Assessment of productivity for concrete bored pile construction
Abstract
The installation or construction of pile foundations is complicated by an enormous number of problems relating to subsurface obstacles, lack of contractor experience, and site planning difficulties. These problems greatly affect the production of concrete piles on site. Consequently, it is difficult for the estimator to evaluate piling productivity. Therefore, it is necessary to use highly sophisticated techniques to analyze the problem and determine the closest optimal solution. This study highlights the problem features and solution. The objectives of this study are to address and evaluate the factors that affect pile construction productivity and cost, assess them using four different techniques, compare their results, and conclude the model that best fit the pile construction process. The four techniques are deterministic, simulation, regression, and Artificial Neural Network (ANN). Data were collected for this study through questionnaires, site interviews, and telephone calls to experts in different construction pile companies. As a result, four different models have been developed using the above-mentioned techniques to assess productivity and cost. Validation was performed where the designed models' results closely capture real world practice. A comparison was made among the designed models' results to decide the model that best fit the pile construction problem. It was shown, based on the collected data set, that the ANN model gives results closest to real world practice. Its outcome has a potential deviation of 18.66% from the collected productivity. Sets of charts have been developed to predict productivity, cycle time, and cost of piling process considering 1080 combinations of input variables. These sets of charts contain great benefits for the piling process estimator in planning and scheduling piling machines.
Degree
Ph.D.
Advisors
Halpin, Purdue University.
Subject Area
Civil engineering
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