•A CNN-based surrogate model is used to predict transmission tower’s time history.•Complex wind is considered as the inducement of transmission tower.•Configuration, window size and training data are ...discussed to optimize CNN network.•The trained CNN can achieve RMSE around 0.01 for time history prediction.
As the steel towers in the power system are vulnerable to intensive wind loads, it is essential to understand their dynamics response to estimate its potential failure. Conventional structural analysis methods like the finite element analysis or the field test are either computational heavy or cost expensive. Thus, this paper proposes a machine learning approach based on convolutional neural network (CNN) to predict the time history response of the transmission tower during the complex wind input. By preprocessing the time history of wind load and the tower’s dynamic response, a well-developed CNN can capture the time and spatial correlation of the wind load successfully and provide high accuracy results. CNN configuration, window size selection, and training data scale are carefully discussed to optimize the CNN design to maximize the prediction accuracy as well as minimize its computational time. Finally, to evaluate the performance of the surrogate model, the accuracy of the optimal CNN is tested in predicting the time history response of the transmission tower under 15 m/s to 70 m/s wind speed. The effectiveness of the CNN surrogate model is validated through a fragility model development, and its robustness is investigated using two wind inputs generated from a random wind profile and a random wind spectrum.
The success of the unscented Kalman filter can be jeopardized if the required initial parameters are not identified carefully. These parameters include the initial guesses and the levels of ...uncertainty in the target parameters and the process and measurement noise parameters. While a set of appropriate initial target parameters give the unscented Kalman filter a head start, the uncertainty levels and noise parameters set the rate of convergence in the process. Therefore, due to the coupling effect of these parameters, an inclusive approach is desired to maintain the chance of convergence for expensive experimental tests. In this paper, a framework is proposed that, via a virtual emulation prior to the experiment, determines a set of initial conditions to ensure a successful application of the online parameter identification. A Bayesian optimization method is proposed, which considers the level of confidence in the initial guesses for the target parameters to suggest the appropriate noise covariance matrices. The methodology is validated on a five-story shear frame tested on a shake table. The results indicate that, indeed, a trade-off can be made between the robustness of the online updating and the final parameter accuracy.
Soil and groundwater Cr(VI) pollution resulting from improper disposal and accidental spills is a critical problem worldwide. In this study, a comprehensive study was conducted to assess the ...hydrogeological conditions of a contaminated site, obtain spatiotemporal distribution and trend forecasts of pollutant Cr(VI), and determine the feasibility of applying clayey engineered barriers for pollution control. The results showed that the hydraulic conductivity (K) of the clayey barrier (1.56E-5 m/d) is several orders of magnitude lower than that of the stratum beneath the contaminated site, with K values ranging from 0.0014 to 4.76 m/d. Cr(VI) exhibits high mobility and a much higher concentration in the vadose zone, with maximum values of 6100 mg/kg in topsoil and 2090 mg/L in the perched aquifer. The simulation results indicated that the groundwater in the vicinity of the contaminated site, as well as downstream of the Lianshui River, is seriously threatened by Cr(VI). Notably, the pollution plume could occur downstream of the Lianshui River after 8 years. The retention efficiency of clayey engineered barriers will decrease over time, at 61.6% after 8 years and 33% after 20 years. This work contributes to an in-depth understanding of Cr(VI) migration at contaminated sites.
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•Cr(VI) concentrated in the topsoil in slag accumulation and production area.•The silty clay layer acted as a horizontal barrier to against the vertical migration of Cr(VI).•The laterite-bentonite barrier shows a good retention performance of Cr(VI).•The retention efficiency of engineered barrier decreased for the chemical compatibility.
The accuracy of transmission tower-line system simulation is highly impacted by the transmission line model and its coupling with the tower. Owing to the high geometry nonlinearity of the ...transmission line and the complexity of the wind loading, such analysis is often conducted in the commercial software. In most commercial software packages, nonlinear truss element is used for cable modeling, whereas the initial strain condition of the nonlinear truss under gravity loading is not directly available. Elastic catenary element establishes an analytical formulation for cable structure under distributed loading; however, the nonlinear iteration to reach convergence can be computational expensive. To derive an optimal transmission tower-line model solution with high fidelity and computational efficiency, an open-source three-dimensional model is developed. Nonlinear truss element and elastic catenary element are considered in the model development. The results of the study imply that both elements are suitable for the transmission line model; nevertheless, the initial strain in nonlinear truss element largely impacts the model accuracy and should be calibrated from the elastic catenary model. To cross-validate the developed models on the coupled transmission tower and line, a one-span eight-line system is modeled with different elements and compared with several state-of-the-art commercial packages. The results indicate that the displacement time-history root-mean-square error (RMSE) of the open-source transmission tower-line model is less than 1% and with a 66% computational time reduction compared with the ANSYS model. The application of the open-source package transmission tower-line model on extreme wind speed considering the aerodynamic damping is further implemented.
