A hybridized intelligence model to improve the predictability level of strength index parameters of rocks

A novel multi-objective dung beetle optimizer for Multi-UAV cooperative path planning

Path planning for multiple unmanned aerial vehicles (UAVs) is crucial in collaborative operations and is commonly regarded as a complicated, multi-objective optimization problem. However, traditional approaches have difficulty balancing convergence and diversity, as well as effectively handling constraints. In this study, a directional evolutionary non-dominated sorting dung beetle optimizer with adaptive stochastic ranking (DENSDBO-ASR) is developed to address these issues in collaborative multi-UAV path planning. Two objectives are initially formulated: the first one represents the total cost of length and altitude, while the second represents the total cost of threat and time. Additionally, an improved multi-objective dung beetle optimizer is introduced, which integrates a directional evolutionary strategy including directional mutation and crossover, thereby accelerating convergence and enhancing global search capability. Furthermore, an adaptive stochastic ranking mechanism is proposed to successfully handle different constraints by dynamically adjusting the comparison probability. The effectiveness and superiority of DENSDBO-ASR are demonstrated by the constrained problem functions (CF) test, the Wilcoxon rank sum test, and the Friedman test. Finally, three sets of simulated tests are carried out, each including different numbers of UAVs. In the most challenging scenario, DENSDBO-ASR successfully identifies feasible paths with average values of the two objective functions as low as 637.26 and 0. The comparative results demonstrate that DENSDBO-ASR outperforms the other five algorithms in terms of convergence accuracy and population diversity, making it an exceptional optimization approach to path planning challenges.

Intelligent prediction of rockburst in tunnels based on back propagation neural network integrated beetle antennae search algorithm

Rockburst is one of the major engineering geological disasters of underground engineering. Accurate rockburst intensity level prediction is vital for disaster control during underground tunnel construction. In this work, a hybrid model integrating the back propagation neural network (BPNN) with beetle antennae search algorithm (BAS) has been developed for rockburst prediction. Before model building, 173 groups of rockburst dataset were collected. Six geological parameters are selected as predictors for rockburst, including the maximum tangential stress of the surrounding rock σ_θ, the uniaxial compressive strength of rock σ_c, the tensile strength of rock σ_t, the stress ratio σ_θ/σ_c, the rock brittleness ratio σ_c/σ_t, and the elastic energy index W_et. After preprocessed by outlier detection and synthetic minority oversampling technique (SMOTE), the new dataset was divided into training and test parts. BAS could optimize the weights and biases of BPNN from the training process. Then the established hybrid model was applied to the test samples with predicted accuracy of 94.3%, proving that the hybrid model has practical value in researching rockburst prediction.

On the Physical and Mechanical Responses of Egyptian Granodiorite after High-Temperature Treatments

In the design and stability of thermal engineering applications, a thorough understanding of the evolution of damage in the rock following high-temperature treatments is crucial. Hence, this study investigates the influence of high temperatures on Egyptian granodiorite rock properties, given its widespread use as ornamental stones and aggregate material for roadways. Temperature effects up to 800 °C on its physical and mechanical responses were examined in conjunction with microstructure alterations. The results show that the density of granodiorite decreases after heat exposure due to a gain in volume and a loss in mass, with volume expansion being the most important component. In addition, the uniaxial compressive strength increases up to 400 °C before reducing linearly as the temperature increases, while the elastic modulus and P-wave velocity show a reducing trend with the temperature. This study suggests that granodiorite has a thermal damage threshold of 400 °C, beyond which its microstructure and physical and mechanical characteristics deteriorate, and granodiorite becomes less brittle and more ductile. Hence, at the mutation range (between 400 and 600 °C), the physical and mechanical responses shift from a stable to an unstable state. As a result, the microstructure of the granodiorite samples was destroyed at 800 °C, resulting in a significant drop in compressive strength and dilemmas in measuring the P-wave and elastic modulus. Accordingly, the findings of this study can be used to aid in the safe handling of this rock in high-temperature conditions.

Analysis of Water and Mud Inrush in Tunnel Fault Fracture Zone—A Case Study of Yonglian Tunnel

Water and mud inrush disaster is easily induced during tunnel construction through water-rich fault fracture zones. In this paper, based on the field data, the process of water and mud inrush in tunnels is introduced in detail, and generation conditions and evolution mechanisms have been analyzed. Results show that the key factors of water and mud inrush include poor strata lithology, abundant groundwater and tunnel excavation disturbance. These key factors provide material conditions, source power and start-up conditions for water inrush, respectively. The evolution process of water and mud inrush can be divided into three stages: generation, development and occurrence. During generation stage, a seepage channel expands continuously, with water flow increasing gradually until a large range of loose zone is formed. During the development stage, a large amount of groundwater and a large range of softened muddy rock mass accumulates around the tunnel. During the occurrence stage, the “protective layer” and initial supports suddenly lose stability and are destroyed. These research results could provide some references for the effective prediction and forewarning of similar engineering disasters.

A Modified Niching Crow Search Approach to Well Placement Optimization

Well placement optimization is considered a non-convex and highly multimodal optimization problem. In this article, a modified crow search algorithm is proposed to tackle the well placement optimization problem. This article proposes modifications based on local search and niching techniques in the crow search algorithm (CSA). At first, the suggested approach is verified by experimenting with the benchmark functions. For test functions, the results of the proposed approach demonstrated a higher convergence rate and a better solution. Again, the performance of the proposed technique is evaluated with well placement optimization problem and compared with particle swarm optimization (PSO), the Gravitational Search Algorithm (GSA), and the Crow search algorithm (CSA). The outcomes of the study revealed that the niching crow search algorithm is the most efficient and effective compared to the other techniques.