Neural Network Surrogate Model Research Articles

Aerodynamic Optimization Method for Propeller Airfoil Based on DBO-BP and NSWOA

To address the issues of tedious optimization processes, insufficient fitting accuracy of surrogate models, and low optimization efficiency in drone propeller airfoil design, this paper proposes an aerodynamic optimization method for propeller airfoils based on DBO-BP (Dum Beetle Optimizer-Back-Propagation) and NSWOA (Non-Dominated Sorting Whale Optimization Algorithm). The NACA4412 airfoil is selected as the research subject, optimizing the original airfoil at three angles of attack (2°, 5° and 10°). The CST (Class Function/Shape Function Transformation) airfoil parametrization method is used to parameterize the original airfoil, and Latin hypercube sampling is employed to perturb the original airfoil within a certain range to generate a sample space. CFD (Computational Fluid Dynamics) software (2024.1) is used to perform aerodynamic analysis on the airfoil shapes within the sample space to construct a sample dataset. Subsequently, the DBO algorithm optimizes the initial weights and thresholds of the BP neural network surrogate model to establish the DBO-BP neural network surrogate model. Finally, the NSWOA algorithm is utilized for multi-objective optimization, and CFD software verifies and analyzes the optimization results. The results show that at the angles of attack of 2°, 5° and 10°, the test accuracy of the lift coefficient is increased by 45.35%, 13.4% and 49.3%, and the test accuracy of the drag coefficient is increased by 12.5%, 39.1% and 13.7%. This significantly enhances the prediction accuracy of the BP neural network surrogate model for aerodynamic analysis results, making the optimization outcomes more reliable. The lift coefficient of the airfoil is increased by 0.04342, 0.01156 and 0.03603, the drag coefficient is reduced by 0.00018, 0.00038 and 0.00027, respectively, and the lift-to-drag ratio is improved by 2.95892, 2.96548 and 2.55199, enhancing the convenience of airfoil aerodynamic optimization and improving the aerodynamic performance of the original airfoil.

Artificial Neural Network Surrogate Model for Geochemical Calculations in Pore-Scale Reactive Transport Simulations

Artificial Neural Network Surrogate Model for Geochemical Calculations in Pore-Scale Reactive Transport Simulations

Heat transfer performance optimization of turbine blade internal cooling channel with pin-fins and recessed-extruded endwalls

This paper involves implementing and optimizing a novel recessed-extruded endwall design for a rectangular cooling channel equipped with pin-fins. The study encompasses an in-depth exploration of the flow dynamics, pondering the generation and sustenance of vortices within the channel, using Reynolds-averaged Navier-Stokes analysis. The study delves into the evaluation of heat transfer characteristics, considering crucial parameters such as Nusselt number, friction factor, and overall heat transfer performance. These attributes are contrasted with the corresponding values observed in cases featuring flat endwalls, concerning Reynolds numbers spanning from 7400 to 36,000. A parametric study of the channel with recessed-extruded endwalls is conducted, aiming to comprehend the influence of geometric factors on channel heat transfer. A single-objective optimization process focusing on enhancing heat transfer effectiveness, quantified through the heat transfer efficiency index (HTEI), is conducted with three design variables. The results indicate that the new endwall configuration significantly enlarges the high heat transfer areas near the pin-fins compared to the flat endwall across all Reynolds numbers. This novel endwall design substantially elevates the heat transfer levels adjacent to the pin-fins, showcasing an impressive 62.4% increase in HTEI at Re = 21,500 compared to the flat endwall, with a protrusion height of 2.5% of the channel height. Furthermore, by increasing the height, the recessed-extruded endwall configuration can achieve a remarkable 91.1% increase in HTEI compared to the flat endwall. This achievement is reached with a protrusion height equivalent to 5% of the channel height at Re = 7400. Through design optimization using the radial basis neural network (RBNN) surrogate model and particle swarm optimization (PSO) technique, the HTEI of the recessed-extruded endwall design shows enhancements of 91.7% and 18.0% compared with the flat endwall and reference designs, respectively, at a Reynolds number of 21,500. The present work demonstrates the potential for enhancing the heat transfer of pin-fins channels by improving endwall design.

