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Seepage analysis is the central issue in solving the unconfined seepage problems from complex earth‐rock dams. The precision and efficiency of seepage simulation are the major technical barriers to exploring the heterogeneities of hydrological state variables in dams. This paper addresses the heterogeneity of the saturated‐unsaturated zones of dams and develops the smoothed finite element method (S‐FEM) capable of solving the seepage problem in domains with arbitrary geometry and continuously varied permeability. The gradient smoothing technique, in which the area integrals are transformed into the line integrals around edges of smoothing cells, is used to obtain the element matrices, which is used to simplify the solution of seepage problem. To be specific, we employ Van Genuchten method to modify the hydraulic conductivity of unsaturated zones medium and Signorini condition to convert the outflow boundary into the water head boundaries. The precision of the proposed method together with the importance of considering the unsaturated zones are first deliberated by the verification tests. Then, to quantify the impacts on the hydrological state variables, we focus on the heterogeneity of the entire zones through exploratory tests. The results show that the presence of leakage anomalies and auxiliary dam structures has a significant effect on the redistribution of hydrological state variables, which convinces that the findings may lay the foundation for the development of multi‐parameter seepage inversion and the derivation of coupled hydro‐geophysical inversion using the most appropriate starting model. [ABSTRACT FROM AUTHOR]

The research investigates the significance of identifying structure discontinuities, such as cracks, in concrete dams to ensure dam safety and stability. A novel automatic image classification method is developed, employing Deep Dense Transfer Learning (DDTL) with pre-trained models, including EfficientNetB1, ResNet50, and a hybrid model to identify the detection of cracks in sub-surfaces at pillow dams in Sichuan province, China. The developed model was trained, validated, and tested, with the Hybrid model demonstrating superior performance. The results showed that the DDTL models had high classification accuracies, surpassing Convolutional identification techniques for sub-surface cracks. Consequently, this study suggests that automatic image classification techniques can effectively identify and localize structural defects in concrete dams. This is an innovative approach to predicting normal borehole images and crack recognition using CCTV borehole images. [ABSTRACT FROM AUTHOR]

Estimating the quantity of seepage through the foundation and body of a dam using proper health and safety monitoring is critical to the effective management of disaster risk in a reservoir downstream of the dam. In this study, a deep learning model was constructed to predict the extent of seepage through Pakistan's Tarbela dam, the world's second largest clay and rock dam. The dataset included hydro-climatological, geophysical, and engineering characteristics for peak-to-peak water inflows into the dam from 2014 to 2020. In addition, the data are time series, recurring neural networks (RNN), and long short-term memory (LSTM) as time series algorithms. The RNN–LSTM model has an average mean square error of 0.12, and a model performance of 0.9451, with minimal losses and high accuracy, resulting in the best-predicted dam seepage result. Damage was projected using a deep learning system that addressed the limitations of the model, the difficulties of calculating human activity schedules, and the need for a different set of input data to make good predictions. [ABSTRACT FROM AUTHOR]

Efficient and accurate simulation of ground penetrating radar (GPR) in the open region helps immensely in both grasping the features of echoes and facilitating the interpretation of real GPR data. Due to the limitation of the computer model, however, the strong artificial boundary reflections, especially the low-frequency propagating waves encountered at the late stage of simulation greatly affect the simulation accuracy of GPR. This paper presents an innovative optimized unsplit-field convolutional perfectly matched layer (UCPML) based on rotated staggered grid (RSG) scheme to truncate the finite-difference time-domain (FDTD) lattices. Rather than obey the sharp variation based on an ${m}$ th-order polynomial, the optimized approach employs a novel optimized term and an adjustment factor to seek a gentle variation on optimal constitutive coefficients. This guarantees that the determination of optimal constitutive coefficients can be less influenced by the order of polynomial and especially, to improve the absorptive performance on low-frequency propagating waves. The calculating efficiency and accuracy of the RSG-FDTD scheme, as well as the absorbing performance of the optimized UCPML, are verified by two numerical examples. In particular, the analysis of the amplitude-frequency features of low-frequency clutters at steady state of the electromagnetic (EM) field and the corresponding global reflection error in the time–frequency domain is also presented. [ABSTRACT FROM AUTHOR]

An integrated geophysical investigation was performed at S dam located at Dadu basin in China to assess the condition of the dam curtain. The key methodology of the integrated technique used was flow-field fitting method, which allowed identification of the hydraulic connections between the dam foundation and surface water sources (upstream and downstream), and location of the anomalous leakage outlets in the dam foundation. Limitations of the flow-field fitting method were complemented with resistivity logging to identify the internal erosion which had not yet developed into seepage pathways. The results of the flow-field fitting method and resistivity logging were consistent when compared with data provided by seismic tomography, borehole television, water injection test, and rock quality designation. [ABSTRACT FROM AUTHOR]

