The rapid progress of science and technology has created favorable conditions for the development of autonomous driverless vehicles. However, the complex conditions in the cities greatly limit the application and promotion of autonomous driving technology. To solve the problem, the underground mine is recommended to apply as a pilot for the promotion of autonomous driverless vehicles. The autonomous driverless vehicles are all connected to the internet to form a vehicle-to-vehicle (V2V) system. Because of the multi-levels and multi-stopes in underground mines, the key point is to obtain the precise locations of the vehicles in real-time. Therefore, the microseismic monitoring, image recognition technology, artificial intelligence training, and smart sensors are comprehensively utilized, based on the internet of things and cloud computing, to locate the autonomous driverless vehicles. Virtual sources localization and pencil lead break tests are conducted to simulate and eliminate the effectiveness and accuracy of the velocity-free localization method. Virtual sources localization demonstrate that the velocity-free localization method is accurate and reliable. Results of pencil lead break tests show that the average locating errors of X coordinates, Y coordinates, and absolute distance are 0.4318 cm, 0.1136 cm, and 0.5188 cm, respectively. The application and promotion of the autonomous driverless vehicles in underground mines can not only solve the problems of deep mining and reduce the frequent disasters caused by the harsh conditions but also can protect the life and property of the workers, as well as provide technical support for the safe and efficient recycling of deep resources.

Stability evaluation of geotechnical engineering slopes is of great significance for the risk control and safe operation of many engineering. Machine learning methods can effectively establish the potential relationship between geological features and slope behavior under complex environments, to accurately evaluate the stability of slope rock and soil. This work investigated the performance of eight commonly used machine learning models to predict slope safety factors. First, the prediction system of slope safety factors based on machine learning was established by combining historical data of slopes for parameter optimization and cross-validation. Then, four accuracy evaluation indexes, MSE, RMSE, MAE, and Pearson correlation, were objectively weighted, and objective weighting-TOPSIS models were constructed to comprehensively quantify the performance of each model. Finally, the best machine learning model was used in the slope stability analysis of the Sino–Russian natural gas control section. The research results show that there are obvious differences in the prediction accuracy of the slope safety factor among different models. The ANN model has the highest evaluation accuracy, and the ensemble learning method performs well in the data set. The machine learning model can better predict the safety factor of the slope under different working conditions. The discrepancies with the numerical simulation results are related to the limitations of data sets and the differences in analysis methods. The analysis method of this study not only provides a new research idea and solution for the construction and evaluation of the model predicting slope safety factors, but also applies to other geotechnical engineering instability problems.

Microseismic monitoring system is one of the effective means to monitor ground stress in deep mines. The accuracy and speed of microseismic signal identification directly affect the stability analysis in rock engineering. At present, manual identification, which heavily relies on manual experience, is widely used to classify microseismic events and blasts in the mines. To realize intelligent and accurate identification of microseismic events and blasts, a microseismic signal identification system based on machine learning was established in this work. The discrimination of microseismic events and blasts was established based on the machine learning framework. The microseismic monitoring data was used to optimize the parameters and validate the classification methods. Subsequently, ten machine learning algorithms were used as the preliminary algorithms of the learning layer, including the Decision Tree, Random Forest, Logistic Regression, SVM, KNN, GBDT, Naive Bayes, Bagging, AdaBoost, and MLP. Then, training set and test set, accounting for 50% of each data set, were prospectively examined, and the ACC, PPV, SEN, NPV, SPE, FAR and ROC curves were used as evaluation indexes. Finally, the performances of these machine learning algorithms in microseismic signal identification were evaluated with cross-validation methods. The results showed that the Logistic Regression classifier had the best performance in parameter identification, and the accuracy of cross-validation can reach more than 0.95. Random Forest, Decision Tree, and Naive Bayes also performed well in this data set. There were some differences in the accuracy of different classifiers in the training set, test set, and all data sets. To improve the accuracy of signal identification, the database of microseismic events and blasts should be expanded, to avoid the inaccurate data distribution caused by the small training set. Artificial intelligence identification methods, including Random Forest, Logistic Regression, Decision Tree, Naive Bayes, and AdaBoost algorithms, were applied to signal identification of the microseismic monitoring system in mines, and the identification results were consistent with the actual situation. In this way, the confusion caused by manual classification between microseismic events and blasts based on the characteristics of waveform signals is solved, and the required source parameters are easily obtained, which can ensure the accuracy and timeliness of microseismic events and blasts identification.

Unconfined compressive strength (UCS) is a critical property for assessing the engineering performances of sustainable materials, such as cement–fly ash mortar (CFAM), in the design of construction engineering projects. The experimental determination of UCS is time-consuming and expensive. Therefore, the present study aims to model the UCS of CFAM with boosting machine learning methods. First, an extensive database consisting of 395 experimental data points derived from the literature was developed. Then, three typical boosting machine learning models were employed to model the UCS based on the database, including gradient boosting regressor (GBR), light gradient boosting machine (LGBM), and Ada-Boost regressor (ABR). Additionally, the importance of different input parameters was quantitatively analyzed using the SHapley Additive exPlanations (SHAP) approach. Finally, the best boosting machine learning model’s prediction accuracy was compared to ten other commonly used machine learning models. The results indicate that the GBR model outperformed the LGBM and ABR models in predicting the UCS of the CFAM. The GBR model demonstrated significant accuracy, with no significant difference between the measured and predicted UCS values. The SHAP interpretations revealed that the curing time (T) was the most critical feature influencing the UCS values. At the same time, the chemical composition of the fly ash, particularly Al₂O₃, was more influential than the fly-ash dosage (FAD) or water-to-binder ratio (W/B) in determining the UCS values. Overall, this study demonstrates that SHAP boosting machine learning technology can be a useful tool for modeling and predicting UCS values of CFAM with good accuracy. It could also be helpful for CFAM design by saving time and costs on experimental tests.