Problems in Engineering parts are given. According to the expert experience, the similarity threshold S th � 0.7. e dimension characteristics of the gear parts are the height, outer diameter`, and inner diameter.…”

Section: Simulation Researchmentioning

confidence: 99%

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Research on the Optimization Model of the Abrasive Blocks Using Weighted Case‐Based Reasoning

Gao

Yang

Tian

et al. 2019

Mathematical Problems in Engineering

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Barrel finishing process is a universal method to improve the surface quality of parts. It is widely used in high-performance parts of high-end equipment. As a necessary tool consumable for barrel finishing process, the characteristic parameters of the abrasive blocks affect the processing quality and production efficiency. However, the current method for selecting the abrasive blocks requires large number of experiments based on the operator’s extensive experience, which does not meet the rapid development needs of the barrel finishing process. Therefore, this paper proposes a case-based reasoning model with variable weights to achieve the intelligent optimization of the abrasive blocks. Based on the in-depth analysis of the characteristics of the barrel finishing process, a reasonable case base is established firstly, which is to determine the comprehensive case features and the solution of the case. AHP (analytic hierarchy process) is proposed to determine the weight of case features and to dynamically adjust the weight of case features according to the characteristics of the parts to be processed and users’ processing requirements. The results show that the proposed case-based reasoning model with variable weights can quickly, accurately, and reasonably select the abrasive blocks during the process of making processing technique of the barrel finishing, which will lay a necessary foundation for the effective implementation of the barrel finishing process and contribute significantly to the improvement of its efficiency.

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“…In recent years, researchers in the field of construction [7], [11]- [14] have investigated and developed new models in an attempt to improve cost estimation and forecasting, mainly using case-based reasoning, reference class forecasting, and artificial neural networks.…”

Section: Cost Estimatesmentioning

confidence: 99%

Construction Cost Estimation of Brazilian Highways Using Artificial Neural Networks

Barros¹,

Marcy²,

Carvalho³

2018

IJSCER

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Estimating costs of construction projects more accurately at the project development stage is crucial for feasibility studies and it is a key factor for their success. Construction costs are often underestimated and recent statistical studies show that errors in cost estimation have not diminished. This paper focuses on the development of a more accurate estimation technique for construction highway projects using Artificial Neural Networks. Different architectures of the network with 10, 15, and 20 neurons were trained and tested with the backpropagation algorithm. Based on this, data from fourteen highway projects in Brazil were collected and analyzed. Eleven parameters that contribute the most to the construction final budget were found after trials and errors. For the best scenario, an average cost estimation accuracy of 99% was achieved. This preliminary study showed the feasibility of the tool applied to projects in Brazil and may be used by public agencies in the future. 

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Covariance effect analysis of similarity measurement methods for early construction cost estimation using case-based reasoning

Cited by 43 publications

References 30 publications

Performance Evaluation of Distance Measurement Methods for Construction Noise Prediction Using Case-Based Reasoning

Performance Evaluation of Distance Measurement Methods for Construction Noise Prediction Using Case-Based Reasoning

Research on the Optimization Model of the Abrasive Blocks Using Weighted Case‐Based Reasoning

Construction Cost Estimation of Brazilian Highways Using Artificial Neural Networks

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