|Table of Contents|

Cost model prediction of minor repair project of bridge and culvert based on machine learning method(PDF)

《建筑科学与工程学报》[ISSN:1673-2049/CN:61-1442/TU]

Issue:
2023年04期
Page:
125-134
Research Field:
桥梁工程
Publishing date:

Info

Title:
Cost model prediction of minor repair project of bridge and culvert based on machine learning method
Author(s):
SHI Xiaoli1;  WANG Xiaowei2;  QIU Chenyang3;  GAO Nan4
(1. School of Highway, Chang'an University, Xi'an 710064, Shaanxi, China; 2. China Road & Bridge Corporation, Beijing 100011, China; 3. JSTI Group Co., Ltd., Nanjing 210019, Jiangsu, China; 4. Shaanxi Transportation Holding Group Co., Ltd., Xi'an 710065, Shaanxi, China)
Keywords:
bridge and culvert asset;  minor repair cost;  grey correlation degree;  Ridge regression;  Lasso regression
PACS:
U445.2
DOI:
10.19815/j.jace.2021.12106
Abstract:
Based on the historical data of bill of quantities of bridge and culvert minor repair project on 11 expressways in Shaanxi province from 2008 to 2015, the machine learning algorithm was used to study the prediction model of bridge and culvert minor repair cost with the minor repair cost of each component of bridge and culvert as the dependent variable. Through the grey correlation degree model, the influencing factors of the minor repair cost of bridge and culvert were analyzed. The Pearson correlation coefficient test method was used to test the multicollinearity of each influencing factor, and the explanatory variables were selected. Ridge regression and Lasso regression were used to analyze the minor repair cost model of each component of bridge and culvert, and the total cost prediction model of minor repair of bridge and culvert was obtained. The results show that the main influencing factors of the cost of bridge and culvert minor repair projects are years of operation, length of bridge(culvert), annual average equivalent axles, annual average rainfall and temperature in the area where the bridges and culverts are located, and number of lanes. Based on the model prediction, the bridge and culvert minor repair cost of the expressway from 2016 to 2017 is carried out, and the Wilcoxon signed rank test is carried out with the actual cost of the year. The test results are all greater than 0.05, which verifies the effectiveness of the machine learning method to predict the bridge and culvert minor repair project cost model. The prediction results can provide reasonable suggestions for allocating maintenance costs and improving maintenance decision-making level.

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Last Update: 2023-07-01