The critical challenge facing the modeling accuracy of borehole-to-surface electrical potential (BSEP) is the singularity effect resulted from the field source and the truncation error due to improper boundary conditions. However, different boundary conditions can reduce the truncation error by enlarging the computational domain. This enlargement undoubtedly causes a significant increase in the calculation burden. To some extent, the mixed boundary condition-based finite element method (MBC-FEM) can mitigate this problem. However, it is inapplicable to the BSEP modeling with line source because these algorithms were initially derived from the point source. For improving the BSEP modeling efficiency and precisely represent the specific correlation between BSEP observation results and the spatial location of the abnormal underground body, this paper introduces a novel finite-infinite coupling method (FICM) based on the secondary potential of arbitrary line source for three-dimensional BSEP modeling. By employing the variational principle and the different interpolation shape functions of both the FEM and the infinite element method (IEM), we discretize the function of three-dimensional line field source in the elements. The proposed coupled algorithm is then tested on a uniform half-space model, a low-resistance spherical anomaly model and an international standard model. The results show the proposed FICM can eliminate the singularity effect to reduce the truncation error. More importantly, the calculation efficiency is significantly improved by essential virtue of the needlessness of enlarging the computational domain, as well as the one-off generation of coefficient matrices. In this sense, the proposed FICM represents a more appropriate candidate for BSEP forward and inversion when a large-scale calculation is involved, especially in terms of irregular underground abnormal bodies in the line source conditions. • Field-source singularity effect is eliminated by quadratic field and infinite element. • Modeling efficiency can be improved by one-off generating the coefficient matrices. • Increasing the calculation speed of BSEP modeling by more than 50%. [ABSTRACT FROM AUTHOR]

The chaos oscillation particle swarm optimization (COPSO) algorithm is prone to binge trapped in the local optima when dealing with certain complex models in ground-penetrating radar (GPR) data inversion, because it inherently suffers from premature convergence, high computational costs, and extremely slow convergence times, especially in the middle and later periods of iterative inversion. Considering that the bilateral connections between different particle positions can improve both the algorithmic searching efficiency and the convergence performance, we first develop a fast single-trace-based approach to construct an initial model for 2-D PSO inversion and then propose a TV-regularization-based improved PSO (TVIPSO) algorithm that employs total variation (TV) regularization as a constraint technique to adaptively update the positions of particles. B by adding the new velocity variations and optimal step size matrices, the search range of the random particles in the solution space can be significantly reduced, meaning blindness in the search process can be avoided. By introducing constraint-oriented regularization to allow the optimization search to move out of the inaccurate region, the premature convergence and blurring problems can be mitigated to further guarantee the inversion accuracy and efficiency. We report on three inversion experiments involving multilayered, fluctuated terrain models and a typical complicated inner-interface model to demonstrate the performance of the proposed algorithm. The results of the fluctuated terrain model show that compared with the COPSO algorithm, the fitness error (MAE) of the TVIPSO algorithm is reduced from 2.3715 to 1.0921, while for the complicated inner-interface model the fitness error (MARE) of the TVIPSO algorithm is reduced from 1.9539 to 1.5674. [ABSTRACT FROM AUTHOR]

One of the great challenges of unconfined seepage through a dam lies in the accurate determination of free surface that depends on the complexity of the seepage model, especially if the model is characterized with complex geometry and sharp variations in permeability distribution. This study presents a practical methodology that combines the adaptive moving-mesh algorithm and the Galerkin finite element method (FEM) to solve an unconfined seepage problem with high efficiency and precision. The methodology employs a set of improvement terms, such as remainder factor, step-size parameter and termination condition, all of which guarantee that the simulation and the refinement fitting can be implemented efficiently until the free surface converges within a given allowable error. In particular, a specialized discussion is presented for the significant relation between the location of the exit point and the corresponding grid fineness. To validate the practicability of the proposed method, a series of examples are performed. Comparing the result with those of other numerical approaches, we conclude that even though the unconfined seepage model may be complicated with arbitrary complex geometry and sharp variations in permeability distribution, the proposed algorithm provides a great improvement in efficiency and accuracy in free-surface searching. [ABSTRACT FROM AUTHOR]

The traditional artificial neural network (ANN) inversion of electrical resistivity imaging (ERI) based on gradient descent algorithm is known to be inept for its low computation efficiency and does not ensure global convergence. In order to solve above problems, a kernel principal component wavelet neural network (KPCWNN) trained by an improved shuffled frog leaping algorithm (ISFLA) method is proposed in this study. An additional kernel principal component (KPC) layer is applied to reduce the dimensionality of apparent resistivity data and increase the computational efficiency of wavelet neural network (WNN). Meanwhile, a novel ISFLA algorithm is adopted for improving the learning ability and inversion quality of WNN. In the proposed ISFLA, a hybrid LC mutation attractor is used to enhance the exploitation ability and a differential updating rule is used to enhance the exploration ability. Four groups of experiments are considered to demonstrate the feasibility of the proposed inversion method. The inversion results of the synthetic and field examples show that the introduced method is superior to other algorithms in terms of prediction accuracy and computational efficiency, which contribute to better inversion results. [ABSTRACT FROM AUTHOR]

Electrical resistivity imaging (ERI) inversion is a complicated non-linear inversion problem, which is high dimensional and non-convex. Using traditional neural networks to solve the ERI inversion problem has an over-fitting phenomenon and easily falls into local minima. Moreover, the artificial neural network (ANN) model is a black box; its relationships between the inputs and outputs are not easy to interpret. In order to solve these problems, a wavelet packet denoising (WPD) procedure and an improved adaptive neuro-fuzzy inference system (ANFIS) based on Cauchy oscillation shuffled frog leaping algorithm (COSFLA) are proposed in this paper. The wavelet packet denoising methodology is based on soft thresholding and Shannon entropy using a Db10 wavelet which is applied to remove the noise component from the measured apparent resistivity data. Meanwhile, COSFLA is introduced for updating the premise parameters of the ANFIS to improve the learning ability and prediction accuracy of the algorithm. The development of the proposed method and the total inversion process are also presented. The inversion of the synthetic and field examples are provided to demonstrate the feasibility and validity of the proposed method. Additionally, the introduced method is transparent and its if–then rules are easy to understand and interpret. [ABSTRACT FROM AUTHOR]