As-built Building Information Models (BIM) are increasingly used to facilitate the management of all aspects of built infrastructure's life cycle. Existing studies mainly focus on automating as-built ...BIM development for surface elements but often ignore embedded elements such as rebar due to the inaccessibility with typical sensing devices, such as image-based or time-of-flight-based methods. To tackle the issue, this research innovatively utilizes Ground Penetrating Radar (GPR) and the photogrammetry method together to generate BIMs for in-service buildings considering both surface elements (e.g., column, slab, wall) and rebar. As-built BIM for surface elements is developed at first and rebar is identified by using GPR. A calibration label is designed and attached to elements which are scanned by GPR device, and a series of images are captured from those elements and then used with other images to generate point clouds. Faster RCNN is utilized to recognize labels among all images. Then, an inverse photogrammetry approach is deployed to identify the scanned elements in BIM. By matching the recorded timestamps of GPR data and labeled images, links between the rebar in GPR data and elements in BIMs are successfully developed. Finally, IFC is developed to generate as-built BIM models. Six cases studies demonstrate the system provides a highly automated procedure to develop as-built BIM, and rebars could be efficiently localized and projected into corresponding elements in BIM.
Regional damage simulation is a promising method to prepare organizations for the unforeseeable impact of a probable seismic natural hazard. Nonlinear time history analysis (NLTHA) of the finite ...element models (FEM) of the buildings in a region can provide resembling results to the actual buildings’ damages and responses. This approach requires large-scale computational resources, and to improve efficiency, parallel processing and representing building FEM models with lumped mass models are proposed. However, the computing complexity is still far-reaching when high-performance computing is not available. The building inventory of a region consists of numerous similar buildings with a limited number of distinct structures. In this paper, we propose a data-driven method that runs the NLTHA for the distinct structures exclusively and infers the damage and responses of other buildings using a surrogate model. Considering the skewed distribution of the buildings in a region, a novel informative sample selection method is proposed that is designed for bimodal sampling of the input domain. We use the Gaussian process regression as the surrogate model and compare the performance of different sample selection methods. The proposed method is able to approximate the results of the regional damage simulation regarding total economic loss estimation with 98.99% accuracy while reducing the computational demand to about 1/7th of the simulation processing time.
Predictable weather-related natural hazards are tied to a significant portion of the blackouts in the United States. Since conventional reliability standards are not designed for such conditions, ...they fail to guarantee reliable delivery of power during such extreme events. This paper proposes a two-stage model for preventive operation of power systems during a natural hazard. At the first stage, failure probability of transmission towers is calculated based on fragility analysis of the structure. The failure scenarios and their probabilities are, then, generated through the analysis conducted in the first stage. At the second stage, the failure scenarios and their probabilities are used in stochastic optimization framework to explicitly model the likely failure of transmission elements. Simulation studies, carried out on IEEE 118-bus system, show that the proposed method can effectively eliminate network violations through preventive adjustment of generation dispatch. The results also show that the redispatch cost is not significant, compared to the potential consequences of shedding load.
In a typical Real-time Hybrid Simulation (RTHS) setup, servo-hydraulic actuators serve as interfaces between the computational and physical substructures. Time delay introduced by actuator dynamics ...and complex interaction between the actuators and the specimen has detrimental effects on the stability and accuracy of RTHS. Therefore, a good understanding of servo-hydraulic actuator dynamics is a prerequisite for controller design and computational simulation of RTHS. This paper presents an easy-to-use parametric identification procedure for RTHS users to obtain re-useable actuator parameters for a range of payloads. The critical parameters in a linearized servo-hydraulic actuator model are optimally obtained from genetic algorithms (GA) based on experimental data collected from various specimen mass/stiffness combinations loaded to the target actuator. The actuator parameters demonstrate convincing convergence trend in GA. A key feature of this parametric modeling procedure is its re-usability under different testing scenarios, including different specimen mechanical properties and actuator inner-loop control gains. The models match well with experimental results. The benefit of the proposed parametric identification procedure has been demonstrated by (1) designing an H∞ controller with the identified system parameters that significantly improves RTHS performance; and (2) establishing an analysis and computational simulation of a servo-hydraulic system that help researchers interpret system instability and improve design of experiments.
•A procedure to conduct parametric identification for a servo-hydraulic actuator is proposed.•Physical system parameters are estimated using Genetic Algorithms.•Resulting parameters are applicable to a range of payload conditions and control gains.•Identified parameters are used to help controller design and stability analysis.
Severe weather threatens the reliability of the power supply by damaging the network. In the case of hurricanes, tens of elements may fail, which would lead to power outages. Under such ...circumstances, preventive unit commitment methods can model the probabilistic failure forecasts and minimise the power outages. Preventive stochastic unit commitment is an effective method to consider failure forecasts to reduce the power outage. Although stochastic unit commitment produces high‐quality solutions, it is computationally burdensome. Thus, this paper evaluates proxy deterministic methods with lighter computational compared with stochastic unit commitment on both the solution time and quality. Adjusted spinning reserve requirements, engineering judgment‐based rules, and robust preventive operation are among the evaluated methods. Numerical results are obtained for the synthetic grid on the footprint of Texas with 2000 buses. The results suggest that while some proxy methods, such as standard spinning‐reserve and adjusted spinning‐reserve with 6% to 30% of the spinning capacity, may not be as effective as the stochastic method, others, such as robust optimisation, deliver the majority of the stochastic benefits with substantially less (85%) computational time. Monte Carlo simulations are used to evaluate the quality of solutions in reducing the expected unserved load and over‐generation.
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