Fatigue reliability analysis of bogie frames considering parameter uncertainty

With the increasing service life of bogie frames, the risk of fatigue failure becomes significant, making fatigue reliability analysis crucial for ensuring operational safety. However, accurately analyzing fatigue reliability presents a significant challenge with uncertain factors such as load fluctuations, unstable material shaping, and dimensional manufacturing deviations. To address this, this paper establishes a comprehensive active learning reliability framework based on surrogate models, enabling high-fidelity modeling and precise fatigue reliability analysis of welded frames under parameter uncertainty. The material utilization method was developed using APDL for secondary development to efficiently evaluate frame fatigue failure indicators. The effectiveness of this method was validated by combining the improved Goodman-Smith fatigue limit diagram and test bench fatigue tests, which helped identify the locations on the frame most prone to fatigue fractures. An Atom Search Optimization-BP Neural Network surrogate model was established with the objective of maximum material utilization, and the fatigue reliability of the bogie frame was obtained by combining the active learning function and the Monte Carlo method. The results show that the uncertainty design parameters greatly impact the fatigue reliability of critical welded structures. The proposed method improves the accuracy and efficiency of the fatigue reliability analysis of the bogie frame.

Autoinjector optimization through cavitation response and severity minimization

Abrupt acceleration of the syringe of an autoinjector (AI) upon rod-plunger impact may induce undesired severe cavitation events and impose extraneous stresses upon the device, leading to device failure. Cavitation results from a rapid and significant pressure drop in a liquid, leading to the formation and growth of small vapor-filled cavities. Upon collapse, these cavities generate an intense shock wave that may lead to protein aggregation and device container damage and shatter. Since the maximum acceleration of the syringe depends upon the operating conditions of the AI, the severity of cavitation will likewise depend on the operating conditions of the AI. Likewise, injection time and ensuring proper needle displacement before drug release also depend on operating conditions, making optimization of the autoinjector a multiobjective optimization problem.Therefore, in this study, optimization of an autoinjector to limit cavitation severity is pursued via an experimentally validated computational model for cavitation in spring-driven autoinjectors. Our goal is to locate AI design configurations that balance maximizing device performance and patient comfort and minimizing the risks of device damage and severe cavitation upon actuation.Relevant parameters of interest are the drive spring force, air gap height, solution viscosity, friction between the rod and spring, frictional force on the plunger, rates of change of frictional force on the plunger, elasticity of plunger, viscosity of the plunger, and initial displacement between the plunger and the driving rod. The kinematics of the syringe barrel, needle displacement (travel distance) at the start of drug delivery, and injection time are gathered using an experimentally validated autoinjector kinematics model. At the same time, cavitation bubble dynamics are resolved using an experimentally validated cavitation model that takes the temporal displacement of the syringe and temporal air gap pressure as inputs.We use our experimentally validated models to explore the parameter space and understand the driving factors of our desired outcomes. Subsequently, we pose the design problem as a multi-objective optimization problem and develop a deep neural network surrogate model supplemented with iterative learning to speed up optimization. A variance-based sensitivity analysis was performed to determine the sensitivity and influence of design parameters on the outcomes, and the main contributors to the outcomes of interest were isolated. Using a multi-objective optimization framework, we located 300 + successful candidates and evaluated them through uncertainty analysis to identify three promising candidates that meet all criteria for drug viscosities of interest. Finally, we show that this methodology can be used to conduct hypothesis testing, leading to novel design configurations.

Information FOMO: The Unhealthy Fear of Missing Out on Information-A Method for Removing Misleading Data for Healthier Models.

Misleading or unnecessary data can have out-sized impacts on the health or accuracy of Machine Learning (ML) models. We present a Bayesian sequential selection method, akin to Bayesian experimental design, that identifies critically important information within a dataset while ignoring data that are either misleading or bring unnecessary complexity to the surrogate model of choice. Our method improves sample-wise error convergence and eliminates instances where more data lead to worse performance and instabilities of the surrogate model, often termed sample-wise "double descent". We find these instabilities are a result of the complexity of the underlying map and are linked to extreme events and heavy tails. Our approach has two key features. First, the selection algorithm dynamically couples the chosen model and data. Data is chosen based on its merits towards improving the selected model, rather than being compared strictly against other data. Second, a natural convergence of the method removes the need for dividing the data into training, testing, and validation sets. Instead, the selection metric inherently assesses testing and validation error through global statistics of the model. This ensures that key information is never wasted in testing or validation. The method is applied using both Gaussian process regression and deep neural network surrogate models.

