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Diversion is a crucial technique for effectively improving shale reservoir production by creating more complex fracture networks. Evaluating diversion effectiveness is necessary to optimize the parameters in hydraulic fracturing. Water hammer diagnostics, an emerging fracturing diagnosis technique, evaluate diversion effectiveness by analyzing water hammer signals. The water hammer attenuation, as indicated by the oscillation time, correlates with the complexity of fracture networks. However, it remains unclear whether the oscillation time is associated with diversion effectiveness. This paper elucidates the relationship between the water hammer oscillation time and diversion effectiveness by taking the probability of diversion and the treating pressure response as the evaluation criteria. Initially, a high-frequency pressure sensor was installed at the wellhead to sample the water hammer signals. Next, the oscillation times were determined using the feature extraction method. Simultaneously, the probability of diversion and the treating pressure response were calculated using the cepstrum error function and treating pressure curve, respectively. Then, the relationship between the oscillation time and diversion effectiveness was analyzed. Finally, a rapid judgment method for evaluating diversion effectiveness based on the water hammer oscillation time was proposed. The results indicated a negative correlation between the probability of diversion and the oscillation time, with higher probabilities resulting in lower oscillation times. The oscillation times exhibited a negative correlation with the treating pressure response, including the treating pressure increases and diversion pressure spikes, wherein a greater pressure differential led to lower oscillation times. Drawing from the statistics of a shale gas horizontal well in Sichuan, a better diversion effectiveness is associated with fewer oscillations, demonstrating a negative correlation between the diversion effectiveness and the oscillation time in water hammer signatures. Finally, a rapid judgment method for evaluating diversion effectiveness was proposed, utilizing the 95% confidence interval of the mean oscillation time. This paper offers useful insights into evaluating diversion performance in field cases. [ABSTRACT FROM AUTHOR]

With the development of data collection and storage capabilities in recent decades, abundant data have been accumulated in geotechnical engineering fields, providing opportunities for the usage of machine learning approaches. Thus, a rising number of scholars are adopting machine learning techniques to settle geotechnical issues. In this paper, the application of three popular machine learning algorithms, support vector machine (SVM), artificial neural network (ANN), and decision tree (DT), as well as other representative algorithms in geotechnical engineering, is reviewed. Meanwhile, the applicability of diverse machine learning algorithms in settling specific geotechnical engineering issues is compared. The main findings are as follows: ANN, SVM, and DT have been widely adopted to solve a variety of geotechnical engineering issues, such as the classification of soil and rock types, predicting the properties of geotechnical materials, etc. Based on the collected relevant research, the performance of random forest (RF) in sorting soil types and assessing landslide susceptibility is satisfying; SVM has high precision in classifying rock types and forecasting rock deformation; and backpropagation ANNs and Hopfield ANNs are recommended to forecast rock compressive strength and soil settlement, respectively. [ABSTRACT FROM AUTHOR]

• A method for preparing cement mortar with diverse numbers of internal cracks was proposed. • Permeability and porosity for the samples under diverse stress states were measured. • The stress-dependent permeability and porosity for the prepared samples was analysed. In this paper, gas permeability and porosity for the prepared cement mortar samples with diverse numbers of internal cracks under diverse pore pressure during confining pressure loading and unloading was measured. According to the outcomes of experiments, the following findings were obtained: Klinkenberg correction is appliable to the modification on gas permeability of cement mortar samples containing different number of internal cracks to obtain the intrinsic permeability, with the quadratic equation (Coefficient of Determination R2 is above than 0.92) having a better performance than that of Klinkenberg equation; Owing to the increase in the equivalent pore radius of seepage passages, Klinkenberg Effect of the cement mortar samples reduces when the number of internal cracks rises. When the number of internal cracks rises from 0 to 12, the slippage factor decreases over 90 %; When the number of internal cracks is high, the same changing extent in the number of internal cracks can lead to higher influence on the intrinsic permeability than that when the number of internal cracks is low. The variation magnitude of intrinsic permeability when the number of internal cracks increases from 6 to 12 is around 3–10 times higher than that when the number goes up from 0 to 6; When confining pressure increases, the sensitivity of intrinsic permeability to the changes in confining pressure reduces. The variation magnitude of intrinsic permeability when confining pressure is loaded from 0 MPa to 25 MPa is 5–10 times higher than that when confining pressure is loaded from 25 MPa to 45 MPa; Owing to the permanent deformation of pores and cracks, under the same confining pressure, the intrinsic permeability and porosity during the unloading stage are less than those during the loading stage, respectively; The sub-cubic law (R2 is above than0.90) is able to describe gas seepage in cement mortar specimens with different number of internal cracks more precisely compared to the cubic law. [ABSTRACT FROM AUTHOR]

