It is acknowledged that various sources of uncertainties play a vital role in the seismic vulnerability of slope systems, while many studies ignore these sources in seismic assessments. This is because seismic performance and fragility evaluation of large soil-structure systems is challenging and computationally intensive by conventional nonlinear dynamic analysis methods, especially when the modeling uncertainties are considered. To address this challenge, this paper proposes a new framework for addressing uncertainties in the seismic evaluation of earth slopes using the Endurance Time Analysis (ETA) method. The ETA method is a dynamic pushover procedure in which the slope is subjected to a limited number of artificial intensifying records, and seismic responses are obtained over a continuous range of seismic intensities. For the purpose of this study, probabilistic two-dimensional numerical simulations of earth slopes are created using the FLAC software by considering the soil parameters uncertainty. Latine Hypercube Sampling is employed to generate random simulations. The models are then subjected to the intensifying prefabricated excitations based on the ETA method, and the fragility curves of the slope are obtained in three damage states by considering and not considering uncertainties. The results indicate that as the endurance time, which is a kind of intensity measure, increases, the uncertainties of seismic responses also increase. This shows that the effects of uncertainties become more significant when the slope is subjected to strong ground motions. Additionally, the influence of modeling uncertainty is negligible in the slight damage state, but significant in the extensive damage state. The proposed framework provides an effective and rapid way for performing the fragility and associated risk analysis of earth slopes considering uncertainties.

The increasing global demand for sustainable agriculture requires accurate and efficient soil analysis methods. Conventional laboratory techniques are often time-consuming, costly and environmentally damaging. To address this challenge, we developed and validated locally calibrated mid-infrared (MIR) spectroscopy models for predicting key soil properties pH, phosphorus (P) and exchangeable cations in soil samples from South Africa's Western Highveld region, using a dataset of 979 soil samples and machine learning algorithms Cubist, partial least squares regression (PLSR) and random forest (RF). A subset of spectra was also submitted to the newly developed Open Soil Spectral Library's (OSSL) prediction models to determine whether global prediction models could be used for local soil property prediction. Accurate predictions for pH, calcium (Ca) and magnesium (Mg), with coefficient of determination (R-2) values exceeding 0.76 were obtained with the local calibration algorithms. The predictions for P, potassium (K) and sodium (Na) did not meet the requirements for reliability. Soil spectroscopic prediction models calibrated with local soils outperformed the corresponding global prediction models considered. The OSSL prediction results were inaccurate, with a RPIQ <1, and consistently underpredicted all soil properties. Furthermore, the OSSL collection of prediction models does not include a pH (KCl) model, the routinely used pH measurement method in South Africa. These findings highlight the importance of local calibration for accurate soil property prediction and underscore the need for regional representation in global spectral libraries. This research serves as the first local calibration of MIR spectroscopy models for the Western Highveld region of South Africa and provides a foundation for future local soil property inference model development. It also serves as a potential starting point for a comprehensive South African soil spectral library that can be contributed to global spectral libraries.