Liquefaction is a common concern for geotechnical engineers in moderate-to-high seismic areas. Loose, non-plastic and saturated soils are most prone to liquefaction. Traditional approaches to decrease liquefaction are still widely used, however there are still major difficulties including restrictions on treatment area size, potential damage to sensitive structures, and environmental impact. Modern methods for liquefaction reduction include passive site remediation, microbial geotechnology and induced partial saturation. Air is far more compressible than water, hence unsaturation or partial saturation can help a soil deposit resist liquefaction. Even small volumes of gas bubbles in saturated soils can increase liquefaction resistance, especially around structures. Recently, bio-denitrification has been used to dissimulate nitrate to nitrogen gas as an alternative desaturation method. In this review article, Induced Partial Saturation (IPS), a modern liquefaction mitigation approach, and its methods: a) Microbially induced partial saturation (MIPS) or biogas and b) Air injection has been discussed in detail. This article examines how compositional and environmental elements affect soil gas bubble retention and treatment system efficiency. Overburden stress, soil density and fines concentration affect gas bubble retention and treatment efficiency. Gas loss from the soil surface, possibly from capillary invasion and crack opening, reduces treatment efficiency.

Most natural disasters result from geodynamic events such as landslides and slope collapse. These failures cause catastrophes that directly impact the environment and cause financial and human losses. Visual inspection is the primary method for detecting failures in geotechnical structures, but on-site visits can be risky due to unstable soil. In addition, the body design and hostile and remote installation conditions make monitoring these structures inviable. When a fast and secure evaluation is required, analysis by computational methods becomes feasible. In this study, a convolutional neural network (CNN) approach to computer vision is applied to identify defects in the surface of geotechnical structures aided by unmanned aerial vehicle (UAV) and mobile devices, aiming to reduce the reliance on human-led on-site inspections. However, studies in computer vision algorithms still need to be explored in this field due to particularities of geotechnical engineering, such as limited public datasets and redundant images. Thus, this study obtained images of surface failure indicators from slopes near a Brazilian national road, assisted by UAV and mobile devices. We then proposed a custom CNN and low complexity model architecture to build a binary classifier image-aided to detect faults in geotechnical surfaces. The model achieved a satisfactory average accuracy rate of 94.26%. An AUC metric score of 0.99 from the receiver operator characteristic (ROC) curve and matrix confusion with a testing dataset show satisfactory results. The results suggest that the capability of the model to distinguish between the classes 'damage' and 'intact' is excellent. It enables the identification of failure indicators. Early failure indicator detection on the surface of slopes can facilitate proper maintenance and alarms and prevent disasters, as the integrity of the soil directly affects the structures built around and above it.