In the present thesis, a method has been modified to predict the long term strength behaviour of thermoplastic materials. The method is based on the Stress Rate accelerated Creep Rupture (SRCR) test, which was previously developed for brittle materials and which was transferred to tough materials within this thesis. For this purpose, monotonic tests under different loading rates were carried out at room temperature as well as at elevated temperature. These tests were performed at various specific reference stresses and stress rates. By varying the stress rates from high to low, the time to failure was extrapolated to a stress rate of 0 MPa/s, which corresponds to a static test at the specific reference stress level. A conservative approach was chosen for the evaluation, in which yielding is considered as total failure. The extrapolation was evaluated using the determined yield stresses and the corresponding time until the yield stress occurs. Also, the linear dependence of the failure behaviour (in this particular case the yield stress) on the stress rate and the temperature was determined to allow extrapolation of the failure behaviour. This correlation is given for all materials, where the yield stress increases with increasing stress rate. The adaptation was performed on four conventional pipe materials - a polyamide 12 (PA12) with a minimum required strength (MRS) of 18 MPa, and three high density polyethylene types (PE HD), MRS = 10 MPa. The modified SRCR test has a high potential for predicting the long-term strength of thermoplastic materials. Due to some experimental conditions (temperature and stress rate) it is difficult to identify the yield stress, so this method has its limitations in the evaluation, particularly at high temperature ranges and low stress rates. Limitations were determined especially for PA12 – the stress rate should be ≥ 0.001 MPa/s and the temperature <80 °C, to allow a sufficient reproducibility. Static long-term measurements (e.g. creep tests) should be conducted to additionally support the accuracy of the accelerated prediction.