Inverse Analysis Method Research Articles

An inverse analysis method for determining tensile softening relationship of concrete considering local response

This study presents an inverse analysis algorithm for deriving the softening relationship of concrete. The algorithm can not only reproduce the influence of local response on the σ-w curve by introducing additional load criteria but also reduce the dependence of optimization results on the initial guesses by combining global best-fitting with the automatic parameter updating technique, improving the prediction accuracy and lowing the possibility of obtaining pseudo-solutions. Then, the fracture tests were carried out on the wedge splitting specimens. The validity and versatility of the algorithm were verified using experimental data. The findings show that the simulation responses agree well with the test ones in all regions of the load-CMOD curve, and the values of fracture energy determined by the load-CMOD and σ-w curves are consistent. The derived σ-w curve is independent of the initial guesses of parameters. The widely used bi-linear σ-w curve tends to overestimate the load in the post-peak of load-CMOD, and the tri-linear model is recommended after considering the accuracy, computation time, and convergence. The tensile strength evaluated by the inverse analysis method is 75% of the splitting tension test and 91% of the fracture theory method. Further, the proposed algorithm exhibits outstanding versatility, applying to various concrete materials and specimen configurations.

Application of spherical macro-indentation for determination of plastic anisotropy and residual stresses using indentation geometry and inverse analysis

The simultaneous determination of mechanical properties and residual stresses using the instrumented indentation technique is more challenging when the anisotropic behaviour of a material is considered. If ignored, it may lead to inaccurate estimation of residual stresses and mechanical properties of materials. In this paper, the existence of in-plane anisotropic plasticity and the equi-biaxial residual stresses were considered in materials simultaneously. These anisotropic plastic properties and residual stresses were extracted for two materials, an aluminium alloy and a stainless steel, using an inverse analysis method and macro-spherical indentation test. The considered anisotropic materials have different yield stresses in both the longitudinal and transverse directions of the part. The inverse analysis method was based on a wide range of finite element (FE) simulations, which included a large number of models covering the properties of different steel and aluminium materials. In this method, the parameters extracted from the load-displacement (P-h) curve for indentation, along with the pile-up around the indentation area after unloading, were considered as the input parameters of the inverse analysis method. The outputs were the mechanical properties of the anisotropic material and the equi-biaxial tensile and compressive residual stresses. The proposed inverse method was based on the analysis of the artificial neural network (ANN). The effectiveness of the inverse method was examined and verified using experimental data which were found to be reliable. The method employed the P-h curve parameters and unloading morphology and was successful to determine the anisotropic plastic properties and residual stresses.

Locating leakage in pipelines based on the adjoint equation of inversion modeling

This paper presents an adjoint method for locating potential leakage in a single-phase fluid pipeline based on the analytic solution of inversion modeling. By studying the mechanism of pipeline leakage pressure, the adjoint equation based on the governing equation of transient flow is established in the single-liquid phase aspect using inverse adjoint theory and sensitivity analysis method. The inverse transient adjoint equation is primarily derived from the single linear fluid pipeline in the semi-infinite domain. The Laplace method is then used to obtain an analytical solution that determines the location of pipeline leakage. The experimental results indicate that the analytic solution can quickly and accurately judge the leakage location of the pipeline. Furthermore, it presents a new approach to engineering applications, such as gas-liquid two-phase flow complex pipe networks, etc.

Inversion Analysis Method for Tunnel and Underground Space Engineering: A Short Review

With the rise of the fourth industrial revolution, traditional methods of analyzing investment have been transformed into intelligent methods under big data and the Internet of Things. This has created a new approach to solving practical engineering problems. This paper examines the formation and evolution of the application of inversion theory in tunnel and underground engineering, summarizing research progress using traditional and intelligent inversion analysis methods to identify three types of target unknown quantities in tunnels and underground projects: initial ground stress, support structure load, and tunnel characteristic parameters. It also offers an outlook on how to optimize inversion analysis methods to solve more challenging and complex tunneling problems in the context of informatization, digitalization, and intelligence. In the current research process of tunnel and underground space engineering problems, the inversion theory system has been improved, but inversion analysis methods still face many challenges. These include the low reliability of initial ground stress inversion under complex geological conditions, the lack of indicators to objectively evaluate the accuracy of inversion analysis, and the high costs of intelligent inversion analysis means. Moving forward in the context of big data and the information era, the future development direction for inversion theory and inversion methods in tunnel and underground space engineering is to combine new monitoring technology, computer vision technology, and simulation analysis technology to establish multifaceted intelligent inversion analysis models.

