Landslides are typically triggered by earthquakes or rainfall occasionally a rainfall event followed by an earthquake or vice versa. Yet, most of the works presented in the past decade have been largely focused at the single event-susceptibility model. Such type of modeling is found insufficient in places where the triggering mechanism involves both factors such as one found in the Chuetsu region, Japan. Generally, a single event model provides only limited enlightenment of landslide spatial distribution and thus understate the potential combination-effect interrelation of earthquakes- and rainfall-triggered landslides. This study explores the both-effect of landslides triggered by Chuetsu-Niigata earthquake followed by a heavy rainfall event through examining multiple traditional statistical models and data mining for understanding the coupling effects. This paper aims to compare the abilities of the statistical probabilistic likelihood-frequency ratio (PLFR) model, information value (InV) method, certainty factors (CF), artificial neural network (ANN) and ensemble support vector machine (SVM) for the landslide susceptibility mapping (LSM) using high-resolution-light detection and ranging digital elevation model (LiDAR DEM). Firstly, the landslide inventory map including 8459 landslide polygons was compiled from multiple aerial photographs and satellite imageries. These datasets were then randomly split into two parts: 70% landslide polygons (5921) for training model and the remaining polygons for validation (2538). Next, seven causative factors were classified into three categories namely topographic factors, hydrological factors and geological factors. We then identified the associations between landslide occurrence and causative factors to produce LSM. Finally, the accuracies of five models were validated by the area under curves (AUC) method. The AUC values of five models vary from 0.77 to 0.87. Regarding the capability of performance, the proposed SVM is promising for constructing the regional landslide-prone potential areas using both types of landslides. Additionally, the result of our LSM can be applied for similar areas which have been experiencing both rainfall-earthquake landslides.

The Sichuan-Tibet Railway (STR) in China is of profound significance for Tibet's long-term development. Construction of railway reshapes the landscape it passes through and consequently influences the geological hazard risk. Despite the importance of railway construction, there remains a paucity of previous studies involving this factor. In this paper, we make a straightforward development on our previous study that a novel index system, notably considering the effect of railway construction is proposed for geological hazard assessment. Different from regional assessment, the concept of the line element is introduced, and the geological hazard risk along STR is comprehensively evaluated using matter-element extension model (MEEM), gray correlation model (GCM), and support vector machine (SVM). Receiver operating characteristic curve (ROC) analysis demonstrates that SVM performs better among the models. Results further indicate that a reduced performance of SVM in the ROC test appears to accompany with the absence of the construction factor. This finding highlights the essential role of construction in geological hazard assessment for the railway.

The concept of digital twin (DT) has emerged as a trend in various industries development, enabling the creation of virtual models of physical objects. We conduct a systematic review of the DT technology in the field of transportation infrastructure management from the aspects of concept definition, whole life cycle application, advanced technology, and equipment utilization, as well as the challenges. We begin with an introduction that defines DT and its components, while also distinguishing it from building information modeling and cyber-physical systems. We explore the diverse applications of DT throughout its lifecycle and highlight the significance of DT in structural monitoring, infrastructure operation and maintenance, and dataset expansion. We further investigate the advanced techniques and equipment associated with DT components, focusing on the importance of virtual parts, data acquisition, transmission, multi-source data fusion processing, and data security as well as dynamic updating of models for effective integration and utilization of DT in transportation infrastructure management. We identify key challenges faced by DT in transportation infrastructure management and propose future trends in the study. This comprehensive review serves as a valuable resource for researchers, practitioners, and decision-makers in understanding the potential of DT technology in transportation infrastructure management.