Multi-zone parametric inverse analysis of super high arch dams using deep learning networks based on measured displacements

Parametric inverse analysis/identification provides significant information for structural damage detection and construction in dam engineering. The main challenge in inverse analysis is to enhance the computational accuracy and efficiency for complex structures, especially for super high arch dams with many zone parameters. This study developed a high-precision deep learning-based surrogate model for rapid inverse analysis of concrete arch dams. The relationship between mechanical parameters and multi-point displacement response is interpreted by convolutional neural networks (CNN)-based surrogate model. The proposed model is integrated with the Latin hypercube sampling and a meta-heuristic optimization algorithm for rapid inverse analysis strategy. The objective function is defined as the distance between the displacement predicted by the surrogate model and the measured displacement. The proposed approach is tested on an actual super high concrete arch dam. Results show that the proposed approach can achieve high accuracy and improve the computational efficiency by 95.83 % compared with the direct finite element method.     

Multi-kernel optimized relevance vector machine for probabilistic prediction of concrete dam displacement

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.

     

Dynamic parameter inverse analysis of concrete dams based on Jaya algorithm with Gaussian processes surrogate model

A dynamic parameter inverse analysis process for concrete dams based on Gaussian process regression and Jaya algorithm is presented. Gaussian process regression is used to establish a response surface representing the relationship between dynamic elastic modulus and modal parameters (natural frequency and mode shape). The Jaya algorithm is applied for dynamic parameter identification by minimizing the objective function. To verify the performance of the proposed method, we consider a concrete single buttress dam and a hyperbolic concrete arch dam as numerical examples. Numerical results show that Gaussian process regression can significantly improve the parameter identification efficiency without compromising on accuracy. Furthermore, the Jaya algorithm is compared with particle swarm optimization algorithm and genetic algorithm; the results show that the Jaya algorithm is promising in parameter recognition.     

The applied research of GTSTRUDL software in the stress analysis of arch dams

The software system GTSTRUDL has obtained favorable effect when combined with arch dam design of a hydropower station. The experience and techniques of the automatic data generation and the structural database management commands used by GTSTRUDL are introduced. As shown, with calculated practice, GTSTRUDL is an effective tool for FEM analysis of arch dams. The level and efficiency for FEM analysis of arch dams can be increased notably when adopting the unitized mechanics model and discretized scheme, as suggested in this paper. The engineering example has been provided and compared with the results of trial-load method.     

Predicting piezometric water level in dams via artificial neural networks

The safety control of dams is based on measurements of parameters of interest such as seepage flows, seepage water clarity, piezometric levels, water levels, pressures, deformations or movements, temperature variations, loading conditions, etc. Interpretation of these large sets of available data is very important for dam health monitoring and it is based on mathematical models. Modelling seepage through geological formations located near the dam site or dam bodies is a challenging task in dam engineering. The objective of this study is to develop a feedforward neural network (FNN) model to predict the piezometric water level in dams. An improved resilient propagation algorithm has been used to train the FNN. The measured data have been compared with the results of FNN models and multiple linear regression (MLR) models that have been widely used in analysis of the structural dam behaviour. The FNN and MLR models have been developed and tested using experimental data collected during 9 years. The results of this study show that FNN models can be a powerful and important tool which can be used to assess dams.     

An integrated method for evaluating and predicting long-term operation safety of concrete dams considering lag effect

Effective operation safety evaluation of concrete dams is critical for ensuring the longevity and quality service of a dam. This paper introduces a novel method for quantifying the safety status of concrete dams and predicting future long-term safety performance, considering lag effect of indices. First, lag effect of operation indices is quantified using the modified moving average-cosine similarity method, based on which a comprehensive safety evaluation index system is established. Second, analytic hierarchy process is used to determine the subjective weighting of each index. Considering data correlation, a new method named coefficient of discreteness and independence is proposed to calculate the objective weighting of each index using maximal information coefficient. The final actual weighting of each index is assumed to be a linear combination of the above subjective and objective weightings. Third, based on the long-term monitoring data of a concrete dam, the safety score of a concrete dam can be quantified using technique for order preference by similarity to an ideal solution. Finally, neural networks (NN) are used to predict future long-term safety performance as a faster and simpler way to obtain future safety score. The effectiveness of this proposed method is verified through a case study. The case study showed that structural safety, environmental safety, and total safety scores of a concrete dam can fluctuate periodically, but the overall performance trend is relatively stable, as expected in real-world cases. NN were found to be accurate in predicting future safety performance.