Structural identification of super high arch dams using Gaussian process regression with improved salp swarm algorithm

Structural identification is critical for evaluating the operation of high arch dams, and has become an important topic. This paper proposes a novel inverse analysis framework combining an improved salp swarm algorithm and Gaussian process regression to identify the material parameters of concrete dams. First, the hydrostatic component is separated from the prototype measured data using the multiple linear regression method. Subsequently, the Gaussian process regression is adopted to build the mapping relationship between elastic modulus and the calculated relative hydrostatic component of the dam. Finally, the improved salp swarm algorithm is used to estimate the real elastic modulus of the dam by minimizing the discrepancy between the measured and calculated relative hydrostatic components. The performance of the proposed method is verified on a real concrete arch dam with sufficient monitoring data. Results show that the proposed inverse analysis method can identify the material parameters accurately and efficiently.

Unified inverse isogeometric analysis and distributed fiber optic strain sensing for monitoring structure deformation and stress

Inverse analysis is an efficient and reliable approach to the estimation of structural state such as deformation and stress distribution from measured real-world data and numerical algorithms. In this work, a new method of inverse analysis is proposed which integrates Isogeometric Analysis (IGA) and distributed fiber optic sensing (DFOS) using a framework based on non-uniform rational B-spline (NURBS) functions. This formulation expands on the existing inverse Finite Element Methods (iFEM) by developing a new approach to use the exact geometry of strain-sensing optical fibers. The constraint equations of theoretical and measured strains are derived using a discrete least-squares functional and then coupled with isogeometric finite element equations to provide regularization for ill-posed inverse problems. The explicit strain fiber model not only separates the structure mesh and strain sensing locations but also can adapt to the sensing parameters such as spatial resolution of DFOS instrument, which is not yet possible with the current models. The proposed method is validated with four numerical examples using plate, hemisphere and cylindrical geometries under prescribed force or displacement boundary conditions. Furthermore, the sensitivity of the inverse solution to the sampling interval and measurement noise is studied. The unified inverse IGA and DFOS is tested under simulated measurement noise up to 30 micro-strains with strain sampling intervals ranging from 10 mm to 1 m, and validated with the comparisons of reference and inverse solutions.

Improved inverse analysis methods and modified apparent earth pressure for braced excavations in soft clay

Inverse analysis procedures are used to update project parameters and applied to predict future performance in the later stages. However, the inverse analysis procedure of earth pressure of excavation has received less attention. This paper proposes an improved inverse analysis method and the modified apparent earth pressure model for braced excavations in soft clay. A new procedure for inverse analysis of excavation is suggested based on the quintic spline interpolation function, and a trial algorithm is used to eliminate the errors generated in the inverse process. The proposed inverse solutions are validated by numerical simulations and reported engineering examples and show good agreements. Based on the inverse data and empirical apparent pressure diagram (APD), a modified apparent earth pressure method for braced excavations in soft clay is further suggested. And the relationship between the degree of anisotropy of soft clay and apparent earth pressure coefficients is discussed. The study results provide a reference for dynamic feedback design and adaptive management of the excavation.

Automatic recognition of slide mass and inversion analysis of landslide based on discrete element method

The slip surface is an important structure for analyzing the landslide mechanism, and it will be deformed by the traction and shear of the slide mass. For the landslide accident without artificial monitoring, if the slip surface can be restored according to the slope morphology before and after the landslide, information such as the safety factor before the damage to the slope and the volume of the slide mass can be obtained by the position of slip surface, which helps engineers to analyze the cause of the landslide. In this paper, an automatic slide mass identification and landslide inversion analysis method based on discrete element simulation is proposed. The method combines Gaussian Mixture Model (GMM), alpha shape algorithm, discrete element simulation, and the morphology of the slope before and after the landslide to invert the slip surface of the slope. This method is suitable for two and three dimensional slope models. In addition, the effect of the slope angle on the deformation of the slip surface is analyzed.

The Parameter Identification of Physical-Based Constitutive Model by Inverse Analysis Method for Application in Near-Net Shape Forging of Aluminum Wheels

A reliable constitutive model is a prerequisite to simulate a new complex forming technique, which is represented by the near-net shape forging process of aluminum wheels in this study. The aim of the present work was to identify the physical-based constitutive model parameters of Al-Zn-Mg alloy via the inverse analysis method based on experimental data and numerical analysis: the stress–strain curves at different temperatures and strain rates were obtained based on hot compression tests. On the basis of the shape of the compressed specimens and experimental force–displacement data, the friction coefficients and the optimized physical-based constitutive model were determined by using two-times inverse analysis techniques. Results showed that the global average error between the predicted and experimental force–displacement curves was only 3.8%. Then, thermo-mechanical finite element models were built in the Deform-3D software to simulate the two-stage forging processes of the near-net shape forging of aluminum alloy wheels, and the results showed that the predicted load–stroke curves were in good agreement with the experimental ones in all forging stages, which verified the prediction accuracy of the optimized physical-based constitutive model. In addition, the identification of the physical-based constitutive model parameters by the inverse analysis method provides a theoretical basis for formulating and optimizing the near-net shape forging process parameters of aluminum alloy wheels.

