LONG-TERM TESTING OF TRENCHLESS PIPE LINERS

摘要

Due to increasing costs and inconveniences in replacing deteriorated sewer pipelines by conventional excavation methods, the trenchless or 'no-dig' technology is being extensively used. In trenchless technology, a polymer or reinforced polymer is applied to the inside of the deteriorated host pipe to prevent ground water from seeping into the sewer pipelines. In this research, a testing method was developed to determine the long-term creep behavior of encased polymer liners used in sewer rehabilitation. Short-term tests, following the ASTM D790 procedure, were conducted on pipe liner samples to determine the initial elastic modulus and compare it to the elastic modulus obtained from long-term testing of the encased liner. Long-term tests were conducted on 6 ft. lengths, 12 in diameter polymer liner samples encased in steel pipes. Fabrication service was provided by the industries participating in the research. The thicknesses of the polymer liners were selected according to the typical use of each product in the field. Three samples each of five liner materials were tested under constant external hydrostatic pressure to find their long-term structural properties and to present creep-buckling models. A pressure regulator, pressure transducer, and several pressure gages at different points in the water line were used to maintain constant hydraulic pressure in the gap between the steel host and the polymer liner. A novel method was developed for sealing the ends of the encased liner samples for testing. The long-term creep data was collected with strain gages bonded along the inner circumference of the liner and connected to a Data Acquisition System (DAS). The temperature of the liners was monitored continuously with the use of a thermocouple. The strain data collected from the DAS was compensated for differences in temperature throughout the period of testing, initial deformation, and coefficient of thermal expansion. Several viscoelastic models were investigated in order to fit the data. The data is used to predict the long-term modulus used in design.