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Quantifying change order impact on productivity by using ANN approach ECE 539 Project Presentation (Order: 316) Min-Jae Lee Construction Management Program.

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Presentation on theme: "Quantifying change order impact on productivity by using ANN approach ECE 539 Project Presentation (Order: 316) Min-Jae Lee Construction Management Program."— Presentation transcript:

1 Quantifying change order impact on productivity by using ANN approach ECE 539 Project Presentation (Order: 316) Min-Jae Lee Construction Management Program Civil & Environmental Engineering Department University of Wisconsin - Madison

2 Research Background  Productivity loss (Delta:  ) happen  “Owner” & “Contractor” conflict ---Claims  We need “Models” developed by historical data Model 1: Was the project impacted by change orders or not Model 2: How much impacted by change orders Direct Field Labor Hours Construction Phase (% Complete) 0100 Estimate Base Change Delta  Total Actual Labor Hours (Base + Change) (Hanna et al. 1999a, 1999b)

3 Data Characteristics  140 case study from U.S. area [impacted(50) / unimpacted(50)]  Ask 70 Indicator factors related with change orders  Find “significant factors” by using Statistical method (20 factors, correlation test, significant test)

4 Previous Research & ANN approach Model1(Impact): bp 20 feature factors Impacted or Not Model2(% Delta ): RBN % Delta Output Training data size: 100 cases, Testing sample size: 30 cases 20 feature factors Model2: Regression Model % delta = + 0.36866 + 0.11957 Percent Change - 0.08065 PM%TimeOnProject - 0.16723 %OwnerInitiatedCO - 0.09147 Productivity -0.05213 Overmanning + 0.022345 ProcessingTime Model1: Logistic Regression 75% Accuracy Average = =72.2% %Error lX actual – X estimated l X estimated

5 Results & Discussion Model1(Impact): bp Model2(% Delta ): RBN Logistic Regression C_rate = 71% Training C_rate = 87% Testing C_rate = 82% Average %Error = lX actual – X estimated l X estimated Regression Model: 73% RBN Model: 14%


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