Combining machine learning and physics modelling to determine the natural cave property with fracturing curves

Currently, it is still challenging to determine the geological parameters of natural caves in deep reservoirs accurately. By inheriting the advantages of Machine Learning (ML) method and physics modelling, a novel ML-Physics method is developed to determine the geological parameters of natural caves based on the data mining of fracturing curves obtained during Hydraulic Fracturing (HF) operation. The computational time of ML-Physics method is divided into two stages, preparation-stage and operation-stage. The preparation-stage happens before HF operation, therefore there is no limitation to the computational time. During this preparation-stage, the implicit relationship between cave property and fracturing curve is generated using ML, which usually fails to ensure the accuracy under different geological and operational conditions. The operation-stage happens during HF operation, in which the computational time is limited because the geological parameters of natural caves are required to be determined in real time. During this operation-stage, the physical modelling based inverse analysis method is carried out, in which the initial value is chosen based on the ML results obtained in preparation-stage. Results show that, with the same target error, the iteration step of ML-Physics method (1 iteration) is much less than that of traditional inverse analysis method (5 iterations). After the same iterations, the error of fracturing curve using the traditional inverse analysis method is 0.40%, while the error using ML-Physics method is 0.02%. Meanwhile, the error of permeability using the traditional ML is up to 10.33%, while the error of ML-Physics method is 0.29%. The present ML-Physics method is potentially useful to optimize the HF design based on the data mining of fracturing curves in real time.

Identification of abnormal loads between carbody and hanging equipment of high speed train using inverse method

ABSTRACT The ability to effectively identify abnormal loads caused by hanging equipment local resonance problems is important for assessing the structural integrity of railway vehicles. The structural dynamics inverse problems analysis method is presented to minimise resonance effects and equipment-underframe interactions at different structural frequencies. A mathematical model of the structure forward and inverse dynamics is developed to simulate such interactions based on multibody system dynamics and finite elements theory. The Euler-Bernoulli beam model is also used to simulate the flexible carbody integrated with the inverse approach to compare the effects of reconstructed loads utilised by the optimised regularisation algorithm. The vehicle rolling vibration rig test is made to verify the identification effects. The results are found to be in good agreement with the predictions of the mathematical model using the measured acceleration and demonstrated the effectiveness and non-destructive test applicability.

Dynamics analysis of the anterior cruciate ligament reconstruction surgery based on magnetic resonance imaging

ABSTRACT In clinical practice, anterior cruciate ligament (ACL) rupture is always repaired by the single-beam reconstruction method. Before the surgery, the surgeon made the diagnosis based on medical images, such as CT (computerized tomography) and MR (magnetic resonance) images. However, little is known about how biomechanics governs the biological nature for femoral tunnel position. In the present study, three volunteers’ motion trails were captured by six cameras when they were doing squat movement. The medical image can reconstruct the structure of the ligaments and bones and a left knee model was reconstructed by MIMICS by MRI data of DICOM format. Finally, the effects of different femoral tunnel positions on ACL biomechanics were characterized by the inverse dynamic analysis method. The results showed that there were significant differences in the direct mechanical effects of the anterior cruciate ligament at different locations of the femoral tunnel (p < 0.05), the peak stress of ACL in the low tension area was 1097.24 ± 25.55 N, and the peak stress of ACL in the distal femur was 356.81 ± 15.39 N, both of which were much higher than that in the direct fiber area (118.78 ± 20.68 N);

