Thermal Infrared Imagery Integrated with Multi-Field Information for Characterization of Pile-Reinforced Landslide Deformation

Abstract

Physical model testing can replicate the deformation process of landslide stabilizing piles and analyze the pile-landslide interaction with multiple field information, thoroughly demonstrating its deformation and failure mechanism. In this paper, an integrated monitoring system was introduced. The instrumentation used included soil pressure cells, thermal infrared (TIR) imagery, 3D laser scanner, and digital photography. In order to precisely perform field information analysis, an index was proposed to analyze thermal infrared temperature captured by infrared thermography; the qualitative relationship among stress state and deformation as well as thermal infrared temperature is analyzed. The results indicate that the integrated monitoring system is expected to be useful for characterizing the deformation process of a pile-reinforced landslide. Difference value of TIR temperature ( T I R m ) is a useful indicator for landslide detection, and its anomalies can be selected as a precursor to landslide deformation.

Method for rock fracture prediction and early warning: Insight from fusion of multi-physics field information

“…On the other hand, acoustic emission analysis can pinpoint the internal locations of coal and rock ruptures [ 3 , 41 , 42 ], yet its effectiveness is often compromised by environmental interference, reducing its adaptability for early warning in critical disaster scenarios. Consequently, the fusion of these two sources of information for early warning analysis holds the potential to significantly enhance accuracy and reliability, offering a more robust approach to disaster prevention and mitigation [ [43] , [44] , [45] , [46] ].…”

Emergency Survey and Stability Analysis of a Rainfall-Induced Soil-Rock Mixture Landslide at Chongqing City, China

“…Multi-field information is very important in understanding the deformation characteristics of a landslide. Over the last decade, some new technologies have been developed to monitor landslide deformation, such as GB-InSAR, TLS, IRT, digital photogrammetry (DP), and so on (Nicola et al, 2010;Del Ventisette et al, 2011;Bardi et al, 2014;Crosetto et al, 2016;Zin et al, 2016;Casagli et al, 2017;Li et al, 2020;Zhou et al, 2020), and have achieved remarkable achievements in monitoring, prediction, and early warning of the landslides (Xu et al, 2016;Piciullo et al, 2018;Ouyang et al, 2019). Those technologies are characterized by more efficient operation and higher accuracy of monitoring data, compared with traditional monitoring sensors: offering high-resolution image data acquisition, data diversity, device portability, and easy and fast data processing (Casagli et al, 2017).…”