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Sensitivity analysis of urban flood model parameters is important for urban flood simulation. Efficient and accurate acquisition of sensitive parameters is the key to real-time model calibration. In order to quickly obtain the sensitive runoff parameters of the urban flood simulation model, this study proposes an artificial neural network-based identification method for sensitive parameters. Artificial neural network (ANN) models were constructed with the binary classification and multi-classification methods, and used environmental indicators that affect the parameter sensitivity of different hydrological response units as the input, with the sensitivity parameters of the Storm water management model (SWMM) being the output. The optimization of the ANN was realized by adjusting the number of nodes in the hidden layer and the maximum number of iterations. An example application was conducted in Zhengzhou, China. The results show that the binary classification ANN quickly identified sensitive parameters, and the prediction accuracy of all parameters exceeded 96%. Convergence can be achieved when the number of nodes in the hidden layer does not exceed twice the number of input nodes, and the maximum number of iterations does not exceed 200. Rapid and accurate identification of the sensitive runoff parameters of the urban flood simulation model was achieved, which reduced the time required for parameter sensitivity analysis.

The surrogate-based model optimization method has been successfully applied in various engineering optimization problems across multiple fields. However, when facing high-dimensional optimization problems with multi-design variables, the efficiency of surrogate-based model optimization is often hindered due to the proliferation of sample points and the computational complexity associated with high-dimensional matrices. In this work, an optimization method of adaptive dual-Kriging method based on parameter sensitivity analysis (PSAD-Kriging) is proposed to solve the inefficient problem of the surrogate-based model in high-dimensional optimization problem. In the PSAD-Kriging method, Kriging models with different accuracy are introduced to ensure the accuracy of optimization and improve optimization efficiency. The low-accuracy Kriging model is used for parameter sensitivity analysis to compute the sensitivity of design variables, which can reduce the dimensions of design space and improve optimization efficiency. The high-accuracy Kriging model is adopted to complete the adaptive filling point process to obtain the optimal solution of the optimization problem. The PSAD-Kriging method is applied to the vibration reduction optimization of the helicopter rotor test-bed, and compared with the other three traditional Kriging model method to verify the high efficiency and high accuracy of the PSAD-Kriging method proposed in this work. The results indicate that the PSAD-Kriging method improves the optimization efficiency by 30.02% on the premise of ensuring good prediction accuracy. Moreover, the maximum displacement response of the rotor test-bed is decreased by 31.32% after the vibration reduction optimization by PSAD-Kriging method. Therefore, the PSAD-Kriging method proposed in this work provides a novel solution for high-dimensional optimization problems with multi-design variables and can be effectively applied to engineering applications.

Sensitivity analysis of urban flood model parameters is important for efficient and accurate flood simulation. In order to explore the problems of large sampling parameters and nonlinear correlation between input and output variables, this paper proposed a new correlation analysis approach. The type, strength, and the order of sensitive parameters to the four outputs are analyzed using the proposed approach. The results show that the R values of Manning-N are biggest, its distribution is linear in heat maps, and the Manning-N has a strong linear correlation with Average Depth, Hour of Maximum Flooding, and Time to Peak. For Average Depth, the second sensitive parameter is Conductivity. For Hour of Maximum Flooding, the second and third more sensitive parameters are Conductivity and N-perv; however, there are certain nonlinear correlations from heat maps. For Total Inflow, the R values of each parameter are between 0.021 and 0.534. Most sensitive parameters are none; however, the more sensitive parameters are Conductivity, N-perv, and initial deficit. For Time to Peak, the second and third more sensitive parameters are N-perv and N-Imperv; however, there are certain non-linear correlations from heat maps. The results can provide theoretical guidance for application and parameter calibration of SWMM in airport.