     

基于单元模态应变能和剩余模态力的结构损伤识别方法在某塔式桁架中的应用

为快速、准确定位工程结构损伤位置,有效提高工程结构安全性能和使用寿命,以某塔式桁架结构为研究对象,运用单元模态应变能法和剩余模态力法对其进行损伤识别.利用MSC Marc对该桁架完整结构和几种不同损伤程度下的损伤结构进行模态分析,通过MATLAB编程从模态分析结果中提取这些结构的模态参数,计算损伤结构单元模态应变能的变化率和损伤结构各节点自由度对应的剩余模态力,并进行结构损伤识别.结果表明单元模态应变能变化率和剩余模态力是有效和准确的结构损伤标志量.     

小型水库雨水情测报与工程安全监测标准化研究

小型水库数量多、分布广、坝型结构多,加上现有的相关技术规范针对性不强,从而使得小型水库雨水情和工程安全监测实施过程中各地理解不一。为提高小型水库雨水情测报与工程安全监测标准化水平,充分考虑水库库容、上游来水、坝型、坝高、溢流泄洪设施等水库特征,结合现有监测技术的发展,根据我国小型水库相关规章和规范性文件,结合相关技术规范,提出小型水库雨水情测报与工程安全监测标准化建议,从监测项目和测点布置、仪器设备配置、软件功能及模型开发几个方面入手,根据水库实际安全风险进行小型水库的标准化研究,推进编制适合我国的小型水库相关技术标准、细化具体配置和运行要求,为小型水库监测系统完善和标准化提供参考。     

基于北斗与InSAR技术的城市水库群坝体变形监测体系研究

随着我国核心城市规模的不断扩大,城市水库群的安全问题变得非常重要。本文提出基于北斗与InSAR技术构建城市水库群坝体变形监测体系的概念,建立以InSAR卫星数据为核心的高效监测能力,并建设覆盖区域的北斗变形监测基准网,通过获取InSAR动态监测数据以及北斗卫星实时监测数据,辅助以人工监测数据,形成城市水库群坝体安全监测新模式。     

基于宽度学习的结构沉降预测

结构监测是确保工程结构建设在施工和运营阶段安全的关键因素,因此采用合理有效的预测模型对结构沉降监测数据进行科学准确的预测成为了当前结构沉降预测研究的重点。针对传统预测方法与深度学习方法用于结构沉降预测存在的预测精度不够高、模型结构复杂、训练耗时等问题,提出了一种基于宽度学习的结构沉降时间序列预测模型。通过实测地铁地下隧道沉降监测数据对宽度学习、人工神经网络、支持向量回归和深度置信网络-支持向量回归预测模型的预测结果进行对比分析。实验结果表明:宽度学习系统(broad learning system,BLS)应用于结构沉降预测具有良好的效果,其训练速度更快,预测精度更高。验证了所提出的宽度学习算法应用于结构沉降预测的可实施性和有效性。     

The new method of arch dam stress calculation and the application of GTSTRUDL CAE/CAD system

In this paper, a new method for arch dam stress calculation is provided. In this method, the recurrence formulation for determining the mechanical parameters in the horizontal arch is obtained by recurrence method from arch ends to arch crown where the reacting forces at the arch ends are considered as the initial parameters. Then, the equations for arch crown cantilever nodes are obtained by using the conditions of internal force equilibrium and displacement continuity. The computer program using this method saves a lot of CPU time and computer storage. The computation results for engineering problems and the comparison with finite element method from GTSTRODL software illustrate that this program is reliable and efficient.     

