Actual Ground Research Articles

Robust Optimal Damper Placement of Nonlinear Oil Dampers With Uncertainty Using Critical Double Impulse

A new robust method for optimal damper placement is presented for building structures under the critical double impulse. Oil dampers are treated here as representative supplemental dampers to control the seismic response of high-rise buildings. Such oil dampers usually obey a bi-linear force-velocity relation in controlling the maximum damping force through a relief mechanism to avoid the occurrence of excessive design forces in surrounding frames. The influence of uncertainty in characteristics of those bi-linear oil dampers on building structural safety is investigated. For the efficient evaluation of dynamic performance, the resonant critical double impulse is used as the base input instead of actual earthquake ground motions. Since the critical double impulse is determined to maximize the input energy to the objective building by changing the second impulse timing, uncertainties in input ground motions can be taken into account in a robust manner. To consider these various uncertainties, the robustness function based on the Info-Gap model is used in the robust optimization to assess structural performance variations caused by various uncertainties in the structural design phase. In this paper, a new innovative objective function in the robust optimal damper placement problem is proposed to enhance the robustness of structural performance under the variation of structural parameters by comparing the robustness function of the robust design with that of an ordinary optimal damper placement without considering uncertainties. Numerical examples of the robust optimal design of linear and bi-linear oil damper placements are shown for 10-story and 20-story planar building frame models. Structural performances of the robust optimal design to the conventional design earthquake ground motions are examined to investigate the validity of using the critical double impulse in the structural design under uncertainties.

Improved hybrid modeling method with input and output self-tuning for gas turbine engine

Gas-path model (GPM) plays a key role in the application of sensor fault tolerant control for the gas turbine engine (GTE). However, the modeling accuracy of traditional physics-based GPM is restricted by modeling inaccuracies and measurement uncertainty of sensors, etc., making fault-tolerant control of sensors difficult. In this paper, an improved input and output self-tuning hybrid modeling (IOSTHM) method is proposed for improving the GPM modeling accuracy of a dual shaft turbofan GTE. The proposed IOSTHM consists of an input self-tuning model (ISTM) and an output self-tuning model (OSTM). In the framework of ISTM, a fundamental traditional physics-based model (PBM) is constructed by using the component level modeling method firstly. Based on the PBM and the actual measurement of rotor speed of low-pressure shaft (N1) which has high measurement accuracy, the ISTM is conducted by tuning the measurement of fuel flow in real-time according to the deviation of N1 between engine and PBM, aiming to deal with the measurement uncertainty of fuel flow. Based on the ISTM, the IOSTHM is constructed by combining with an OSTM which is constituted by a bank of residual learning models to reduce the modeling inaccuracies further. The effectiveness of the proposed IOSTHM is evaluated with the verification of simulated flight data and actual ground test data. Both flight and ground verification results reveal that the proposed hybrid model IOSTHM achieves the best modeling performance when compared with other models such as traditional PBM, ISTM, and OSTM, it shows the superiority of this methodology.

Finding the remains of classical Bagan’s peri-urban support population: using ethnoarchaeological data to enhance archaeological excavation and interpretation

ABSTRACT Little is known about the lifeways of the commoner populations that supported the expansive pre-industrial cities of Southeast Asia. Archaeologically driven understandings are constrained by the fact that the architecture and much of the material culture utilized by ordinary citizens were made from perishable materials, and many living floors were also raised above the actual ground surface on piles. The challenges associated with searching for and interpreting these quotidian remains, once they are found, can be mitigated to some degree through the integration of ethnoarchaeological insights. This study outlines the results of detailed ethnoarchaeological investigations within ten traditional Myanmar villages located in proximity to the remains of ‘classical’ Bagan’s walled and moated royal city. We then explore how these findings have helped our excavation team recognize and interpret a range of residential remains associated with the ancient city’s peri-urban support population.