The peak shear strength of clayey soil-geomembrane interfaces is a vital parameter for the design of relevant engineering infrastructure. However, due to the large number of influence factors and the complex action mechanism, accurate prediction of the peak shear strength for clayey soil-geomembrane interfaces is always a challenge. In this paper, a machine learning model was established by combining Mind Evolutionary Algorithm (MEA) and the ensemble algorithm of Adaptive Boosting Algorithm (ADA)-Back Propagation Artificial Neural Network (BPANN) to predict the peak shear strength of clayey soil-geomembrane interfaces based on the results of 623 laboratory interface direct shear experiments. By comparing with the conventional machine learning algorithms, including Particle Swarm Optimisation Algorithm (PSO) and Genetic Algorithm (GA) tuned ADA-BPANN, MEA tuned Support Vector Machine (SVM) and Random Forest (RF), the superior performance of MEA tuned ADA-BPANN has been validated, with higher predicting precision, shorter training time, and the avoidance of local optimum and overfitting. By adopting the proposed novel model, sensitivity analysis was carried out, which indicates that normal pressure has the largest influence on the peak shear strength, followed by geomembrane roughness. Furthermore, an analytical equation was proposed to assess the peak shear strength that allows the usage of machine learning skills for the practitioners with limited machine learning knowledge. The present research highlights the potential of the MEA tuned ADA-BPANN model as a useful tool to assist in preciously estimating the peak shear strength of clayey soil-geomembrane interfaces, which can provide benefits for the design of relevant engineering applications. • The ensemble artificial intelligence algorithms in predicting the peak shear strength of interfaces is compared. • An analytical equation for estimating the peak shear strength is proposed. • Physical experiments are conducted to validate the effectiveness of the proposed analytical equation. [ABSTRACT FROM AUTHOR]

• The corrosion initiation time and reinforcement corrosion depth of CFRP composite piles are predicted. • The degradation model of bending stiffness of CFRP composite piles is established. • The flexural stiffness reduction factor of CFRP composite piles is obtained by conducting the model pile bending tests. • The horizontal bearing characteristics of CFRP composite piles in marine environments is analyzed. In this paper, the durability life and horizontal bearing characteristics of carbon fiber reinforced polymers (CFRP) composite piles in marine environments are studied. The durability life of CFRP composite piles is divided into the initial corrosion stage of reinforcement and the crack propagation stage of concrete protection layer. The chloride diffusion model and Monte Carlo simulation method are used to predict the initial corrosion time of CFRP composite piles. Based on the thick-walled cylinder theory, the critical corrosion depth of the reinforced steel corresponding to the cracking of concrete protective layer is deduced. The durability life prediction model of crack propagation stage is established. The flexural stiffness reduction factor of CFRP composite piles with different replacement ratios is obtained by conducting the model pile bending tests. The attenuation law of bending stiffness and horizontal bearing characteristics of CFRP composite piles in marine environments are analyzed. The results show that the higher the replacement ratio of CFRP bar, the slower the reduction rate of flexural stiffness of CFRP composite pile is. Under the same buried depth, the pile bending moment and shear force of CFRP composite piles decrease with the increase of CFRP reinforcement replacement ratio. The horizontal displacement of CFRP composite piles increases with the increase of CFRP reinforcement replacement ratio. [ABSTRACT FROM AUTHOR]