The observation data of dam displacement can reflect the dam’s actual service behavior intuitively. Therefore, the establishment of a precise data-driven model to realize accurate and reliable safety ...monitoring of dam deformation is necessary. This study proposes a novel probabilistic prediction approach for concrete dam displacement based on optimized relevance vector machine (ORVM). A practical optimization framework for parameters estimation using the parallel Jaya algorithm (PJA) is developed, and various simple kernel/multi-kernel functions of relevance vector machine (RVM) are tested to obtain the optimal selection. The proposed model is tested on radial displacement measurements of a concrete arch dam to mine the effect of hydrostatic, seasonal and irreversible time components on dam deformation. Four algorithms, including support vector regression (SVR), radial basis function neural network (RBF-NN), extreme learning machine (ELM) and the HST-based multiple linear regression (HST-MLR), are used for comparison with the ORVM model. The simulation results demonstrate that the proposed multi-kernel ORVM model has the best performance for predicting the displacement out of range of the used measurements dataset. Meanwhile, the ORVM model has the advantages of probabilistic output and can provide reasonable confidence interval (CI) for dam safety monitoring. This study lays the foundation for the application of RVM in the field of dam health monitoring.
Engineered Cementitious Composite (ECC) is a family of high performance fiber reinforced cementitious composites featuring strain-hardening behavior and high tensile ductility (with tensile strain ...capacity of 3–5%). ECC achieves high ductility by forming multiple microcracks with crack width less than 60 μm under tension. The tight crack width of ECC naturally lends itself to low permeability even in the cracked stage. Such properties are of particular interest to hydraulic structure applications. In addition to the tight crack width, self-healing of microcracks further lowers the permeability of cracked ECC, enhancing the durability and safety of hydraulic structures. In this paper, the permeability of ECC composites under the influence of self-healing was experimentally studied. Single crack permeability tests were also conducted to directly correlate the permeability and self-healing behavior of a single crack with a given initial crack width. Additionally, an analytical model capable of predicting the permeability of ECC composites that undergo self-healing process is proposed and verified with experimental data. The research findings in the present paper can be used to accurately estimate the permeability of ECC and are expected to provide support for future design and application of ECC for hydraulic structures.
Engineered Cementitious Composites (ECC) forms multiple micro-cracks under tension when loaded to beyond the elastic stage. Unlike normal concrete, such tight cracks help to maintain low water ...permeability even in the cracked stage. Therefore ECC shows great potential for application in hydraulic structures, such as dams and levees for which water seepage control is critical for their performance. In this paper, the permeability of ECC under constant tensile load was experimentally studied using a specially designed displacement-control loading device, providing permeability data for ECC under realistic loading conditions. In addition, an analytical model capable of predicting permeability property of ECC composite based on tensile strain and crack patterns has been proposed and experimentally verified on two different ECC mixtures. The findings of this research are expected to support future design and application of ECC for hydraulic structures.
Displacement is the most intuitive reflection of the comprehensive behavior of concrete dams, especially the time effect displacement, which is a key index for the evaluation of the structural ...behavior and health status of a dam in long-term service. The main purpose of this paper is to establish a state space model for separating causal components from the measured dam displacement. This approach is conducted by initially proposing two equations, which are the state and observation equations, and model parameters are then optimized by the Kalman filter algorithm. The state equation is derived according to the creep deformation of dam concrete and foundation rock and is used to preliminarily predict the dam time effect displacement. Considering the generally recognized three components of dam displacement, the hydraulic-seasonal-time (HST) model is used to establish the observation equation, which is used to update the time effect displacement. The efficiency and rationality of the established state space model is verified by an engineering example. The results show that the hydraulic component separated by the state space model only contains the instantaneous elastic hydraulic deformation, while the hysteretic elastic hydraulic deformation is divided into the time effect component. The inverted elastic modulus of dam body concrete is an instantaneous value for the state space model but a comprehensive reflection of the instantaneous and hysteretic elastic deformation ability for the HST model, where the hysteretic elastic deformation is a part of the hydraulic component. For the Xiaowan arch dam, the inverted values are 42.9 and 36.7 GPa for the state space model and HST model, respectively. The proposed state space model is useful to improve the interpretation ability of the separated displacement components of concrete dams.
Dams are the main water retaining structures in the hydraulic engineering field. Safe operations of dams are important foundations to ensure the hydraulic functionalities of these engineering ...structures. Deformation, as the most intuitive feature of the dams' operation behaviors, can comprehensively reflect the dam structural states. In this case, the analysis of the dam prototype deformation data and the establishment of a real-time prediction model become frontier research contents in the field of dam safety monitoring. Considering the multi-nonlinear relationships between dam deformation and relative influential factors as well as the time lag effect of these influential factors, this article adopts long-short-term memory (LSTM) network algorithm in deep learning to deal with the long-term dependence existing in dam deformation and explore the deformation law. The method proposed in this work can effectively avoid the gradient disappearance and gradient explosion problems by using the recurrent neural network (RNN). In addition, this work adopts the Attention mechanism to screen the information that has significant influence on deformation, combining the Adam optimization algorithm that has high calculation efficiency and low memory requirement to improves the learning accuracy and speed of the LSTM. The model overfitting is avoided by applying the Dropout mechanism. The effectiveness of this proposed model in studing the long time series deformation prediction of concrete dams is confirmed by case studies, whose MSE (mean square error) and other 4 error indexes can be reduced.
