Understanding processes and conditions that lead to rockfalls during and after a wildfire in different geological contexts is crucial since this phenomenon is one of the major hazards in mountainous regions across Europe. Spain is one of the European countries with the highest rate of wildfires, and rockfalls cause high economic and social impact, with many fatalities every year. The increase of rockfalls during and after wildfires is connected with the merging of different factors, not only in the detached area but also in the propagation and potentially affected area. When wildfire occurred, many actions take place: changes in the mechanical conditions of the rocks, the loss of protective capacity from vegetation, the effect induced by firefighting activities and/or the impact by the high temperatures in the adopted protective measures. After the wildfire, there is an increase in frequency and intensity of rockfalls in the burned area, causing a major impact of rockfalls on not only road networks and built-up areas but also people living. Additionally, the removal of vegetation by wildfires causes an increase in the risk perception, related not only to detached blocks but also to the general appearance of the rock mass. In this review, the main factors that influence the occurrence of rockfalls after a wildfire are analyzed, and three actual case studies in Spain are presented to support the variety of conclusions obtained.

In the rock mechanics and rock engineering field, the strength parameter considered to characterize the rock is the uniaxial compressive strength (UCS). It is usually determined in the laboratory through a few statistically representative numbers of specimens, with a recommended minimum of five. The UCS can also be estimated from rock index properties, such as the effective porosity, density, and P-wave velocity. In the case of a porous rock such as travertine, the random distribution of voids inside the test specimen (not detectable in the density-porosity test, but in the compressive strength test) causes large variations on the UCS value, which were found in the range of 62 MPa for this rock. This fact complicates a sufficiently accurate determination of experimental results, also affecting the estimations based on regression analyses. Aiming to solve this problem, statistical analysis, and machine learning models (artificial neural network) was developed to generate a reliable predictive model, through which the best results for a multiple regression model between uniaxial compressive strength (UCS), P-wave velocity and porosity were obtained.

An accurate evaluation of the shear strength of discontinuities is frequently a key aspect for determining the safety of mining and civil engineering works and for solving instability issues at rock mass scale. This is usually done by using empirical shear strength criteria in which the basic friction angle (φ_b) is a relevant input parameter. Tilt testing is probably the most widespread method to obtain the φ_b due to its simplicity and low cost, but previous research has demonstrated that the results are strongly affected by several factors (e.g. surface finishing, cutting speed, specimen geometry, wear, time and rock type). In this connection, despite it is well known that water significantly reduces the mechanical properties of sedimentary rocks, very scarce research has focused on assessing the impact of the variations in water content on tilt test results. With the aim to fill this gap, saw-cut slabs of three limestone lithotypes were tilt tested in dry state, wet condition (fully water saturated, non-submerged samples) and under exposure to an environmental relative humidity (RH) of 90%. The results revealed that full water saturation caused moderate φ_b reductions in two lithotypes and a φ_b increase in one lithotype. This can be explained by their different microstructure and mineralogy, which makes that lubrication effect prevails over suction effect or vice versa. However, the exposure to a high RH environment did not cause significant φ_b variations. In addition, some important considerations related to tilt testing are provided and discussed, such as the intrinsic variability of the sliding angle (β) and the impact of multiple sliding on the same rock surfaces on β.