Rock joint scale effects in numerical modelling - Hinkley Point C, a case study

This paper presents the details of the approach to numerical modelling of the subvertical rock slopes resulting from the deep excavations associated with the foundations and basements for Hinkley Point C, Nuclear Power Station, in Somerset, UK. The ground model, which was based on an extensive ground investigation, indicated that the rock mass comprised thinly bedded mudstones and occasional limestones of the Blue Lias Formation. The structural geology of the site consisted of two major faults with five minor faults. These faulting resulted in highly fractured rocks with closely spaced discontinuities with unfavourable orientations and dip angles relative to the slope face. The design methodology for the project included Discrete Element Method (DEM) of numerical modelling using UDEC, a commercial software suitable for modelling fractured rock behaviour. As part of the development of the analytical model, a number of simplifications in terms of modelling, needed to be undertaken to enable development of a series of models that adequately reflected the likely ground conditions during excavation, but that could be run effectively under the usual constraints of a commercial project. One of such key simplifications relates to rock joint spacings. Modelling the rock joints spacings as measured on site in UDEC would result in extremely long computation time. It was necessary to scale up joint spacing in the model without compromising on the accuracy of the results. Series of sensitivity analyses were carried out to assess the effects of joint spacing on the slope stability analysis. From these analyses, an appropriate joint spacing scale up factor was derived. The output from the modelling included assessments of likely displacement of the rock slopes during excavation and as part of the construction works for the basements a programme of extensive monitoring was undertaken to verify the results of the modelling. The paper presents the ground model as interpreted from the ground investigation, the sensitivity analysis undertaken to help develop the approach for the analytical model and the results of the numerical modelling. It then goes on to compare these with the actual ground conditions revealed as excavation was undertaken, and the deformation recorded on the rock slopes during excavation. The paper goes on to present conclusions with respect to the modelling similar rock masses effectively using numerical modelling techniques.

Density matrix renormalization group boosted by Gutzwiller projected wave functions

We propose to boost the performance of the density matrix renormalization group (DMRG) in two dimensions by using Gutzwiller projected states as the initialization ansatz. When the Gutzwiller projected state is properly chosen, the notorious "local minimum" issue in DMRG can be circumvented and the precision of DMRG can be improved by orders of magnitude without extra computational cost. Moreover, this method allows to quantify the closeness of the initial Gutzwiller projected state and the final converged state after DMRG sweeps, thereby sheds light on whether the Gutzwiller ansatz captures the essential entanglement features of the actual ground state for a given Hamiltonian. The Kitaev honeycomb model has been exploited to demonstrate and benchmark this new method.

The Optimization Model of Composite Material UAV Shooting Flight Routes

UAV is one of the important achievements of scientific and technological innovation, which has been widely used in various fields of life in recent years. UAVs of composite materials are the most popular because the composite itself has designability, and can be optimized according to the strength and stiffness of the aircraft without changing the structural weight. In order to ensure the UAV to carry out more accurate image acquisition under the actual ground conditions, through the analysis of decision variables, this paper uses the linear weighting of image shooting range and pixel accuracy to establish the objective function, and establishes the optimal shooting angle under different constraints Considering the actual motion characteristics of objects such as automobiles, this paper assumes that the tracked ground targets are a class of motion objects with non-integrity constraints when the flight altitude of UAV is fixed and the flight speed is constant. The ground target model and the flight dynamics nonlinear model are established. This paper presents a new RRT* algorithm for path planning based on the obstacles encountered by UAV during flight, that is, using the cost function to select the node with the minimum cost in the field of expanding nodes as the parent node, using the MATLAB to select a reasonable obstacle avoidance strategy, the global optimal route is obtained by smoothing processing, and the data simulation test is carried out. The experimental results show that the model is robust.

