The main aim of this study was to produce landslide susceptibility maps using statistical index (SI), certainty factors (CF), weights of evidence (WoE) and evidential belief function (EBF) models for the Long County, China. Firstly, a landslide inventory map, including a total of 171 landslides, was compiled on the basis of earlier reports, interpretation of aerial photographs and supported by extensive field surveys. Thereafter, all landslides were randomly separated into two data sets: 70% landslides (120 points) were selected for establishing the model and the remaining landslides (51 points) were used for validation purposes. Eleven landslide conditioning factors, such as slope aspect, slope angle, plan curvature, profile curvature, altitude, distance to faults, distance to roads, distance to rivers, lithology, NDVI and land use, were considered for landslide susceptibility mapping in this study. Then, the SI, CF, WoE and EBF models were used to produce the landslide susceptibility maps for the study area. Finally, the four models were validated using area under the curve (AUC) method. According to the validation results, the EBF model (AUC = 78.93%) has a higher prediction accuracy than the SI model (AUC = 77.72%), the WoE model (AUC = 77.62%) and the CF model (AUC = 77.72%). Similarly, the validation results also indicate that the EBF model has the highest training accuracy of 80.25%, followed by SI (79.80%), WoE (79.71%) and CF (79.67%) models.

In this study, a refined finite element model was built that represented the structural and mechanical properties of railway bridges. A coupled vehicle–bridge vibration model was established to simulate the dynamic behavior of the bridge under moving trains. Field tests were then conducted to determine the free vibration characteristics as well as the strain, displacement, and acceleration of the bridge structure under trains moving at different speeds and braking at a specified position from a set speed. The dynamic response of the bridge was found to increase with the train speed, but the main beam of the bridge was more affected by a train braking than the one passing over it. Both the bridge and vehicle were found to meet the relevant requirements for safe design and operation, with the train exhibiting good running safety and smoothness, and the bridge structure showing sufficient stiffness and dynamic performance. Based on the field measurements and analytical modeling, it can be concluded that the use of appropriate equipment and the selection of a suitable layout for the sensors permit the assessment of the dynamic behavior of the bridges from vibration measurements. This indicates the potential for the continuous health monitoring of the railway bridges that may be sensitive to the vehicle–bridge interaction.

Soil compaction caused by agricultural machinery has been increasingly recognized as a considerable problem facing intensive agriculture. Most of the models used to estimate soil deformation during the passage of machines are based on the concept of total stress: they have neglected an important stress variable for unsaturated soils; that is, the matric suction. The aim of the present work was to evaluate the validity of this hypothesis by studying suction variation during a static compression test. A standard oedometer cell equipped with a tensiometer was used to measure soil suction in situ for different vertical stresses. Measurements were carried out on remoulded soil samples obtained by compacting a loamy soil at different initial water suctions (<100 kPa). The results showed that the suction remained almost constant until a stress threshold value, σ _t , beyond which the suction decreased as the stress increased. This stress threshold increased with the initial suction. These results corroborated the hypothesis of a constant suction during deformation, which is usually assumed to model soil compaction during traffic for soils with suction greater than 20 kPa. The results obtained highlighted the effect of soil structure on the stress threshold: σ _t was greater for soil samples with initial aggregates <2 mm than for those with initial aggregates <0.4 mm. This was interpreted at the pore scale by comparing qualitatively the change in pore‐size distribution and the expected distribution of water in the pores. This interpretation was based on pore‐size distribution measurement by mercury intrusion.