Artificial Neural Network and Kriging Surrogate Model for Embodied Energy Optimization of Prestressed Slab Bridges

The main objective of this study is to assess and contrast the efficacy of distinct spatial prediction methods in a simulation aimed at optimizing the embodied energy during the construction of prestressed slab bridge decks. A literature review and cross-sectional analysis have identified crucial design parameters that directly affect the design and construction of bridge decks. This analysis determines the critical design variables to improve the deck’s energy efficiency, providing practical guidance for engineers and professionals in the field. The methods analyzed in this study are ordinary Kriging and a multilayer perceptron neural network. The methodology involves analyzing the predictive performance of both models through error analysis and assessing their ability to identify local optima on the response surface. The results show that both models generally overestimate the observed values. The Kriging model with second-order polynomials yields a 4% relative error at the local optimum, while the neural network achieves lower root mean square errors (RMSEs). Neither the Kriging model nor the neural network provides precise predictions but point to promising solution regions. Optimizing the response surface to find a local minimum is crucial. High slenderness ratios (around 1/28) and 40 MPa concrete grade are recommended to improve energy efficiency.

Predictive analysis of tensile strength ratios in laminated bamboo composites: Unraveling the stochastic impact of ply angle variations through machine learning model

Laminated bamboo composites (LBC), made by sandwiching bamboo strips, offer promising alternatives to traditional construction materials, especially for housing. However, subjection to the continuous static loading makes these materials initiate cracks inside their various ply. This study uses classical laminate theory (CLT) to determine the strength ratio (SR) of the LBC at different ply orientations by applying Tsai-Wu and Tsai-Hill failure criteria using MATLAB. The study aims to calculate the SRs for LBCs using CLT, employing an ANN model and stochastic finite element (FE) modeling to investigate SRs of five-layered LBCs with varying ply orientations. Applying CLT, the highest SR was determined to be 1.5375 × 107 N/m for the [0°/0°/0°/0°/0°] laminate, as per both failure theories. The study reveals substantial SR variations depending on ply orientation, consistently showing higher SR predictions with the Tsai-Wu theory compared to the Tsai-Hill theory. Next, the study emphasizes the deterministic methodologies to account for the stochastic effects of ply angle on the obtained SR of LBCs. Monte Carlo simulation (MCS) was utilized to model 10,000 randomly generated ply angle inputs and their associated SRs. By incorporating MCS to introduce ±1% variations in ply angles and utilizing normally distributed data, this research effectively captures uncertainties associated with ply orientation. Finally, to forecast the random SR of LBC with various ply orientations, an artificial neural network (ANN) surrogate model is employed. The stochastic analysis confirms the need to quantify the associated uncertainties. The findings of this study are crucial for advancing the application of LBC in sustainable construction, providing valuable insights into their mechanical behavior under different ply orientations, considering the stochastic effect in properties.

A crack identification scheme based on neural network surrogate model and XFEM

Crack detection and identification is of great significance to the safety issues of engineering structures. In this paper, an intelligent crack identification scheme based on extended finite element and neural network surrogate model is proposed to realize the accurate identification of crack parameters. The method firstly employs extended finite element forward analysis to obtain the displacement data of measurement points on geometric models with different crack lengths, and inputs them as sample data to train the agent model, establishes a neural network-based inverse analysis model for crack identification, and automatically updates the threshold and weight of the neural network by using the Gray Wolf optimization algorithm to finally compute the globally optimal results. In the screening of the surrogate model, this paper verifies the advantages of the neural network surrogate model in data fitting and crack information extraction by comparing and analyzing the characteristics of neural network, support vector machine and other surrogate models, and optimizing the neural network surrogate model by adopting the Gray Wolf optimization algorithm. Finally, several numerical examples of different types of cracks are given to verify the validity of the proposed method, and the results show that the proposed method can accurately invert the geometric information of cracks.