Validation of the EDGES Low-band Antenna Beam Model

Abstract The response of the antenna is a source of uncertainty in measurements with the Experiment to Detect the Global Epoch of Reionization Signature (EDGES). We aim to validate the electromagnetic beam model of the low-band (50–100 MHz) dipole antenna with comparisons between models and against data. We find that simulations of a simplified model of the antenna over an infinite perfectly conducting ground plane are, with one exception, robust to changes in the numerical electromagnetic solver code or algorithm. For simulations of the antenna with the actual finite ground plane and realistic soil properties, we find that two out of three numerical solvers agree well. Applying our analysis pipeline to a simulated drift-scan observation from an early EDGES low-band instrument that had a 10 m × 10 m ground plane, we find residual levels after fitting and removing a five-term foreground model from the simulated data binned in local sidereal time (LST) average about 250 mK with ±40 mK variation between numerical solvers. A similar analysis of the primary 30 m × 30 m sawtooth ground plane reduced the LST-averaged residuals to about 90 mK with ±10 mK between the two viable solvers. More broadly we show that larger ground planes generally perform better than smaller ground planes. Simulated data have a power that is within 4% of real observations, a limitation of net accuracy of the sky and beam models. We observe that residual spectral structures after foreground model fits match qualitatively between simulated data and observations, suggesting that the frequency dependence of the beam is reasonably represented by the models. We find that a soil conductivity of 0.02 S m−1 and relative permittivity of 3.5 yield good agreement between simulated spectra and observations. This is consistent with the soil properties reported by Sutinjo et al. for the Murchison Radio-astronomy Observatory, where EDGES is located.

A GROUND MOTION SYNTHESIS METHOD FOR MATCHING MULTI-OBJECTIVE PARAMETERS

Ground motion synthesis methods provide input time histories for structural seismic design and performance evaluation. The existing synthesis methods can only match the target spectrum to consider the frequency spectrum and amplitude of the ground motion, but cannot consider the duration characteristics. To synthesize ground motions which matches the three elements of ground motions for a specific area, a method that incorporates machine learning is proposed. In this method, the data-driven principal component analysis is used to extract the characteristic mother waves from the actual ground motion database of the target area. The target response spectrum and duration are obtained by the ground motion prediction equation of the region. The multi-objective genetic algorithm is applied to solve the linear combination coefficient of the mother waves, so that the combined ground motion can match the preset error standard and the multi-objective parameters of the regional ground motion. The method is verified for the 2019 Ms 6.0 Changning earthquake in Sichuan Province. The results show that, because the proposed method is driven by the actual ground motion data in the target area, the synthetic ground motion matches the demand of the target spectrum and the duration characteristics of regional ground motions, which can provide more reasonable ground motion inputs for seismic analyses considering regional seismic characteristics.

Quasi-static numerical simulation of the dynamic launching process for high-altitude balloon

In this paper, by establishing a set of mathematical models and computing methods, the numerical simulation of the ground dynamic launching process under the quasi-static assumption for a high-altitude balloon system is conducted to reveal the rich mechanical and geometric behaviors involved in it. The mathematical model includes the solution of the bubble shapes and the kinematic analysis, as well as the static model of the “membrane bundle system”. Considering the middle and lower region of the bubble still meet the zero circumferential stress condition under the hydrostatic pressure gradient, the upper fully expanded shape generatrix is the same as the balloon generatrix of the ceiling height. The multiple shooting method combined with Sequential Quadratic Programming (SQP) is used to solve the lower partially expanded bubble generatrix. Based on the force balance equations of the microelement in “membrane bundle system”, and considering the friction force in contact with the horizontal ground and the characteristics that the membrane bundle is a variable mass system along the arc length direction, the “multiple shooting method + SQP” method is still used to solve the shape and internal force of the “membrane bundle system” under two end geometric boundary conditions. In co-simulation, taking the dynamic launching process of the zero-pressure balloon system with the ceiling flight height of 30 km and hanging a load of 300 kg as the object, under the conditions of 1.5 m/s and 4.5 m/s wind speeds, the initial layout and the rising process of dynamic launching is calculated. Through the analyses of the simulation results, four stages of the rising process are concluded, namely, the stage of the “membrane bundle system” relaxation and falling to the ground, the longitudinal rising stage, the main rising stage, and the straightening stage. The geometry and force characteristics of the bubble and the “membrane bundle system” are discussed to provide a reference for the simulation of the actual high-altitude balloon ground dynamic launching testing.

