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Quantifying change order impact on productivity by using ANN approach

Explore the impact of change orders on productivity using statistical and regression models, based on historical data analysis. Identify significant factors influencing project outcomes and assess the extent of change order impact through logistic and regression analyses. Training and testing data sizes, along with model accuracies, are discussed.

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Quantifying change order impact on productivity by using ANN approach

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  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. Change Delta  Base Direct Field Labor Hours Estimate Total Actual Labor Hours (Base + Change) (Hanna et al. 1999a, 1999b) 0 100 Construction Phase (% Complete) 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

  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. Model1: Logistic Regression • 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 lXactual – Xestimatedl Xestimated Average = =72.2% %Error 75% Accuracy Previous Research & ANN approach Training data size: 100 cases, Testing sample size: 30 cases Model1(Impact): bp Model2(%Delta): RBN Output Output 20 feature factors 20 feature factors Impacted or Not % Delta

  5. Results & Discussion Model1(Impact): bp Logistic Regression C_rate = 71% Training C_rate = 87% Testing C_rate = 82% Model2(%Delta): RBN Regression Model: 73% RBN Model: 14% lXactual – Xestimatedl Xestimated Average %Error =

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