Optimizing an auxetic metamaterial structure for enhanced mechanical energy absorption: Design and performance evaluation under compressive and impact loading

Auxetic metamaterials, characterized by their negative Poisson’s ratio, offer promising prospects for utilization in absorbing energy during quasi-static compressive loading as well as in applications requiring impact energy absorption. The optimization of auxetic structures’ geometrical parameters can improve their performance. This research aims to optimize the design of an auxetic structure for maximum specific energy absorption and investigate its behavior under quasi-static compressive and high-velocity impact loading. The geometrical parameters of the cross-petal auxetic structure are optimized using genetic algorithm and a neural network surrogate model. The behavior of the optimized auxetic structure is examined in quasi-static compressive loading and compared with that of the basic auxetic structure using finite element simulations. The optimized auxetic structure is then evaluated in high-velocity impact loading as the core of a sandwich panel, with two plates placed in the front and rear. Simulations of projectile impacts at velocities ranging from 100 to 250 m/s reveal the sandwich panel’s behavior. Results indicate a 69.82% increase in specific energy absorption capacity for the optimized auxetic structure as compared to the basic structure in quasi-static compressive loading. In high-velocity impact, the sandwich panel with the optimal auxetic core outperforms the one with the basic core. At velocities more than the minimum perforation velocity, the core contributes about 64%–67% of the total absorbed energy by the sandwich panel.

Statistical data analysis of x-ray spectroscopy data enabled by neural network accelerated Bayesian inference.

Bayesian inference applied to x-ray spectroscopy data analysis enables uncertainty quantification necessary to rigorously test theoretical models. However, when comparing to data, detailed atomic physics and radiation transfer calculations of x-ray emission from non-uniform plasma conditions are typically too slow to be performed in line with statistical sampling methods, such as Markov Chain Monte Carlo sampling. Furthermore, differences in transition energies and x-ray opacities often make direct comparisons between simulated and measured spectra unreliable. We present a spectral decomposition method that allows for corrections to line positions and bound-bound opacities to best fit experimental data, with the goal of providing quantitative feedback to improve the underlying theoretical models and guide future experiments. In this work, we use a neural network (NN) surrogate model to replace spectral calculations of isobaric hot-spots created in Kr-doped implosions at the National Ignition Facility. The NN was trained on calculations of x-ray spectra using an isobaric hot-spot model post-processed with Cretin, a multi-species atomic kinetics and radiation code. The speedup provided by the NN model to generate x-ray emission spectra enables statistical analysis of parameterized models with sufficient detail to accurately represent the physical system and extract the plasma parameters of interest.

Insights into Flexible Bioinspired Fins for Unmanned Underwater Vehicle Systems through Deep Learning.

The last few decades have led to the rise of research focused on propulsion and control systems for bio-inspired unmanned underwater vehicles (UUVs), which provide more maneuverable alternatives to traditional UUVs in underwater missions. Recent work has explored the use of time-series neural network surrogate models to predict thrust and power from vehicle design and fin kinematics. We expand upon this work, creating new forward neural network models that encapsulate the effects of the material stiffness of the fin on its kinematic performance, thrust, and power, and are able to interpolate to the full spectrum of kinematic gaits for each material. Notably, we demonstrate through testing of holdout data that our developed forward models capture the thrust and power associated with each set of parameters with high resolution, enabling highly accurate predictions of previously unseen gaits and thrust and FOM gains through proper materials and kinematics selection. As propulsive efficiency is of utmost importance for flapping-fin UUVs in order to extend their range and endurance for essential operations, a non-dimensional figure of merit (FOM), derived from measures of propulsive efficiency, is used to evaluate different fin designs and kinematics and allow for comparison with other bio-inspired platforms. We use the developed FOM to analyze optimal gaits and compare the performance between different fin materials. The forward model demonstrates the ability to capture the highest thrust and FOM with good precision, which enables us to improve thrust generation by 83.89% and efficiency by 137.58% with proper fin stiffness and kinematics selection, allowing us to improve material selection for bio-inspired fin design.

Neural networks for emergent behavior in biological microstructures

PurposeThis paper develops a neural network surrogate model based on a discrete lattice approach to investigate the influence of complex microstructures on the emergent behavior of biological networks.Design/methodology/approachThe adaptability of network-forming organisms, such as, slime molds, relies on fluid-to-solid state transitions and dynamic behaviors at the level of the discrete microstructure, which continuum modeling methods struggle to capture effectively. To address this challenge, we present an optimized approach that combines lattice spring modeling with machine learning to capture dynamic behavior and develop nonlinear constitutive relationships.FindingsThis integrated approach allows us to predict the dynamic response of biological materials with heterogeneous microstructures, overcoming the limitations of conventional trial-and-error lattice design. The study investigates the microstructural behavior of biological materials using a neural network-based surrogate model. The results indicate that our surrogate model is effective in capturing the behavior of discrete lattice microstructures in biological materials.Research limitations/implicationsThe combination of numerical simulations and machine learning endows simulations of the slime mold Physarum polycephalum with a more accurate description of its emergent behavior and offers a pathway for the development of more effective lattice structures across a wide range of applications.Originality/valueThe novelty of this research lies in integrating lattice spring modeling and machine learning to explore the dynamic behavior of biological materials. This combined approach surpasses conventional methods, providing a more holistic and accurate representation of emergent behaviors in organisms.

