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Motivated by the search for new strategies for fitting a material model, a new approach is explored in the present work. The use of numerical and complex algorithms based on machine learning techniques such as support vector machines for regression, bagged decision trees, and artificial neural networks is proposed for solving the parameter identification of constitutive laws for soft biological tissues. First, the mathematical tools were trained with analytical uniaxial data (circumferential and longitudinal directions) as inputs, and their corresponding material parameters of the Gasser, Ogden, and Holzapfel strain energy function as outputs. The train and test errors show great efficiency during the training process in finding correlations between inputs and outputs; besides, the correlation coefficients were very close to 1. Second, the tool was validated with unseen observations of analytical circumferential and longitudinal uniaxial data. The results show an excellent agreement between the prediction of the material parameters of the strain energy function and the analytical curves. Finally, data from real circumferential and longitudinal uniaxial tests on different cardiovascular tissues were fitted; thus, the material model of these tissues was predicted. We found that the method was able to consistently identify model parameters, and we believe that the use of these numerical tools could lead to an improvement in the characterization of soft biological tissues.

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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.

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.