The application of a Fuzzy Adaptive Learning Control Network in cost estimates for road bridges

Abstract

In general, costs are an important aspect long before the construction of a civil project starts. In the conceptual phase, which is the first phase of a civil construction project, little cost information is available, and the cost estimate is at that stage of project development less accurate than when the project is almost completed. When a cost estimate is made with a conventional method, such as SSK, in the conceptual phase of a civil construction project, the Association for the Advancement of Cost Engineering, AACE, argues that the error of a cost estimate compared to the real price can be 15-50%. The lower accuracy in the conceptual phase is caused by several obstacles: a lack of estimating experience, lack of information, and a method that cannot calculate an accurate cost estimate. Besides, the conventional approach is time-consuming. Machine learning models can overcome these obstacles. This research is initiated by a practical problem within the company Witteveen+Bos. Witteveen+Bos uses a conventional approach for cost estimates and wants to improve the accuracy and calculation time of cost estimates of road bridges in the conceptual phase of a project. This research aims to improve the cost estimates of bridges in the conceptual phase of bridge projects by use of the machine learning model called FALCON. This model is already successful applied in the field of cost estimates for construction project types. Improvement that should be realized in the reduction of the calculation time from hours to minutes. Besides the research aims to reduce the maximum deviation of estimates for road bridge projects to 30%. The FALCON model is not the only model to solve an estimation problem. Standard models, suitable for estimation problems, are multiple linear regression, K nearest neighbors (KNN), and decision trees. The results show that the average absolute deviation is 34% for the KNN model, 36% for the decision tree regression model, 57% for the multiple linear regression model, and 24% for the FALCON model. All 4 models are able to calculate results within a couple of minutes. FALCON is the most accurate model The error of the individual predictions of the FALCON model roughly corresponds to the bandwidth of the AACE (15-50%) for the conceptual phase of a project. The validation of the FALCON model is realized through interviews with cost estimators of Witteveen+Bos. These interviews showed that the realized results meet the expectations of the interviewees. Besides, the interviewees are willing to implement the model in practice. In the end, it must be concluded that the FALCON model can calculate a cost estimate, of bridge projects in the conceptual phase, more quickly, and with a comparable accuracy level as with conventional methods that are used today. For future research, it is recommended to use a larger dataset that has less variation. The used dataset consists of 39 bridge projects. Besides, it is recommended to use international standards for data structuring instead of national standards.