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Revised Case-Based Reasoning Model Development Based on Multiple Regression Analysis for Railroad Bridge Construction

机译:基于多元回归分析的铁路桥梁施工案例推理修正模型开发

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摘要

Many large construction projects are being carried out simultaneously. The accuracy of the budget allocated in the planning phase of such projects is considered a key element in efficient budget use, but lack of information during the planning phase results in the inaccurate estimation of the construction cost. Thus, it is necessary to devise a method that improves the accuracy of construction cost estimation in the planning phase. Although recently there has been an increase in the use of case-based reasoning (CBR) for construction cost estimation, the use of CBR tends to reduce the accuracy of the estimated construction cost, unless there is sufficient similarity between the cases stored in the database and the retrieved cases. Therefore, a revised CBR model based on the regression analysis model was developed in this study, and a calculation model capable of estimating the construction cost in the planning phase was developed with a focus on railroad-bridge construction projects. To verify the revised CBR model, five case studies were conducted. The results showed that the revised CBR model reduced the construction cost error rate of the proposed CBR model by 16.2%. In particular, it is expected that the revised CBR model will be useful when there is a lack of similarity between the cases stored in the database and the retrieved cases.
机译:许多大型建筑项目正在同时进行。在此类项目的规划阶段分配预算的准确性被认为是有效使用预算的关键因素,但是在规划阶段缺乏信息会导致对建筑成本的估算不准确。因此,有必要设计一种在计划阶段提高建筑成本估算准确性的方法。尽管最近基于案例的推理(CBR)用于建筑成本估算的使用有所增加,但除非数据库中存储的案例之间有足够的相似性,否则使用CBR往往会降低估算的建筑成本的准确性。和检索到的案例。因此,本研究基于回归分析模型开发了修正的CBR模型,并针对铁路桥梁建设项目开发了能够在规划阶段估算建筑成本的计算模型。为了验证修订后的CBR模型,进行了五个案例研究。结果表明,修订后的CBR模型使该CBR模型的建造成本错误率降低了16.2%。特别是,当数据库中存储的案例与检索到的案例之间缺乏相似性时,预期修订的CBR模型将很有用。

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