Deep learning-assisted real-time defect detection and closed-loop adjustment for additive manufacturing of continuous fiber-reinforced polymer composites

Real-time defect detection and closed-loop adjustment of additive manufacturing (AM) are essential to ensure the quality of as-fabricated products, especially for carbon fiber reinforced polymer (CFRP) composites via AM. Machine learning is typically limited to the application of online monitoring of AM systems due to a lack of accurate and accessible databases. In this work, a system is developed for real-time identification of defective regions, and closed-loop adjustment of process parameters for robot-based CFRP AM is validated. The main novelty is the development of a deep learning model for defect detection, classification, and evaluation in real-time with high accuracy. The proposed method is able to identify two types of CFRP defects (i.e., misalignment and abrasion). The combined deep learning with geometric analysis of the level of misalignment is applied to quantify the severity of individual defects. A deep learning approach is successfully developed for the online detection of defects, and the defects are effectively controlled by closed-loop adjustment of process parameters, which is never achievable in any conventional methods of composite fabrication.     

DRLSTM: A dual-stage deep learning approach driven by raw monitoring data for dam displacement prediction

Dam displacement is an important indicator of the overall dam health status. Numerical prediction of such displacement based on real-world monitoring data is a common practice for dam safety assessment. However, the existing methods are mainly based on statistical models or shallow machine learning models. Although they can capture the timing of the dam displacement sequence, it is difficult to characterize the complex coupling relationship between displacement and multiple influencing factors (e.g., water level, temperature, and time). In addition, input factors of most dam displacement prediction models are artificially constructed based on modelers’ personal experience, which lead to a loss of valuable information, thus prediction power, provided by the full set of raw monitoring data. To address these problems, this paper proposes a novel dual-stage deep learning approach based on one-Dimensional Residual network and Long Short-Term Memory (LSTM) unit, referred to herein as the DRLSTM model. In the first stage, the raw monitoring sequence is processed and spliced with convolution to form a combined sequence. After the timing information is extracted, the convolution direction is switched to learn the complex relationship between displacement and its influencing factors. LSTM is used to extract this relationship to obtain Stage I prediction. The second stage takes the difference between the actual measurement and the Stage I prediction as inputs, and LSTM extracts the stochastic features of the monitoring system to obtain Stage II prediction. The sum of two stage predictions forms the final prediction. The DRLSTM model only requires raw monitoring data of water level and temperature to accurately predict displacement. Through a real-world comparative study against four commonly used shallow learning models and three deep learning models, the root mean square error and mean absolute error of our proposed method are the smallest, being 0.198 mm and 0.149 mm respectively, while the correlation coefficient is the largest at 0.962. It is concluded that the DRLSTM model performance well for evaluating dam health status.     

心形线双曲拱坝三维建模

心形线作为几何数学上最美最浪漫的一条曲线,第一次在拱坝设计中研 究应用。在研究心形线几何特性时,发现有一段非常扁平的曲线,作为拱坝中轴线非常适合, 可以使得拱坝的合力方向向山里偏转,有利于坝肩的稳定。因此,研究心形线作为拱坝的一 种新线形,理论上具有可行性,并在实际工程中有一定的应用参考价值。该文在研究心形线 双曲拱坝各种参数的基础上,建立心形线双曲拱坝的数学方程,实现图形的三维建模,为拱 坝下一步进行有限元分析提供精确的三维模型,也为今后拱坝坝型的设计提供更多的选择和 有意义的参考。     

黄河下游坝垛险情自动监测预警探讨

坝垛险情监测预警是黄河防洪工程防守和保证防洪安全的关键措施,针对传统人工巡坝查险存在劳动强度高、工作效率低、漏报错报等难以解决的缺陷,对坝垛险情自动监测预警进行探讨：在坝垛建立前端基站和5G专网通信,实现前端信息采集和与后台的数据通信;在前端摄像头嵌入人工智能算法,对采集影像有无险情进行初步判断;在后台建立大数据平台,自动比对前端摄像头数据并进行险情精准预判,自动生成工程险情关键参数,实现预警坝垛险情自动监测预警目的。基于深度学习方法进行模拟试验,实验结果表明：利用5G传输、大数据和人工智能等新技术,进行黄河下游坝垛险情监测预警建设,可有效提升发现险情和应急上报能力,是未来黄河下游坝垛险情监测预警的重要手段,也是黄河信息化建设的重点和发展方向。     

