Liquefaction has been responsible for several earthquake-related hazards in the past. An earthquake may cause liquefaction in saturated granular soils, which might lead to massive consequences. The ability to accurately anticipate soil liquefaction potential is thus critical, particularly in the context of civil engineering project planning. Support vector machines (SVMs) and Bayesian optimization (BO), a well-known optimization method, were used in this work to accurately forecast soil liquefaction potential. Before the development of the BOSVM model, an evolutionary random forest (ERF) model was used for input selection. From among the nine candidate inputs, the ERF selected six, including water table, effective vertical stress, peak acceleration at the ground surface, measured CPT tip resistance, cyclic stress ratio (CSR), and mean grain size, as the most important ones to predict the soil liquefaction. After the BOSVM model was developed using the six selected inputs, the performance of this model was evaluated using renowned performance criteria, including accuracy (%), receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC). In addition, the performance of this model was compared with a standard SVM model and other machine learning models. The results of the BOSVM model showed that this model outperformed other models. The BOSVM model achieved an accuracy of 96.4% and 95.8% and an AUC of 0.93 and 0.98 for the training and testing phases, respectively. Our research suggests that BOSVM is a viable alternative to conventional soil liquefaction prediction methods. In addition, the findings of this research show that the BO method is successful in training the SVM model.

This research was conducted to forecast the uniaxial compressive strength (UCS) of rocks via the random forest, artificial neural network, Gaussian process regression, support vector machine, K-nearest neighbor, adaptive neuro-fuzzy inference system, simple regression, and multiple linear regression approaches. For this purpose, geo-mechanical and petrographic characteristics of sedimentary rocks in southern Iran were measured. The effect of petrography on geo-mechanical characteristics was assessed. The carbonate and sandstone samples were classified as mudstone to grainstone and calc-litharenite, respectively. Due to the shallow depth of the studied mines and the low amount of quartz minerals in the samples, the rock bursting phenomenon does not occur in these mines. To develop UCS predictor models, porosity, point load index, water absorption, P-wave velocity, and density were considered as inputs. Using variance accounted for, mean absolute percentage error, root-mean-square-error, determination coefficient (R²), and performance index (PI), the efficiency of the methods was evaluated. Analysis of model criteria using multiple linear regression allowed for the development of a user-friendly equation, which proved to have adequate accuracy. All intelligent methods (with R² > 90%) had excellent accuracy for estimating UCS. The percentage difference of the average of all six intelligent methods with the measured value was equal to +0.28%. By comparing the methods, the accuracy of the support vector machine with radial basis function in predicting UCS was (R² = 0.99 and PI = 1.92) and outperformed all the other methods investigated.

In order to study and predict the flowing behaviour of controlled low-strength material (CLSM) slurry filling with piping transportation, a three-dimensional computational fluid dynamics simulation is carried out based on the Herschel–Bulkley (H-B) rheological model. The simulation is validated by L-pipe flow experiments of CLSM on the flow time and flow pattern. The analysis results show that it is reliable to use the H-B model to simulate the flow of the CLSM slurry. On this basis, simulations are performed to predict the dynamic characteristics of CLSM slurry filling with piping transportation and analyse the velocity distribution and pressure variation in pipeline. The simulation results are consistent with the basic law of power flow, which proves the rationality and effectiveness of the method. The study on the filling characteristics of CLSM slurry pipeline lays a theoretical foundation for the realisation of low-cost, safe and efficient filling of abandoned pipelines.

Nowadays, China is not experienced in the research and practice of the abandonment approaches to oil and gas pipelines retirement. Using the experience of the North America for reference in abandonment of onshore pipelines, including retired pipeline safety environmental disposal scheme choice, waste disposal technology and retired environmental impact assessment of the key problems were discussed. The comparatively complete construction technology was put forward to dismantle pipelines. Besides, the construction technology of filling the controlled low strength material to the decommissioning oil pipelines was put up. Based on the case of Dong-Huang pipeline abandonment, the selection disposal scheme was optimized, key construction practices such as the cleaning of pipeline residues and low strength controlled material filling pipeline were expounded, potential environmental influence of different disposal scheme was analyzed, besides, some measures were represented to prevent and relieve the pollution corresponding to the construction technology. Research results have guiding significance for perfecting safe abandonment research of domestic retired oil and gas pipelines.