Terrain Slope Effect on Forest Height and Wood Volume Estimation from GEDI Data

The Global Ecosystem Dynamics Investigation LiDAR (GEDI) is a new full waveform (FW) based LiDAR system that presents a new opportunity for the observation of forest structures globally. The backscattered GEDI signals, as all FW systems, are distorted by topographic conditions within their footprint, leading to uncertainties on the measured forest variables. In this study, we explore how well several approaches based on waveform metrics and ancillary digital elevation model (DEM) data perform on the estimation of stand dominant heights (Hdom) and wood volume (V) across different sites of Eucalyptus plantations with varying terrain slopes. In total, five models were assessed on their ability to estimate Hdom and four models for V. Results showed that the models using the GEDI metrics, such as the height at different energy quantiles with terrain data from the shuttle radar topography mission’s (SRTM) digital elevation model (DEM) were still dependent on the topographic slope. For Hdom, an RMSE increase of 14% was observed for data acquired over slopes higher than 20% in comparison to slopes between 10 and 20%. For V, a 74% increase in RMSE was reported between GEDI data acquired over slopes between 0–10% and those acquired over slopes higher than 10%. Next, a model relying on the height at different energy quantiles of the entire waveform (HTn) and the height at different energy quartiles of the bare ground waveform (HGn) was assessed. Two sets of the HGn metrics were generated, the first one was obtained using a simulated waveform representing the echo from a bare ground, while the second one relied on the actual ground return from the waveform by means of Gaussian fitting. Results showed that both the simulated and fitted models provide the most accurate estimates of Hdom and V for all slope ranges. The simulation-based model showed an RMSE that ranged between 1.39 and 1.66 m (between 26.76 and 39.26 m3·ha−1 for V) while the fitting-based method showed an RMSE that ranged between 1.26 and 1.34 m (between 26.78 and 36.29 m3·ha−1 for V). Moreover, the dependency of the GEDI metrics on slopes was greatly reduced using the two sets of metrics. As a conclusion, the effect of slopes on the 25-m GEDI footprints is rather low as the estimation on canopy heights from uncorrected waveforms degraded by a maximum of 1 m for slopes between 20 and 45%. Concerning the wood volume estimation, the effect of slopes was more pronounced, and a degradation on the accuracy (increased RMSE) of a maximum of 20 m3·ha−1 was observed for slopes between 20 and 45%.

A numerical investigation to analyze the effect of changing ambient conditions on accurate and stable identification of thermal conductivity of ground source heat pump system using thermal response test

This study aimed to investigate the effect of changing ambient conditions on the accuracy and stability of ground thermal conductivity, λs, using the thermal response test (TRT). First, a detailed numerical model considering the heat fluxes on the field surface of TRT was developed. Then, the numerical model was compared with that with the adiabatic surface boundary in two representative test seasons. After that, the impacts of three parameters associated with the surface heat fluxes, including the surface absorptivity, evaporation coefficient, and surface infrared emissivity on the λs were analyzed. Lastly, statistical analyses with various ambient conditions were employed to evaluate the degree of estimated λs deviating from its actual value in different specifications of TRT and actual ground thermal conductivities. Results show that the surface absorptivity can be generally seen as the most significant contributory factor to the uncertainty of λs. The λs is overestimated and the uncertainty of TRT is aggravated by considering the changing ambient conditions, especially when the depth of drilling test well is short and the actual λs is high. For such conditions, the heat insulation on the test site surface is highly recommended considering the acceptable accuracy of λs with the adiabatic boundary.