浅谈减小碾压混凝土双曲拱坝内观监测差动电阻式仪器测量误差

大坝作为最主要的挡水建筑物,其安全性至关重要。一般根据内观监测结果判断其安全性,但是如果测量误差较大,则会导致得出错误结论,做出错误判断,造成无法挽回的损失。本文以象鼻岭水电厂大坝安全监测为例,对目前电厂差动电阻式仪器观测方法提出改进,减小系统误差,保证数据分析合理,通过正反测电阻比方法对测量仪器校核,对测量结果进行优化,提高测量精度,及时消除误差,未对仪器校核,观测人员只凭借经验,只根据正向电阻比测量,误差可达10个单位,对仪器校核分析以后可将测量误差控制在5个单位以内,精确度提高了1倍以上,同时也能及时发现问题,例如线路接头老化,钢丝锈蚀、脱落、导致测量结果失真等,并根据实际情况适当的作出一些修复,对于其他混凝土双曲拱坝内观监测具有一定借鉴和推广意义。     

Development of dam safety management system

Recently, we can see an increasing amount of dam damage or failure due to aging, earthquakes occurrence and unusual changes in weather. For this reason, dam safety is gaining more importance than ever before in terms of disaster management at a national level. Therefore, the government is trying to come up with an array of legal actions to secure consistent dam safety. Other dam management organizations are also taking various institutional and technical measures for the same purpose. The Korea Water Resource Corporation (Kwater) which is currently operating and managing 30 large dams, has developed a dam safety management system, KDSMS, for consistent and efficient dam safety management. The KDSMS consists of dam and reservoir data, a hydrological information system, a field inspection and data management system, instrumentation and monitoring system including earthquake monitoring, a field investigation and safety evaluation system, and a collective information system. The KDSMS is a kind of enterprise management system which has been developed to deal with safety management of each field, research center, and headquarter office and their correlation as well as detailed safety information management.     

An approach using random forest intelligent algorithm to construct a monitoring model for dam safety

The mechanism of dam safety monitoring model is analyzed; for the dam system comprehensive affected by multi-factor, the mapping relationship between the influence factors and the dam behavior effects domain is usually nonlinear. Synthesizing each kind of factor, 27 parameters are chosen as the main factors which affect the accuracy of the monitoring model. Taking the actual monitoring data as the evaluation factor, the dam safety monitoring model based on the random forest (RF) intelligent algorithm was built with the actual monitoring data to predict uplift pressure. At the same time, test the significance of each variable based on the RF monitoring model and calculate the importance degree of each variable for the model through the importance function. It is indicated that RF model can be relatively fast and accurately predict the uplift pressure of the dam according to the influence factors. The average prediction accuracy is more than 95%. As compared with other intelligent algorithms such as support vector machine, RF has better robustness, higher prediction accuracy, and faster convergence speed. Because of the uniformity of the calculation procedure and the universality of the prediction method, the RF model also has reasonable extrapolation for other dam safety monitoring models (such as crack opening and seepage discharge). Significance test results obtained by the two methods have shown that the impact of reservoir water level and daily rainfall on the uplift pressure is significant, and other factors’ impact on dam deformation is unstable and changes with the external environmental influence.

     

Dynamic response of gravity dam model with crack and damage detection

Gravity dam is a typical structure that has been frequently used in the fields of water conservancy engineering, and the safety of the structure has received widespread attention recently. Due to earthquakes or other reasons, gravity dams normally have damage such as cracks in practical service. Damage in the structures can alter the structural dynamic behavior and seriously affect structural performance. Maintaining safety and integrity of the gravity dam structures requires a better understanding of dynam...     

Seismic safety of arch dams with aging effects

Arch dams suffer time-varying external loadings and harsh environment that harm their physical properties. With the aging of such dams, damage accumulates and concrete degradation inevitably appears. In this paper, a model is proposed for simulating concrete degradation with aging because of chemo-mechanical damage. The seismic response of an arch dam with aging effects is analyzed using the proposed model. The results show that the damage caused by the aging of arch dams may result in an increase in tensil...