This research focuses on developing hybrid intelligence techniques to predict the bearing capacity of circular foundations using the most effective parameters. In this regard, a database involving 968 test circular foundations involving different rock properties, soil characteristics, and foundation radius has been prepared by laboratory tests for training and testing the new model presented in this study. For adequately considering various factors, the several parameters of depth (D) in m, density of soil (DS) in gr/cm³, internal angle of friction (IAF) in degree, cohesion of soil (CS) in kg/cm², and foundation radius (FR) in m were considered as the input of the model and bearing capacity in kg/cm² was considered as the target parameter. Three main strategies were addressed in this study. First, four well-known machine learning algorithms, Artificial Neural Network (ANN), Bagging Regressor (BR), Least Squares Support Vector Machine (LSSVM), and Gradient Boosting Regressor (GBR), were adopted to predict bearing capacity. Second, a novel and robust mathematical computation named dimensional analysis (DA) theorem was integrated with machine learning techniques to improve the accuracy and performance of the models by decreasing the number of inputs. Third, based on DA implementation, a rational mathematical formula using gene expression programming (GEP) was structured to anticipate bearing capacity. Furthermore, the sensitivity analysis process specified the impact of the five effective factors. The results of this study demonstrate the effectiveness of integrating DA with machine learning models to predict the BC of circular foundations. Among the developed models, the DA-LSSVM model showed superior performance, achieving an R² of 0.998 and 0.996 for training and testing, respectively, indicating high prediction accuracy. The results indicated that the IAF was the most sensitive factor with r = 0.709, while CS was the least sensitive with r = -0.087. A graphical user interface (GUI) app has been designed to apply the proposed models in this study conveniently. In the last step of this process, the GUI and the DA-based machine learning models were implemented by analyzing two examples. According to the findings, the GUI may be employed to make a reliable and speedy projection of the bearing capacity of circular foundations by considering a variety of input parameters.

This article aiming at the lack of research on the influence of rock clamp production on cutting blasting under high in-situ stress conditions and the lack of rock damage criteria for RHT constitution in numerical simulation. Combined with the critical rock damage criterion and the embedded function of RHT constitution, the criterion for determining the critical damage of rock in RHT constitutive was studied, and the mechanical parameters of Metamorphic sodium lava were substituted to obtain the critical damage threshold of rock in numerical simulation. The smooth particle hydrodynamics (SPH) method was used to numerically simulate and analyze the influence of different rock clamping coefficients on the rock damage range and the cavity area in the cutting blasting. The stress state applied by the numerical simulation was inversely deduced by the field test scanning results to simulate the rock clamping coefficient K_r at the corresponding depth. The relationship between the cavity area S_c and the free surface distance D_f is analyzed and established. The results show that the rock clip production has an inhibitory effect on the development and propagation of blast-induced cracks. The stress applied in the numerical simulation affects the range and development degree of cracks, and the cracks generated by the explosion are mainly circumferential cracks. The larger coefficient of rock clip production, the more obvious the inhibitory effect on cut blasting, the less the blast-induced cracks and the smaller the rock damage circle. The fitting results show that the curve fitting degree is about 0.94, which proves the accuracy of S_c-D_f curve, and provides important reference value for the design of one-time completion blasting of upward blind shaft.

Precise control of casting velocity and effective throwing kinetic energy conversion efficiency in blasting engineering are challenges. To provide a theoretical basis and reference for the implementation plan and fine construction of the cast blasting project, we study the problems of casting velocity and energy consumption ratio of broken rock under the impact load of explosions in this manuscript. The calculation methods of casting velocity and throwing energy of broken rock under two blasting modes of spherical charge and cylindrical charge are established by using the theory of dimensional analysis and rock breaking by blasting. A large number of model tests are carried out by using high-speed photography. The results indicate that the casting velocity of broken rock after explosive initiation has two evident stages: instantaneous acceleration to a certain value and subsequent fluctuation; the velocity presents an ordinary distribution law with the step height, and the fitting correlation of high-speed photography results is more than 91%. With the minimum burden increasing from 0.12 m to 0.2 m, the energy consumption decreases from 1306.88 J to 747.49 J and the proportion of energy consumption decreases from 14.77% to 8.45%.