Characterization of the Nozzle Ablation Rate Based on 3D Laser Scanning System

The nozzle of solid rocket motor (SRM) is easily ablated by high temperature, high pressure, high speed, and corrosive particles, which affects the stability of rocket flight. Therefore, the measurement and characterization of the nozzle ablation rate are helpful in providing some guidance for the design of nozzle material and structure. However, due to the high surface roughness of the composite nozzle after ablation, it is difficult to obtain an accurate ablation rate by contact measurement methods. The 3D laser scanning system is a 3D non-contact measurement technology using structured light technology, phase measurement technology, and computer vision technology. It has the advantages of non-contact, large scanning size, flexibility, and portability. In this paper, a 3D reconstruction of the ablation nozzle is carried out based on the 3D laser scanning system. Additionally, the ablation rate of the nozzle is measured without cutting the actual specimen. Furthermore, the pressure, temperature, and surface convective heat transfer coefficient trends are numerically calculated and compared with the ablation rate. Additionally, the empirical formula between ablation rate and pressure, temperature, convective heat transfer coefficient is obtained empirically by the inversion analysis method. The empirical formula can provide theoretical guidance for nozzle size design and optimization. The results show that the non-contact 3D laser scanning system is a valuable method for reconstructing the model of the ablated nozzle, and the empirical formula of ablation rate can accurately predict the ablation rate of the nozzle.

Inverse Heat Transfer Analysis Method to Determine the Entropic Coefficient of Reversible Heat in Lithium-Ion Battery

An entropic coefficient of reversible entropic heat is a key parameter in determining the battery thermal responses, but its measurement is challenging due to time consuming and inaccurate traditional methods. In this regard, an analytical approach based on the inverse heat transfer problem is newly proposed to precisely determine the entropic coefficient with low experiment cost. Experiments are conducted by discharging the battery under four different current rates to inversely estimate the entropic coefficients, and the least squares regression are conducted to optimize the derived entropic coefficients. Through the comparison with the existing potentiometric method, the experimental time can be reduced by 93.7%. Furthermore, the accuracy of the proposed method is well verified by validating within the root mean square error of 0.848°C by comparing with the experimental results. Through the validation processes under various operating conditions, such as low to high current rates, charging process, dynamic loads, and different ambient temperatures, the proposed method is proven over temperatures ranging from 10°C to 60°C. Conclusively, the proposed method can be a great alternative to replace the classical experimental methods.

Estimating energy savings from HVAC controls fault correction through inverse greybox model-based virtual metering

Fault impact analysis is an important phase of fault detection and diagnosis (FDD), enabling prioritization of the faults detected and allocation of resources to procure services and materials. However, many existing fault impact analysis workflows rely on building performance simulation (BPS) models with high engineering cost, limiting their use in FDD software. To this end, this paper introduces an inverse greybox model-based fault impact analysis method using building automation system (BAS) trend data from variable air volume (VAV) air handling unit (AHU) systems to quantify the heating and cooling energy impact of control system faults. It trains inverse models of a VAV AHU system by employing a sequential parameter estimation algorithm, formulates virtual meters quantifying the sensible heat transfer rate from AHU heating and cooling coils and the perimeter heating devices, and employs the virtual meters to estimate heating and cooling energy use of an existing VAV AHU system and the same system after fault correction. The method was used with a synthetic BAS dataset generated by using an EnergyPlus model of a 26-zone VAV AHU system in Ottawa, Canada. The results indicate that the virtual meters could estimate the monthly energy use for heating and cooling at a coefficient of variation of the root mean squared error (CV(RMSE)) of 4–6%. The method was demonstrated with measured BAS data from a 72-zone VAV AHU system, revealing an energy savings potential of 43% for heating and 22% for cooling through fault correction.

Study on crack identification for pipe with through-wall crack by cuckoo optimization algorithm

In recent years, the aging of power plants and other infrastructure facilities has become an issue. Therefore, a health-monitoring technology has attracted attention in order to ensure the integrity of structures. An inverse analysis is conducted to estimate the size and position of cracks in this technology. The accuracy of the inverse analysis is dependent on the form of the objective function and the optimization calculation method. Generally, optimization calculation methods using the gradient of the objective function is adopted. However, it is known to have the drawback that the search solution strongly depends on the initial value setting. The biomimetics-based cuckoo optimization algorithm (COA) is a method that can overcome this drawback. However, there are few examples of estimating the crack size and position in structures using the COA. In this study, an inverse analysis method is developed to estimate the crack angle and position of circumferential through-wall crack in pipes. In this study, transfer matrix method (TMM) was utilized to numerically calculate the deformation and rotation angle of the pipe, which are required in the inverse analysis. For this purpose, the fundamental theory of the TMM was extended to the element with a through-wall crack. Consequently, it was confirmed that the crack angle, position and magnitude of the applied shear load can be accurately estimated by the developed method.