Learning experimental data to predict fluid-elastic instability and optimize configuration of tube arrays

Fluid-elastic instability (FEI) poses a particularly significant challenge to the safety of steam generators, given its potential to cause substantial damage in short periods. The tube bundle system model is inherently complex, resulting in expensive costs for experiments and numerical simulations during the design process. In this study, a substantial volume of FEI experimental data, conducted by various scholars, was compiled to create FEI surrogate model. The accuracy and feasibility of this surrogate model were demonstrated and compared to experimental results. This work represents the pioneering reference on the optimization of tube array configurations, achieved by combining FEI deep neural network (DNN) surrogate model with genetic algorithm (GA). The design variables for optimization encompassed tube array configurations and structural parameters. The optimization objective is to achieve a tubular array structure exhibiting a higher critical velocity, Ur, enabling safe operation of the heat exchanger tube bundle across a wider range of flow rates without any FEI accident. The developed approach holds promise for data-driven FEI analysis and optimized tube bundle design, resulting in substantial time and cost savings. We publicly share all code implementations, and we believe that our efforts open a door for the surrogate-model-assisted structural optimization of tube arrays.

Robust optimization of a novel ultraviolet (UV) photoreactor for water disinfection: A neural network approach

To optimize the ultraviolet (UV) water disinfection process, it is crucial to determine the ideal geometric dimensions of a corresponding model that enhance performance while minimizing the impact of uncertain photoreactor inputs. As water treatment directly affects people's lives, it is crucial to eliminate the risks associated with the non-ideal performance of disinfection photoreactors. Input uncertainties greatly affect photoreactor performance, making it essential to develop a robust optimization algorithm in advance to mitigate these effects and minimize the physical and financial resources required for constructing the photoreactors. In the suggested algorithm, a two-objective genetic algorithm is integrated with a non-intrusive polynomial chaos expansion (PCE) technique. Additionally, the Sobol sampling method is employed to select the necessary samples for understanding the system's behavior. An artificial neural network surrogate model is trained using sufficient data points derived from computational fluid dynamics (CFD) simulations. A novel type of UV photoreactors working based on exterior reflectors is chosen to optimize the process with three uncertain input parameters, including UV lamp power, UV transmittance of water, and diffusive fraction of the reflective surface. In addition, four geometrical design variables are considered to find the optimal configuration of the photoreactor. The standard deviation (SD) and the reciprocal of log reduction value (LRV) are set as the objective functions, calculated using PCE. The optimal design provides a LRV of 3.95 with SD of 0.2. The coefficient of variation (CoV) of the model significantly declines up to 7%, indicating the decreased sensitivity of the photoreactor to the input uncertainties. Additionally, it is discovered that the robust model exhibits minimal sensitivity to changes in reflectivity in various flow rates, and its output variability aligns with the SD obtained through robust optimization.

Model-predictive space heating control for energy flexibility – a case study using a long short-term memory neural network surrogate model and a genetic optimization algorithm

This paper presents a case study where a model predictive control (MPC) logic is developed for energy flexible operation of a space heating system in an educational building. A Long Short-Term Memory Neural Network (LSTM) surrogate model is trained on the output of an EnergyPlus building simulation model. This LSTM model is used within an MPC framework where a genetic algorithm is used to optimize setpoint sequences. The EnergyPlus model is used to validate the performance of the control logic. The MPC approach leads to a substantial reduction in energy consumption (7%) and energy costs (13%) with improved comfort performance. Additional energy costs savings are possible (7–16%) if a sacrifice in indoor thermal comfort is accepted. The presented method is useful for developing MPC systems in the design stages where measured data is typically not available. Additionally, this study illustrates that LSTM models are promising for MPC for buildings.

