The constitutive model is essential for predicting the deformation and stability of rock-soil mass. The estimation of constitutive model parameters is a necessary and important task for the reliable characterization of mechanical behaviors. However, constitutive model parameters cannot be evaluated accurately with a limited amount of test data, resulting in uncertainty in the prediction of stress-strain curves. This paper proposes a Bayesian analysis framework to address this issue. It combines the Bayesian updating with the structural reliability and adaptive conditional sampling methods to assess the equation parameter of constitutive models. Based on the triaxial and ring shear tests on shear zone soils from the Huangtupo landslide, a statistical damage constitutive model and a critical state hypoplastic constitutive model were used to demonstrate the effectiveness of the proposed framework. Moreover, the parameter uncertainty effects of the damage constitutive model on landslide stability were investigated. Results show that reasonable assessments of the constitutive model parameter can be well realized. The variability of stress-strain curves is strongly related to the model prediction performance. The estimation uncertainty of constitutive model parameters should not be ignored for the landslide stability calculation. Our study provides a reference for uncertainty analysis and parameter assessment of the constitutive model.

Soil salinization in arid and coastal areas poses a significant threat to crop production, which is further aggravated by climate change and the over-exploitation of aquifers. Cultivation of salt and drought-tolerant crops such as quinoa represents a promising adaptation pathway for agriculture in saline soils. Quinoa (Chenopodium quinoa Willd.) is a salt-loving plant, known for its tolerance to drought and salinity using complex stress responses. However, available models of quinoa growth are limited, particularly under salinity stress. The objective of this study was to calibrate the crop growth, and salinity and drought stress parameters of the SWAP - WOFOST model and evaluate whether this model can represent quinoa's stress tolerance mechanisms. Field experimental data were used from two quinoa varieties: ICBA-Q5 grown under saline conditions in Laayoune, Morocco, in 2021, and Bastille grown under rainfed, non-saline conditions in Merelbeke, Belgium, from 2018 to 2023. Calibration and parameter uncertainty was performed using the DiffeRential Evolution Adaptive Metropolis (DREAMzs) algorithm on key parameters identified via sensitivity analysis using the Morris method. The resulting crop parameters provide insights into the stress tolerance mechanisms of quinoa, including reduction of transpiration and uptake of solutes. The salinity stress function of SWAP effectively represents these tolerance mechanisms and accurately predicts the impact on yield, under arid conditions. Under Northwestern European climate, the model replicates the impact of drought stress on yield. The calibrated model offers perspectives for evaluating practices to reduce soil salinization in arid conditions and for modeling crop performance under water-limited conditions or future salinization in temperate regions.

Dynamic soil-structure interaction (SSI) is an important field in civil engineering with applications in earthquake engineering, structural dynamics, and structural health monitoring (SHM). There is an ongoing need for the development of numerical methods that can accurately estimate SSI parameters to model these systems. In this paper, a Frequency Response Function (FRF)-based model updating method is developed that can estimate the embedded length of foundation piles, in addition to the mobilized soil mass and stiffness, when a lateral impact load is applied. Knowledge of the embedded length of piles is very important for modelling foundation behaviour, and may not be readily available from as-built construction information. For example, if developing reference damage models or digital twins of foundation structures, full knowledge of the pile geometry is required. The work in this paper develops a two-stage iterative model updating method, which utilizes FRF data obtained at the pile ' s head as a result of an applied lateral impact load. The method uses information from the 1st mode of vibration to estimate the mobilised soil mass and stiffness, and subsequently uses information from the 2nd mode of vibration to estimate the embedded length. To appraise the approach, impact tests are numerically simulated on a number of 'piles ' (numerical spring-beam systems) with varying length/diameter ( L/D ) ratios to derive FRFs, whereby the models have known length and dynamic properties. These FRFs are then used as targets in the model updating approach, which iteratively varies the properties of a numerical model of a pile to obtain a match in the FRF data, and subsequently estimates the mobilised stiffness, mass, and embedded length. The results of the analyses illustrate that by minimising the difference in the first and second FRF peaks between the target and estimated FRFs, the method can accurately estimate the mass, stiffness and embedded length properties of the test 'piles ' . The performance of the approach against numerical case applications is assessed in this paper, as the properties of these systems are known in advance, facilitating quantification of the errors and performance. The developed method requires further validation through full-scale testing to confirm its effectiveness in real-world scenarios.