To optimise the benefits of the deep-rolling process in the service life context of treated components, the process application must be investigated. In addition to the reduction in surface roughness and near-surface material strengthening, compressive residual stresses are introduced, which are primarily responsible for the increase in service life for components, especially in the case of high-strength steel materials. A numerical parameter sensitivity analysis is performed in order to investigate the introduced residual stresses in detail. For this purpose, a validated deep-rolling simulation model is used, which replicates the deep rolling of a railway axle made of the high-strength steel material 34CrNiMo6. The model is based on an elastic-plastic Chaboche material model parameterised on uniaxial tensile and LCF test results and validated with residual stress measurements. Using this model as a basis, the effect of the main process parameters deep-rolling force, feed rate, friction coefficient, number of overruns, tool geometry, and shaft geometry on the resulting residual stress state are investigated. The results reveal that the deep-rolling force has the most significant influence on the introduced residual stress state and should therefore be highlighted. In the case of applying a deep-rolling force of more than 10 kN, maximum compressive residual stresses of around − 1000 MPa are introduced, and a strong saturating behaviour is shown. Maximum compensating tensile residual stresses of + 100 MPa occur below the surface. The main influence of the deep-rolling force is the effective depth achieved, which is determined by the depth of the zero crossing. This varies from 1 mm with an applied force of 2 kN to more than 3.5 mm with 20 kN. Furthermore, the results are analysed to conclude suggestions for the process’s applicability, and a proposal for an optimised deep-rolling treatment is presented. There multiple deep rolling with decreased deep-rolling forces is used to achieve a comparably optimised residual stress state. In summary, with the presented results, a contribution to a deeper understanding of the deep-rolling process can be achieved; the influence of the most important process parameters on the residual stress in-depth profiles is established; an optimisation proposal is presented; and correlations are found. Thus, the base work for further fatigue strength assessments and the optimisation of the deep-rolling process regarding the increase of service is laid.

Rock brittleness is an essential mechanical property, which plays a significant role in rock classifications and rockburst risk evaluations. To overcome the problems associated with the traditional brittleness indexes not comprehensively charaterizing the rock strength and deformation behaviors, this study systematically summarized the existing rock brittleness indexes. Then, a novel brittleness index (BICSS) based on the complete stress–strain curves of rock under different confining pressures was proposed. Its advantages included innovatively considering the characteristic stresses and strains at the stage of crack initiation, the peak points, and residual points. The index also described the stress growth rates from the pre-peak crack-initiation stress to the peak stress points, as well as the stress drop rates from the peak stress to the residual stress points. This study conducted uniaxial and triaxial compression tests of metamorphic sandstone, granite, and gneiss obtained from a deeply buried long-line tunnel group. The aforementioned tests were combined with wave velocity tests and thin-section identification tests using polarizing microscopy techniques. The reliability and applicability of the index were then successfully verified. The results showed that the BICSS could not only quantify and classify the brittleness characteristics of different rock types and characterize the confining pressure inhibition behaviors of rock brittleness, but could also comprehensively express the influences of homogeneity, mineral compositions, and particle sizes on the rock brittleness. Finally, through the parameter sensitivity analysis of the BICSS, the influences of subjective errors in the results of the cracking initiation stress and strain values caused by the different selections during the linear elastic phase could be successfully excluded, resulting in the further verification of the stability of the BICSS.

With the development of structural health monitoring (SHM) technology, digital of long-span bridge construction has become the focus of intelligent construction in the future. Data have become the fifth largest factor of production after land, labor, capital and technology, that is, data have become the oil in the era of digital economy. Therefore, how to obtain the potential information behind the data has become a hot spot in the current research direction. Based on the SHM system of the world’s longest multi-tower cable-stayed bridge, this paper shares the environmental load data at the bridge site, including wind field (wind speed, wind direction), temperature and humidity data. In addition, the response data of cable structure, including acceleration signal data, are also shared. To analyze the influence of environment on cable structure, first, the environmental data are statistically characterized and probabilistic predicted. Then, the structural response data are also statistically characterized and probability predicted, and the frequency change value of the cable is obtained based on the Fourier transform method, and then the cable force change is estimated. Finally, the parameter sensitivity analysis is carried out based on Back Propagation neural network method to describe the mapping relationship between specific load source and structural response.