Damage detection of truss structures using meta-heuristic algorithms and optimized group method of data handling surrogate model

Optimization-based methods are increasingly being implemented for structural damage detection (SDD) problems through the minimization of the objective functions based on vibration data. Meanwhile, the challenge of high computational cost hinders the applied use of SDD methods, especially upon addressing large-scale structures. In this study, a two-stage damage detection approach is proposed for damage localization as well as extent quantification which reduces the computational time of SDD problems. In the first stage of the proposed framework, suspected locations of damage are identified by employing a residual force vector. Then in the second stage, damage extent of already identified elements will be assessed by model updating method through three different optimization algorithms, namely particle swarm optimization (PSO), differential evolution (DE), and enhanced colliding bodies optimization (ECBO). To spare time-consuming modal analysis in each iteration of optimization algorithms, a PSO-based group method of data handling (GMDH) polynomial neural network (PNN) surrogate model is proposed. Performance of GMDH PNN strongly depends on the input variables, the number of input variables, and the order of the polynomial. The PSO-based GMDH PNN, developed in this study, employs PSO for the optimum selection of these parameters. Furthermore, the efficiency of Elemental Energy Quotient Indicator (EEQI) as a new damage index proposed in this paper is compared with the approach based on the natural frequency-based index. This paper also deals with the incomplete modal data measured from limited number of sensors. This problem is addressed by employing the model reduction technique and solving the SDD problem with modal information obtained from the reduced model. Numerical examples show that the suggested PSO-based GMDH surrogate model is an accurate tool for various types of SDD problems, and it can provide promising results with a significant reduction in computational time.

[formula omitted][formula omitted]ave [formula omitted]earner: Predicting wave farms power output using effective meta-learner deep gradient boosting model: A case study from Australian coasts

Precise prediction of wave energy is indispensable and holds immense promise as ocean waves have a power capacity of 30–40 kW/m along the coast. Utilising this energy source does not generate harmful emissions, making it a superior substitute for fossil fuel-based energy. The computational expense associated with simulating and computing intricate hydrodynamic interactions in wave farms restricts optimisation methods to a few thousand evaluations and makes a challenging situation for training in deep neural prediction models. To address this issue, we propose a new solution: a Meta-learner gradient boosting method that employs four multi-layer convolutional dense neural network surrogate models combined with an optimised extreme gradient boosting. In order to train and validate the predictive model, we used four wave farm datasets, including the absorbed power outputs and 2D coordinates of wave energy converters (WECs) located along the southern coast of Australia, Adelaide, Sydney, Perth and Tasmania. Furthermore, the capability of the transfer learning strategy is evaluated. The WECs used in this study are of the fully submerged three-tether converter type, similar to the CETO prototype. The effectiveness of the proposed approach is assessed by comparing it with 15 well-established and effective machine learning (ML) methods. The experimental findings indicate that the proposed model is competitive with other ML and deep learning approaches, exhibiting considerable accuracy of 88.8%, 90.0%, 90.3%, and 84.4% in Adelaide, Perth, Sydney and Tasmania and improved robustness in predicting wave farm power output.

Computational Fluid Dynamics–Deep Neural Network (CFD-DNN) Surrogate Model with Graphical User Interface (GUI) for Predicting Hydrodynamic Parameters in Three-Phase Bubble Column Reactors

Computational Fluid Dynamics–Deep Neural Network (CFD-DNN) Surrogate Model with Graphical User Interface (GUI) for Predicting Hydrodynamic Parameters in Three-Phase Bubble Column Reactors

Framework for assessing the performance of overhead transmission lines under wind-temperature effects

This paper presents a framework for evaluating the wind-induced resistance of overhead transmission lines (OTLs), aiming to overcome the limitations associated with disregarding temperature effects and relying on a single metric for failure assessment. The proposed framework factors in temperature influence, carefully considers applied loads and structural characteristics, and accurately calculates failure probability based on a specific case study. Meteorological data is initially collected to create a probabilistic model of wind speed and temperature distribution, accounting for correlation. Components and systems are graded based on structural characteristics and damage modes. The neural network surrogate model is trained with finite element computations for load-structural response sample sets. The surrogate model, in conjunction with logistic regression, generates samples to determine fragility. Finally, the total structural failure probability and monthly failure likelihood are computed by integrating the joint probability model and fragility. Analyses highlight temperature's significant impact on conductor-ground line safety, validating the proposed framework's rationale.