This article investigates an inverse problem for variable physical and mechanical comprehensive parameters. A back analysis method based on an improved BFGS (Broyden-Fletcher-Goldfarb ...Shanno-Newtonian iteration) and AC (adjustment coefficient) is proposed to estimate the physical and mechanical comprehensive parameters of complex concrete arch dam structures. These quasi-viscoelastic variable physical and mechanical comprehensive parameters of concrete arch dam structures are established based on in situ monitoring data and test results. By studying the constitutive model of concrete arch dam structures and the corresponding characterization method of the gradual changes of those parameters, a method utilizing adjustment coefficient is established to determine the initial values of the physical and mechanical comprehensive parameters and an adjoin method combined with BFGS Newtonian iteration method is proposed to inverse the variable physical and mechanical comprehensive parameters of concrete arch dam structures and analyze the variation regulation of these parameters. The proposed inversion analysis method was successfully implemented for the second-highest concrete double curvature arch dam. The results indicate that the proposed method has high precision and strong generalizability.
The Foziling multi-arch dam, one of the few multi-arch dams in the world, was built on the bedrock with complicated geological conditions. It has undergone several reinforcements since it was put ...into service in the 1950s. In this study, the dam safety is evaluated by analyzing the measured displacements and simulating stresses in the concrete. Firstly, the multiple linear stepwise regression (MLSR) is used to train and test the relationships between the loads and displacement based on the hydrostatic-temperature-time (HTT) model. Subsequently, the contributions of water level, temperature, and time to displacements are determined, and the influence characteristics of water level and temperature on displacements are interpreted. Finally, the dam stress state is evaluated by establishing a dam finite element model and simulating the stress distribution in various operating conditions. The results indicate that (1) the dam is currently in an elastic state after the last reinforcement; (2) temperature contributes the most to the displacement, and the drastic fluctuation of temperature is the disadvantage factor for multi-arch dam safety; (3) the stresses generally can meet the requirements of code; and (4) the ideas and methods of the study can provide references for the safety evaluation of other concrete dams.
We present a novel deformation prediction model for super-high arch dams based on the prototype monitoring displacement field. The noise reduction processing of the monitoring data is conducted by a ...wavelet technique. The performance-improved random forest intelligent regression approach is then established for constructing the arch dam deformation statistical models, whose hyper-parameters are intelligently optimized in terms of the improved salp swarm algorithm. In total, three enhancement strategies are developed into the standard salp swarm algorithm to improve the global searching ability and the phenomenon of convergence precocious, including the elite opposition-based learning strategy, the difference strategy, and the Gaussian mutation strategy. A prediction example for super-high arch dams is presented to confirm the feasibility and applicability of the prediction model based on five evaluation criteria. The prediction results show that the proposed model is superior to other standard models, and exhibits high-prediction accuracy and excellent generalization performance. The stability of the proposed prediction model is investigated by artificially introducing noise strategies, which demonstrates the high-robust prediction features and provides a promising tool for predicting carbon emissions, epidemics, and so forth.
We present a novel non-local integral-type damage formulation for hydraulic fracture of poro-viscoelastic media under the framework of irreversible thermodynamics. The poro-viscoelastic material is ...modeled by a generalized Maxwell model, whose shear modulus is described in terms of Prony series. A bilinear damage law is assumed, which is driven by three equivalent strain invariants. Darcy’s law is employed to describe the fluid flow in the entire domain including the fracture process zone, where the permeability is assumed to be nonlinear and anisotropic. A monolithic two-field (
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) mixed finite element method is employed to discretize the coupled hydromechanical system. A Newton–Raphson method is utilized to solve the nonlinear system, and a backward Euler scheme is applied to evolve the system in time. Several numerical examples are presented to investigate the time-dependent deformation response of saturated porous media. In particular, we study the effects of relaxation time and the ratios of anisotropic initial permeability on the strongly coupled processes of the solid deformation, fluid transport and damage evolution of geomaterials. In addition, the different modes of energy dissipation mechanisms including damage and solid viscous response are presented and discussed.
Displacement data modelling is of great importance for the safety control of concrete dams. The commonly used artificial intelligence method modelled the displacement data at each monitoring point ...individually, i.e., the data correlations between the monitoring points are overlooked, which leads to the over-fitting problem and the limitations in the generalization of model. A novel model combines Gaussian mixture model and Iterative self-organizing data analysing (ISODATA-GMM) clustering and the random coefficient method is proposed in this article, which takes the temporal-spatial correlation among the monitoring points into account. By taking the temporal-spatial correlation among the monitoring points into account and building models for all the points simultaneously, the random coefficient model improves the generalization ability of the model through reducing the number of free model variables. Since the random coefficient model supposed the data follows normal distributions, we use an ISODATA-GMM clustering algorithm to classify the measuring points into several groups according to its temporal and spatial characteristics, so that each group follows one distribution. Our model has the advantage of having a stronger generalization ability.
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