Adaptation of a Cost Overrun Risk Prediction Model to the Type of Construction Facility

Abstract

To assess the risk of project cost overrun, it is necessary to consider large amounts of symmetric and asymmetric data. This paper proposes a cost overrun risk prediction model, the structure of which is based on the fuzzy inference model of Mamdani. The model consists of numerous inputs and one output (multi-input-single-output (MISO)), based on processes running consecutively in three blocks (the fuzzy block, the interference block, and the block of sharpening the representative output value). The input variables of the model include the share of element costs in the building costs (SE), predicted changes in the number of works (WC), and expected changes in the unit price (PC). For the input variable SE, it is proposed to adjust the fuzzy set shapes to the type of building object. Single-family residential buildings, multi-family residential buildings, office buildings, highways, expressways, and sports fields were analyzed. The initial variable is the value of the risk of exceeding the costs of a given element of a construction investment project (R). In all, 27 rules were assumed in the interference block. Considering the possibility of applying sharpening methods in the cost overrun risk prediction model, the following defuzzification methods were investigated: the first of maxima, middle of maxima, and last of maxima method, the center of gravity method, and the bisector area method. Considering the advantages and disadvantages, the authors assumed that the correct and basic defuzzification method in the cost overrun risk prediction model was the center of gravity method. In order to check the correctness of the assumption made at the stage of designing the rule database, result diagrams were generated for the relationships between the variable (R) and the input variables of individual types of buildings. The results obtained confirm the correctness of the assumed assumptions and allow to consider the input variable (SE), adjusted individually to the model for each type of construction object, as crucial in the context of the impact on the output value of the output variable (R).

Energy Efficiency with Internet of Things Based Fuzzy Inference System for Room Temperature and Humidity Regulation

“…There are several methods that can be used in the defuzzification process, namely (1) Weighted Average Method (58)(59)(60), a method that calculates the average value by assigning a certain weight or weight to each element in the data set aimed at reflecting the relative importance or contribution of each element to the final result, (2) Mean-Max Membership (61)(62)(63), a method that combines several overlapping membership rules by taking the maximum value of each set membership at a point, then calculate the average of those maximum values, (3) Centroid (Center of Gravity) Method (64,65), a method that calculates the center point (centroid) of a membership set using the weighted principle based on membership level. The membership value of each point on the set is multiplied by the position of that point, then added and divided by the total membership value, (4) Height Method (Max-Membership Principle) (66,67), a method that takes the maximum value of the membership level in a membership set as a representation of the membership value of the entire set, (5) Center of Sums (68-70), a method that calculates the center of input values by adding input points and dividing them by the total number of inputs, (6) First (or Last) of Maxima (66,71,72), a method that selects the first (or last) point at which the membership level reaches the maximum value of a membership set as a representation of the overall membership value of that set, and (7) Center of Largest Area (73,74), a method that calculates the center of the area of a membership set by taking the midpoint at the interval with the largest set area. The defuzzification method used in this study is the Weighted Average written in Equation 5.…”

Symmetric and Asymmetric Data in Solution Models

“…The application fields of the proposed solution models rather often involved different engineering problems. Much attention was given to civil engineering in terms of construction project management [56,58] and the analysis of building structures [59,61]. Three papers analysed the optimisation of supply chains [62,64,66].…”

Life-cycle cost estimation of a building structure: An example of partition walls

“…If the documentation is processed in the earlier stages of the project, it contains less detailed data and cost predictions become less accurate. To make such cost estimation possible and more accurate, various prediction models have been proposed, for example those based on multiple and stepwise regression, support vector machine, neural networks and fuzzy inference (Xie and Fang 2018;Leśniak et al 2020;Plebankiewicz and Wieczorek 2020;Fan and Sharma 2021). Special models were developed, for example for refurbishment works on historical buildings (Śladowski et al 2019) or for accounting for risks (Plebankiewicz et al 2021).…”