Deep-Learning Based Recognition of Mask Wearers for a Droplet Infection Prevention and Monitoring System

The primary method to prevent virus infection through droplets is to check if individuals wear a mask. However, inspecting and controlling each individual involves challenges. This study suggests a method to distinguish those who wear a mask from those who do not and to issue a warning alarm when detecting a person whose access is restricted. With data sets generated in the preprocessing steps, those who do not wear a mask are distinguished from mask wearers in application of the deep-learning model. Individuals are counted for image analysis, and alarms are issued upon an event. In order to secure a high level of reliability on warning alarms, the actual ground truth (GT) was compared with the accuracy rate of recognizing mask wearers and non-wearers based on images that the learning model acquired. As a result, the accuracy of mask wearer recognition based on test data sets was 95.3%, and that of non-wearer recognition was 94.5%. With the number and percentage of non-wearers checked out of the entire subjects, it turned out that based on detection results, warning alarms were issued correctly. In conclusion, it has been verified that the method suggested in this study is applicable to an access control system judging situations based on images and issuing warning alarms accordingly.

A grammatical construction in the service of interpersonal distance regulation. The case of the Polish directive infinitive construction

Abstract The aim of the paper is to consider the pragmatic effects of the Polish (Proszę) ‘request1 SG. NON-PAST.’ + VINF construction in different contexts. The specific research problem is how these effects are related to the conceptual make-up of the construction. The framework for the analysis is the theory of cognitive grammar (cf. e. g. Langacker 1987, 1991, 2008, 2009). The analysis starts with an account of the conceptual make-up of the construction. Then, its selected uses are considered, with emphasis on the pragmatic effects in the relevant contexts. The study offers a qualitative analysis of two kinds of data: a sample of hand-picked utterances and a corpus of utterances extracted from the National Corpus of Polish (NCP). The claim made in the study is that the construction profiles a process figuring in a directive scenario in the role of the process the speaker wishes the hearer to engage in. At the same time, it involves defocusing of the trajector of the profiled process, identified with the hearer. The construction’s pragmatic effects in specific contexts are claimed to follow from how this trajector defocusing is put in correspondence with specific aspects of the actual ground.

Comparison of performance of single- and double-core prefabricated vertical drains for thick reclaimed ground improvement

The performance of a double-core prefabricated vertical drain (PVD), which could reduce the well resistance via its large cross-sectional area and structural stability, was investigated experimentally and numerically. The double-core and the conventional single-core PVD, which has been widely adopted in reclaimed ground improvement, were compared in this study. In the experimental investigation, modified Delft tests and a calibration chamber test were carried out in a laboratory to determine the discharge capacities of the PVDs. Thereafter, single- and double-core PVDs were installed in the Busan New Port site, which had a 40 m thickness of clay deposit, and the performances of the PVDs estimated through surface settlements were compared. In addition, a numerical program named ILLICON was applied with field measurements, and a parametric study was conducted for different discharge capacities of the PVDs. The result of the laboratory tests and field measurements indicated that there was no difference in the performance between the two types of PVDs for improving the soft ground, considering actual ground conditions. Similarly, the parametric study results also implied that both PVDs could be satisfactorily applied in thick reclaimed ground, and there is no significant difference in the performance of single- and double-core PVDs.

3D-3D Self-Calibration of Sensors Using Point Cloud Data

<div class="section abstract"><div class="htmlview paragraph">Self-calibration of sensors has become highly essential in the era of self-driving cars. Reducing the sensors’ errors increases the reliability of the decisions made by the autonomous systems. Various methods are currently under investigation but the traditional methods still prevail which maintain a strong dependency on human experts and expensive equipment that consume significant amounts of labor and time. Recently, various calibration techniques proposed for extrinsic calibration for Autonomous Vehicles (AVs) mostly rely on the camera 2D images and depth map to calibrate the 3D LiDAR points. While most methods work with the whole frame, some methods use the objects in the frame to perform the calibration. To the best of our knowledge, majority of these self-calibration methods rely on using actual or falsified ground truth values.</div><div class="htmlview paragraph">We propose a 3D-3D point cloud based continuous self-calibration approach that uses one or many objects identified in the sensor frames to cross-calibrate sensors without any reliance on initial calibration parameters or ground truth values. Considering the fact that multiple sensors have multiple views of the same scene, the common features or objects within the scene can be used to calibrate one sensor node with respect to the other. Our approach relies on point cloud data (PCD) generated from at least two sensors to cross-calibrate the mis-calibrated sensor with respect to the calibrated sensor. In this paper, we demonstrate that we can apply the method either by extracting the essential feature points of the object PCD (i.e., centroids of the objects) or the whole object PCD. Following which we optimize the cost function to obtain the extrinsic calibration parameters. Our method handles the problem of pose correction similar to calibration without any ground truth value from sensors just by using multiple consecutive frames. In comparison to other methods, our method performs calibration with low margins of errors in rotation and translation. This method has been tested on the publicly available KITTI dataset, which is widely used to assess various problems (object detection, segmentation, tracking, depth prediction etc.) related to self-driving cars. The simplicity of the method lets us do on-the-fly calibration of sensors with a high level of accuracy.</div></div>

Toward Atmospheric Electricity Research: A Low-Cost, Highly Sensitive and Robust Balloon-Borne Electric Field Sounding Sensor

High-altitude electric fields are a critical parameter of electricity in the atmosphere. Due to such drawbacks as their bulk and high cost, traditional electric field mills are not available for daily atmospheric sounding purposes. This paper introduces a highly sensitive and robust electric field sounding sensor based on an existent micro-electro-mechanical systems chip and a new protective package that augments sensor sensitivity. To gain high sensitivity, an innovative external metal electrode connects to the electric field microchip's package cap, hangs down out of the guardian foam, and effectively enhances the local electric field near the microchip due to the distortion effect. This distortion regularity was studied during a series of finite element simulations, calibrations, and comparison experiments. With a 1.5-meter-long external electrode, the sensitivity of the sensor reached 465 mV/kV/m with a resolution that was better than 10 V/m in an actual long-term ground comparison test. Furthermore, the microsensor chip and its circuit are protected inside a foam while the metal electrode is exposed outside, therefore gaining a robust stability during harsh environments such as low temperature, rainfall, etc. To examine the performances, we built an electric field sounding system, including the electric field sensor, a radiosonde, and a receiving antenna, and measured high-altitude electric fields in various weather conditions. Intended for atmospheric electricity research, our innovative electric field sounding sensor would be especially propitious if integrated with a radiosonde and applicable to daily sounding services.

Density based semi-automatic labeling on multi-feature representations for ground truth generation: Application to handwritten character recognition

A huge number of labeled samples are required as training data to construct an efficient recognition mechanism for an optical character recognition system. Although samples of characters can be easily collected from available manuscripts, they often lack class labels, especially for ancient and local alphabets. The creation of a training dataset requires a great number of characters manually annotated by experts. It is a costly and time-consuming process. To considerably reduce the human effort required in the construction of training datasets, a novel semi-automatic labeling method is proposed in this work under the assumption that there are no initial labeled samples. The proposed method performs an iterative procedure on a nearest neighbor graph that views samples in multiple feature spaces. In each iteration, an expert is first called upon to label a relevant unlabeled sample that is automatically selected from the highest density area of unlabeled samples. Then, the manually annotated label is propagated to the neighbor samples with safe conditions based on sample density and multi-views. The procedure is repeated until all unlabeled samples are labeled. The labeling procedure of the proposed method is evaluated on MNIST, Devanagari, Thai, and Lanna Dhamma datasets. The results show that the proposed method outperforms state-of-the-art labeling methods, achieving the highest labeling accuracy. In addition, it can handle outlier samples and deal with alphabets that include visually similar characters. Moreover, the recognition performance of the classifier trained by using the semi-automatically generated training dataset is comparable with that classifier trained by actual ground truth.