Using Artificial Neural Networks to Couple Satellite C-Band Synthetic Aperture Radar Interferometry and Alpine3D Numerical Model for the Estimation of Snow Cover Extent, Height, and Density

This work presents a new approach for the estimation of snow extent, height and density in complex orography regions, which combines differential interferometric synthetic-aperture-radar (DInSAR) data and snowpack numerical model data through artificial neural networks (ANNs). The estimation method, subdivided into classification and estimation, is based on two artificial neural networks trained by a DInSAR response model coupled with Alpine3D snow cover numerical model outputs. Auxiliary satellite training data from satellite visible-infrared MODIS imager as well as digital elevation and land cover models are used to discriminate wet and dry snow areas. For snow cover classification the ANN-based estimation methodology is combined with fuzzy-logic and compared with a consolidated decision threshold approach using C-band SAR backscattering information. For snow height and density estimation, the proposed methodology is compared with an analytical inverse method and two model-based statistical techniques (linear regression and maximum likelihood). The validation is carried out in Central Apennines, a mountainous area in Italy with an extension of about 104 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and peaks up to 2912 m, using in situ data collected between December 2018 and February 2019. Results show that the ANN-based technique has a snow cover area classification accuracy of more than 80% when compared MODIS maps. Estimation bias and root mean square error are equal to about 0.5 cm and 20 cm for snow height and to 5 kg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> and 80 kg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> for snow density. As expected, worse results are associated to low DInSAR coherence between two repeat passes and to snow melting periods.

Solution And Visualization 3D Plane Inverse Kinematics Method

The hyper-redundant type of robot is a type of robot that in carrying out its duties in the field of kinematics its degrees of freedom exceed the required minimum degrees. The advantage will be increased capability in operation and performance, if the degrees of freedom are excessive, even in unorganized and complex systems and environments. Algebraic approach method in inverse kinematics algorithm analysis can use; analytic algebra, jacobian basis, analytic KI, exponential multiplication, grobner, and conformal geometry. Iterative approach method in inverse kinematics algorithm analysis can use; genetic algorithm, fuzzy logic, ANFIS, and evolutionary algorithm. The geometric approach method in the inverse kinematics algorithm analysis can use; capital method. The purpose of this study is to analyze a virtual 2 arm robot, which will use axis manipulation in three dimensions using an inverse kinematics solution, using a geometric approach. How to step along on the z axis by rotating and using the reverse kinematics solution to the desired location. The visualization results will be repeated so as to ensure the effectiveness of the algorithm. As for this algorithm will provide a single solution, and this algorithm will prevent and reduce singularities if the link is lower.

Shape optimization of Hourglass-shaped damper using Fe-Mn-Si-Based alloy considering target Load-displacement relationship and target fatigue characteristics

To reduce the response of buildings during earthquakes, the use of various hysteresis dampers for passive control is increasing. For effective application to buildings, these dampers must meet both load displacement relationship and fatigue property requirements. In this study, shape optimization of an Fe-Mn-Si-based alloy damper was conducted to improve the fatigue property while maintaining the target load displacement relationship, and the obtained optimized damper was examined. First, shape optimization was conducted using the modal iterative error correction (MIEC) inverse analysis method. Subsequently, a loading test on the damper fabricated based on the optimized shape was conducted, and its effectiveness was confirmed. As a result, it was confirmed that the optimized shape of the damper, which improves the fatigue performance while maintaining the load displacement relationship, was successfully searched using the MIEC inverse analysis method. In addition, it was confirmed that the fatigue performance of the test specimen based on the optimized shape was improved compared to that of the initial-shape.

Apparent wave velocity inverse analysis method and its application in dynamic pile testing

AbstractThis study proposes a new non‐destructive testing method (NDT), namely the apparent wave velocity of piles (AWVP) inverse analysis method, to solve the detection problem of the gradually varying cross‐sectional defects (cracks or necking) and the material property defects (concrete disintegration or steel corrosion) of piles. The analytical solution of the AWVP has been derived based on the additional mass model. The rationality and accuracy of the theoretical model have been validated through the comparisons with the experiment results. The variation mechanism of the AWVP due to the presence of soil has been clarified. A parametric study is conducted to investigate the major factors to determine the variation tendency of the AWVP. The optimum working conditions and parameter combinations of the AWVP based NDT method have been recommend. The main conclusions can be drawn as: (1) the variation of the AWVP with respect to frequency can be generally divided into a sensitive zone and a stable zone. (2) The AWVP initially decreases rapidly with the frequency within the sensitive zone, however when the frequency decreases to the stable zone, this decline tendency becomes much smaller. (3) Although the AWVP varies extensively within the frequency sensitive zone, this frequency range can substantially relax the requirements for the parametric accuracy of the pile–soil system.