On-board estimation of battery state of charge (SOC) plays a critical role in various functionalities performed by battery management systems (BMS) applicable to electric vehicles (EVs). The traditional approach of SOC estimation uses offline identification of battery model parameters as a function of SOC. It requires an update of SOC-dependent parameters in EVs run-time. Since battery dynamics or model parameters change as a function of state of health (SOH), identifying and updating these parameters online is a crucial challenge. Researchers have recently presented many techniques of online state estimation, but they are unsuitable for deployment due to constraints from the embedded point of view. This article presents a detailed investigation and analysis of battery model parameter sensitivity concerning the entire range of SOC and over the life cycle, followed by simplified model-based SOC estimation. First, the second-order equivalent circuit model with hysteresis is developed and validated. The sensitivity of model parameters is investigated using a state-of-the-art one-factor-at-a-time (OFAT) approach to classify parameters as high and low sensitive and to propose a simplified model considering the compromise between accuracy and embedded computations. The extended Kalman filter-based SOC estimation at different SOHs is designed. In the case of lithium-ion NCA battery, the proposed simplified model yields maximum SOC error of 2%, 1.47%, and 3.27% at SOH levels 92.12%, 89.36%, and 85.96%, respectively. Similarly, for lithium-ion LFP battery, the proposed simplified model yields a maximum SOC error of 1.5% when SOH is 100%, which demonstrates how a simplified model provides satisfactory results compared to traditional methods and is suitable for embedded deployment due to reduced computations in run-time.

Subject-specific musculoskeletal models hold promise for adult spinal deformity management. However, fully subject-specific models require subject-specific soft-tissue properties not typically available in clinical settings. Models created using generic properties are more accessible but potentially less accurate. The objective of this study was to identify which biomechanical properties of muscle function, and in which specific body positions, exhibit significant differences when implementing generic versus subject-specific properties. Using OpenSim, we analyzed 250 subject-specific models, focusing on four muscle parameters: geometry-path, maximum-isometric-force, optimal-fiber-length, and tendon-slack-length across 11 postures, encompassing standing and flexed postures. A linear mixed-effects model evaluated the impact of muscle parameters on spinal compression loads. Differences in compression load between the models with subject-specific and generic data were compared statistically using non-parametric methods. Subject-specific geometry-path and maximum-isometric-force significantly influenced spinal compression loads, with mean differences of 13 % and 8 %, respectively. Differences were posture-dependent (geometry-path p < 0.001; max-isometric-force p = 0.005). Optimal-fiber-length (p = 0.053) and tendon-slack-length (p = 0.680) showed minimal impact (∼1% difference). Flexed postures were more sensitive to generic muscle parameters, with mean differences of 17 % (geometry-path) and 6 % (max-isometric-force), compared to standing (6 % and 4 %, respectively). The pronounced deviations observed in flexion simulations emphasized the necessity of subject-specific data in such simulations. However, when subject-specific data is not available, simulations based on standing postures are the least affected by the use of generic properties.

The systematic assessment of spatial and temporal distribution of groundwater recharge (GWR) is crucial for the sustainable management of the water resources systems, especially in large-scale river basins. This helps in identifying critical zones in which GWR largely varies and thus leads to negative consequences. However, such analyses might not be possible when the models require detailed hydro-climate and hydrogeological data in data-scarce regions. Hence, this calls for alternate suitable modeling approaches that are applicable with the limited data and, however, includes the detailed assessment of the spatial–temporal distribution of different water balance components especially the GWR component. This paper aimed at investigating the spatial and temporal distribution of the GWR at monthly, seasonal and annual scales using the WetSpass-M physically distributed hydrological model, which is not requiring the detailed catchment information. In addition, the study conducted the sensitivity analysis of model parameters to assess the significant variation of GWR. The large-scale river basins such as the Omo river basin, Ethiopia, were chosen to demonstrate the potential of the WetSpass-M model under limited data conditions. From the modeling results, it was found that the maximum average monthly GWR of 13.4 mm occurs in July. The estimated average seasonal GWR is 32.5 mm/yr and 47.6 mm/yr in the summer and winter seasons, respectively. Further, it was found that GWR is highly sensitive to the parameter such as average rainfall intensity factor.

In non-experimental research, a sensitivity analysis helps determine whether a causal conclusion could be easily reversed in the presence of hidden bias. A new approach to sensitivity analysis on the basis of weighting extends and supplements propensity score weighting methods for identifying the average treatment effect for the treated (ATT). In its essence, the discrepancy between a new weight that adjusts for the omitted confounders and an initial weight that omits them captures the role of the confounders. This strategy is appealing for a number of reasons including that, regardless of how complex the data generation functions are, the number of sensitivity parameters remains small and their forms never change. A graphical display of the sensitivity parameter values facilitates a holistic assessment of the dominant potential bias. An application to the well-known LaLonde data lays out the implementation procedure and illustrates its broad utility. The data offer a prototypical example of non-experimental evaluations of the average impact of job training programmes for the participant population.