Landslide Susceptibility Modeling Using a Hybrid Bivariate Statistical and Expert Consultation Approach in Canada Hill, Sarawak, Malaysia

Landslide susceptibility assessment was conducted in Canada Hill, Sarawak, Malaysia through a combined bivariate statistics and expert consultation approach using geographical information system, which captures landslide-conditioning parameters specific to the study area; to ensure its usefulness in practice. Over the past four decades, many landslide parameters and increasingly sophisticated statistical methods have been used in landslide research. However, the findings have had very limited use in practice as the actual ground conditions are not well represented. The weakness is due to poor quality of data in landslide inventories and inadequate understanding of landslide-conditioning parameters. In this study, bivariate statistical method was used in conjunction with an iterative process of expert consultation. Thirteen original landslide-conditioning parameters were narrowed down to six, with the addition of a unique parameter, planar failure potential, which was selected based on expert input. The parameter captures planar failure landslides, which has the highest impact in the study area, causing loss of lives and property destruction. The inaugural landslide susceptibility map for the study area has five classes; very low, low, moderate, high and very high susceptibility. All major planar failures and most smaller circular failures fall within the very high susceptibility class, with a success rate of 75.8%. The approach used in this study has improved the quality of the landslide inventory and delineated key conditioning parameters. The resultant map captures local conditions, which is useful for landslide management.

Neural network prediction of slurry erosion of heavy-duty pump impeller/casing materials 18Cr-8Ni, 16Cr-10Ni-2Mo, super duplex 24Cr-6Ni-3Mo-N, and grey cast iron

The aim of the present study is to develop a neural network model for the prediction of slurry erosion (SE) of heavy-duty pump impeller steels and casing material. The heavy-duty pump impeller has a wide range of applications like slurry transportation system and coal conveying system of thermal power stations, and various other industries like mining, chemical and marine industry. In the present study, the slurry erosion performance of three different pump impeller steels namely 18Cr-8Ni, 16Cr-10Ni-2Mo and super duplex 24Cr-6Ni-3Mo-N, and one pump casing material namely Grey cast iron was tested. The SE experiments were carried out on a laboratory scale pot tester using the sand as an erodent. A data set was produced to develop a prediction-based neural network (NN) model and 70% of this data set was used as input to NN model. The learning of NN model was based on an artificial neural network (ANN) and used to build a prediction model that can predict the results while the input data was supplied to it. Firstly, the data were divided into training, validation, and testing. The NN model was trained on 70% of the original dataset and validated by another 15% data. A total of 30 training epochs were performed for training, testing, and validation of the model. The validation of the model indicates that the build NN model was the best fit and comprises no over-fitting and under-fitting issues. In the end, the prepared NN model was given the remaining 15% data for testing. It outputs the corresponding values for each input. These predicted values are then compared with the actual ground truth to check the robustness of the designed model. The various measures used for evaluating the model were R2 (coefficient of determination), Root mean square error, etc. Results show that 18Cr-8Ni steel exhibits the best SE performance followed by 16Cr-10Ni-2Mo, 24Cr-6Ni-3Mo-N, and Grey cast iron.

Non-deterministic model validation methodology for simulation-based safety assessment of automated vehicles

Safeguarding and type approval of automated vehicles is a key enabler for their market launch in our complex traffic environment. Scenario-based testing by means of computer simulation is becoming increasingly important to cope with the enormous complexity and effort. However, there is a huge gap when assessing the safety of the virtual vehicle while the real vehicle will drive on the road. Simulation must be accompanied by model validation to ensure its credibility since errors and uncertainties are inherent in every model. Unfortunately, this is rarely addressed in the current literature. In this paper, a modular process is presented covering both model validation and safeguarding. It is characterized by the fact that it quantifies a large number of errors and uncertainties, represents them in the form of an error model, and ultimately integrates them into the safeguarding results. It is applied to a type-approval regulation for the lane-keeping behavior of a vehicle under various scenario conditions. The paper contains a thorough validation of the methodology itself by comparing its results with actual ground truth values. For this comparison, a binary classifier and confusion matrices are used that relate the binary type-approval decisions. The classifier demonstrates that the methodology of this paper identifies a systematic error of the simulation model across several safeguarding scenarios. Finally, the paper provides recommendations for alternative configurations of the modular methodology depending on different requirements.