HR: 1340h
AN: H23A-1126    [Abstracts]
TI: Estimating Vertical Surface Movement using Artificial Neural Networks
AU: Doris, J J
EM: jdoris@emba.uvm.edu
AF: University of Vermont, 213 Votey Bldg, Burlington, VT 05405 United States
AU: * Rizzo, D M
EM: drizzo@emba.uvm.edu
AF: University of Vermont, 213 Votey Bldg, Burlington, VT 05405 United States
AU: Dewoolkar, M
EM: mandar@emba.uvm.edu
AF: University of Vermont, 213 Votey Bldg, Burlington, VT 05405 United States
AB: Artificial Neural Networks (ANNs) are used to predict vertical surface movement when soils expand and contract with changes in soil moisture caused by climatic conditions. Temperature and rainfall data, soil property data, and soil moisture measurements are used for training ANNs to simulate the movement of spread footings at a field site in Arlington, Texas. A research team from Texas A&M University surveyed the footing movement monthly over a two-year period. The performance of the ANNs is evaluated by comparing the predictions to the observed movements. Data for temperature and rainfall are available for each month of the two-year field study, but soil moisture data (on which soil shrinking/swelling is predominantly dependent) is only available for seven of those months. Therefore, two ANNs were used in series. The first ANN estimates soil moisture for the months when data are not available, while the second ANN uses both measured and estimated soil moisture as training input to estimate vertical movement.
DE: 1866 Soil moisture
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting