Data-driven Modeling Techniques Research Articles

Alzheimer’s disease diagnosis using genetic programming based on higher order spectra features

In Alzheimer’s diagnosis field, Computer-Aided Diagnosis (CADx) technology can improve the work performance of medical researchers and practitioners since it gives early chances to patient’s eligibility for clinical trials. The aim of this study is to develop a novel CADx system for the diagnosis of Alzheimer’s disease (AD) by utilizing genetic programming (GP) as data-driven evolutionary computation based modeling. The proposed method invokes a majority voting based scheme to select a set of most discriminant features which leads to the highest diagnosis accuracy of the final classification. The effectiveness of GP in categorizing patients with Alzheimer’s versus healthy group was revealed by developing models according to their performance in terms of higher-order spectra (HOS) features. The results show that the GP method achieved better performance compared to other the-state-of-the-art approaches. It is also found that the highest accuracy index was yielded by using the proposed data-driven modeling technique. The results of this study emphasize the practicality of GP-based method for developing CADx systems, on the basis of spontaneous speech analysis; can efficiently assist in the diagnosis of Alzheimer’s disease.

OASIS-P: Operable Adaptive Sparse Identification of Systems for fault Prognosis of chemical processes

With the increasing process complexities, data-driven fault prognosis has emerged as a promising fault management tool that predicts and manages abnormal events well in advance. In this paper, we develop a fault prognosis framework named ‘OASIS-P’ by integrating operable adaptive sparse identification of systems (OASIS), which is a data-driven adaptive modeling technique, with a risk-based process monitoring approach and contribution plots. Firstly, OASIS is employed with the risk assessment procedure for the prediction of impending faults. As the OASIS model is adaptive, it copes with the initial fault symptoms and forecasts the future behavior of the process under faulty conditions reasonably well, thereby providing an early fault prediction. Next, the fault isolation step is immediately initiated using contribution plots to identify the faulty variables. Unlike in fault diagnosis, the problem of ambiguity in interpreting contribution results due to fault propagation is not an issue in fault prognosis, if the fault isolation step is implemented at an early stage of the fault before it affects the other variables. Hence, the contribution plots together with OASIS can proactively monitor the process in real-time. As a case study, we demonstrate OASIS-P for fault prognosis of a reactor–separator system.

Ensemble prediction of mean bubble size in a continuous casting mold using data driven modeling techniques

Gas injection into a slab continuous casting mold is a common practice as it enables long casting sequences by reducing SEN clogging. However, too much gas injection can generate lots of bubbles, and bubbles formed inside the mold are also responsible for the occurrence of various quality defects such as deterioration of steel cleanliness, reoxidation of liquid steel, and occurrence of sliver and blister defects, etc. Mean bubble size is a key parameter and controlling it can resolve/reduce these quality issues. Operating parameters such as gas flow rate and liquid flow rate are the major factors affecting the mean bubble size. Two-phase water modeling​ experiments were performed to generate bubbles in the mold, and the mean bubble diameter was captured using various gas and liquid flow rates. Clear images of bubbles were recorded using a high-speed high-resolution camera along with the application of shadowgraphy. Bubble images were processed using an image processing software, ImageJ to obtain bubble characteristics, and Sauter mean diameter was calculated for each operating condition. Advance machine learning techniques such as Multilinear Regression (MLR), Decision Tree (DT), Support Vector Machine (SVM), and Artificial Neural Network (ANN) were used on the experimental data to predict the combined effect of these operating parameters on the mean bubble diameter. All four ML techniques were compared considering the values of cross-validated adjusted R2, and a performance metric is presented to compare the suitability of ML techniques in this case.

Prior informed regularization of recursively updated latent-variables-based models with missing observations

Many data-driven modeling techniques identify locally valid, linear representations of time-varying or nonlinear systems, and thus the model parameters must be adaptively updated as the operating conditions of the system vary, though the model identification typically does not consider prior knowledge. In this work, we propose a new regularized partial least squares (rPLS) algorithm that incorporates prior knowledge in the model identification and can handle missing data in the independent covariates. This latent variable (LV) based modeling technique consists of three steps. First, a LV-based model is developed on the historical time series data. In the second step, the missing observations in the new incomplete data sample are estimated. Finally, the future values of the outputs are predicted as a linear combination of estimated scores and loadings. The model is recursively updated as new data are obtained from the system. The performance of the proposed rPLS and rPLS with exogenous inputs (rPLSX) algorithms are evaluated by modeling variations in glucose concentration (GC) of people with Type 1 diabetes (T1D) in response to meals and physical activities for prediction windows up to one hour, or 12 sampling instances, into the future. The proposed rPLS family of GC prediction models are evaluated with both in-silico and clinical experiment data and compared with the performance of recursive time series and kernel-based models. The root mean squared error (RMSE) with simulated subjects in the multivariable T1D simulator where physical activity effects are incorporated in GC variations are 2.52 and 5.81 mg/dL for 30 and 60 mins ahead predictions (respectively) when information for all meals and physical activities are used, increasing to 2.70 and 6.54 mg/dL (respectively) when meals and activities occurred, but the information is withheld from the modeling algorithms. The RMSE is 10.45 and 14.48 mg/dL for clinical study with prediction horizons of 30 and 60 mins, respectively. The low RMSE values demonstrate the effectiveness of the proposed rPLS approach compared to the conventional recursive modeling algorithms.

An efficient method for acquisition of spectral BRDFs in real-world scenarios

Modelling of material appearance from reflectance measurements has become increasingly prevalent due to the development of novel methodologies in Computer Graphics. In the last few years, some advances have been made in measuring the light-material interactions, by employing goniometers/reflectometers under specific laboratory’s constraints. A wide range of applications benefit from data-driven appearance modelling techniques and material databases to create photorealistic scenarios and physically based simulations. However, important limitations arise from the current material scanning process, mostly related to the high diversity of existing materials in the real-world, the tedious process for material scanning and the spectral characterisation behaviour. Consequently, new approaches are required both for the automatic material acquisition process and for the generation of measured material databases. In this study, a novel approach for material appearance acquisition using hyperspectral data is proposed. A dense 3D point cloud filled with spectral data was generated from the images obtained by an unmanned aerial vehicle (UAV) equipped with an RGB camera and a hyperspectral sensor. The observed hyperspectral signatures were used to recognise natural and artificial materials in the 3D point cloud according to spectral similarity. Then, a parametrisation of Bidirectional Reflectance Distribution Function (BRDF) was carried out by sampling the BRDF space for each material. Consequently, each material is characterised by multiple samples with different incoming and outgoing angles. Finally, an analysis of BRDF sample completeness is performed considering four sunlight positions and 16x16 resolution for each material. The results demonstrated the capability of the used technology and the effectiveness of our method to be used in applications such as spectral rendering and real-word material acquisition and classification.

Fast Simulation of Analog Circuit Blocks Under Nonstationary Operating Conditions

This article proposes a black-box behavioral modeling framework for analog circuit blocks (CBs) operating under small-signal conditions around nonstationary operating points. Such variations may be induced either by changes in the loading conditions or by event-driven updates of the operating point for system performance optimization, e.g., to reduce power consumption. An extension of existing data-driven parameterized reduced-order modeling techniques is proposed, which considers the time-varying bias components of the port signals as nonstationary parameters. These components are extracted at runtime by a low-pass filter and used to instantaneously update the matrices of the reduced-order state-space model realized as a SPICE netlist. Our main result is a formal proof of quadratic stability of such linear parameter varying (LPV) models, enabled by imposing a specific model structure and representing the transfer function in a basis of positive functions whose elements constitute a partition of unity. The proposed quadratic stability conditions are easily enforced through a finite set of small-size linear matrix inequalities (LMIs), used as constraints during model construction. Numerical results on various CBs, including voltage regulators, confirm that our approach not only ensures the model stability but also provides speedup in runtime up to two orders of magnitude with respect to full transistor-level circuits.

Data-driven modelling techniques for earth-air heat exchangers to reduce energy consumption in buildings: a review

Data-driven modelling techniques for earth-air heat exchangers to reduce energy consumption in buildings: a review

Machine learning prediction models for battery-electric bus energy consumption in transit

Road gradient (g), initial state of charge (SoCi), drag coefficient (Cd), road condition (RC), HVAC, driver aggressiveness (Dagg.), passenger loading (PL), stop density (SD), average speed (Va), Multiple Regression Analysis (MLR), Radial Basis Function (RBF), Decision Tree Model (DT), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), Multilayer Perception Neural Network (MLP-NN), and Radial Basis Neural Network (RBNN) • We developed and validated a simulation model to estimate the E C of BEBs. • A factorial experimental design generated BEB operation scenarios. • Seven data-driven models are developed to predict BEB energy consumption. • The models accommodate vehicular, operational, topological, and external parameters. • Multiple Regression and support vector machine models are deemed appropriate to predict BEB’s E C . The energy consumption (E C ) of battery-electric buses (BEB) varies significantly due to the intertwined relationships of vehicular, operational, topological, and external parameters. This variation is posing several challenges to predict BEB’s energy consumption. Several studies are calling for the development of data-driven models to address this challenge. This study develops and compares seven data-driven modelling techniques that cover both machine learning and statistical models. The models are based on a full-factorial experimental design ( n = 907,199 ) of a validated Simulink energy simulation model. The models are then used to predict E C using a testing dataset ( n = 169,344 ). The results show some minor discrepancies between the developed models. All models explained more than 90% of the energy consumption variance. Further, the results indicate that road gradient and the battery state of charge are the most influential factors on E C , while driver behaviour and drag coefficient have the lowest impact.

The design and development of a diesel engine electromechanical EGR cooling system based on machine learning-genetic algorithm prediction models to reduce emission and fuel consumption

Data-driven modelling techniques have recently been used in the development of engine design and control systems for defining the engine in-cylinder complex combustion process. The aim of this investigation is to improve exhaust emissions and fuel consumption by designing an electromechanical exhaust gas recirculation (EGR) cooling system consisting of an electric water pump and fan unlike conventional systems. To determine the effects of the EGR ratio and the temperature of the exhaust gas entering the intake manifold on the diesel engine parameters of nitrogen oxides (NOx) emission and brake specific fuel consumption (BSFC), four learning (ML) algorithms were adapted according to statistical evaluation criteria such as root mean squared error (RMSE), coefficient of determination (R2), mean squared error (MSE) and mean absolute error (MAE). The hyper parameters of the selected best model among four learning algorithms were determined by using grid search method. The results showed that the Gaussian process regression model (GPR) outperformed other ML models according to success error prediction of NOx and BSFC. Then, performance of the designed electromechanical EGR cooling system was analyzed under global driving conditions, the New European driving cycle (NEDC), and the world-wide harmonized light duty test procedure (WLTP). In these test cycles, global optimization was utilized with the GPR model as the objective function based on minimizing NOx and BSFC. Consequently, this study demonstrates the potential of the proposed system based on ML-GA to reduce NOx and BSFC by achieving reductions of 13.7%(NEDC)–9.98%(WLTP) and 2.61%(NEDC)–2.07%(WLTP) in NEDC and WLTP conditions, respectively compared to the conventional EGR cooling approach.

A Two-Stage Data-Driven Spatiotemporal Analysis to Predict Failure Risk of Urban Sewer Systems Leveraging Machine Learning Algorithms.

Risk-informed asset management is key to maintaining optimal performance and efficiency of urban sewer systems. Although sewer system failures are spatiotemporal in nature, previous studies analyzed failure risk from a unidimensional aspect (either spatial or temporal), not accounting for bidimensional spatiotemporal complexities. This is owing to the insufficiency of good-quality data, which ultimately leads to under-/overestimation of failure risk. Here, we propose a generalized methodology/framework to facilitate a robust spatiotemporal analysis of urban sewer system failure risk, overcoming the intrinsic challenges of data imperfections-e.g., missing data, outliers, and imbalanced information. The framework includes a two-stage data-driven modeling technique that efficiently models the highly right-skewed sewer system failure data to predict the failure risk, leveraging a bidimensional space-time approach. We implemented our analysis for Bogotá, the capital city of Colombia. We train, test, and validate a battery of machine learning algorithms-logistic regression, decision trees, random forests, and XGBoost-and select the best model in terms of goodness-of-fit and predictive accuracy. Finally, we illustrate the applicability of the framework in planning/scheduling sewer system maintenance operations using state-of-the-art optimization techniques. Our proposed framework can help stakeholders to analyze the failure-risk models' performance under different discrimination thresholds, and provide managerial insights on the model's adequate spatial resolution and appropriateness of decentralized management for sewer systemmaintenance.

Risk-based fault prediction of chemical processes using operable adaptive sparse identification of systems (OASIS)

Fault prediction has arisen as a basic monitoring strategy that predicts an abnormal event occurring in near future based on the current symptoms observed in a process. Such a proactive approach helps in taking an appropriate action beforehand so as to mitigate the impact a fault can have on a process. Recently, data-driven modeling techniques have been widely used due to an increased accessibility to process data. Though the offline trained models are successful in modeling complex dynamics, they have limited ability in capturing the dynamic process behavior, especially under abnormal conditions. To address this issue, we utilize an adaptive modeling technique called operable adaptive sparse identification of systems (OASIS) that can cope with any dynamical changes. Based on the forecasted process behavior using OASIS, we perform risk-assessment to predict faults and assess risk. In the proposed method, risk is used as a criteria to monitor and manage process operation.

Ten challenges for the future of pedometrics

Pedometrics, the application of mathematical and statistical methods to the study of the distribution and genesis of soils, has broadened its scope over the past two decades. The primary focus of pedometricians has traditionally been on spatial and spatio-temporal soil inventories with numerical soil classification, geostatistical modelling of spatial variation and mapping. The rapid development of remote and proximal soil sensing as well as data-driven statistical modelling techniques have had a major impact on pedometrics over the past decades. During this time, a general demand for quantitative digital soil information for environmental modelling and management has compelled pedometricians to address other soil-related questions from a quantitative point of view: soil genesis and utility and quality of soil. While scientific progress is largely an autonomous process that is difficult to steer, research efforts could benefit from an agenda with pressing pedometric research topics. This paper defines and discusses ten recent or longstanding pedometrics challenges, with the attempt to identify knowledge gaps and suggest new concepts and methods to overcome them. The ten challenges were selected through a collaborative effort and may serve as a guidance for future pedometrics research and to foster collaboration among soil scientists. The challenges discussed in this paper are also indicators of the current understanding and state of knowledge from which progress can be measured in the future.

Hybrid Modeling Approach for Melt-Pool Prediction in Laser Powder Bed Fusion Additive Manufacturing

Abstract Multi-scale, multi-physics, computational models are a promising tool to provide detailed insights to understand the process–structure–property–performance relationships in additive manufacturing (AM) processes. To take advantage of the strengths of both physics-based and data-driven models, we propose a novel, hybrid modeling framework for laser powder bed fusion (L-PBF) process. Our unbiased model-integration method combines physics-based, simulation data, and measurement data for approaching a more accurate prediction of melt-pool width. Both a high-fidelity computational fluid dynamics (CFD) model and experiments utilizing optical images are used to generate a combined dataset of melt-pool widths. From this aggregated data set, a hybrid model is developed using data-driven modeling techniques, including polynomial regression and Kriging methods. The performance of the hybrid model is evaluated by computing the average relative error and comparing it with the results of the simulations and surrogate models constructed from the original CFD model and experimental measurements. It is found that the proposed hybrid model performs better in terms of prediction accuracy and computational time. Future work includes a conceptual introduction to the use of an AM ontology to support improved model and data selection when constructing hybrid models. This study can be viewed as a significant step toward the use of hybrid models as predictive models with improved accuracy and without the sacrifice of speed.

Model Predictive Control-Based Hierarchical Control of a Multi-Zone Commercial HVAC System

Abstract This paper presents a novel architecture for model predictive control (MPC)-based indoor climate control of multi-zone buildings to provide energy efficiency. Unlike prior works, we do not assume the availability of a high-resolution multi-zone building model, which is challenging to obtain. Instead, the architecture uses a low-resolution model of the building that is divided into a small number of “meta-zones” that can be easily identified using existing data-driven modeling techniques. The proposed architecture is hierarchical. At the higher level, an MPC controller uses the low-resolution model to make decisions for the air handling unit (AHU) and the meta-zones. Since the meta-zones are fictitious, a lower level controller converts the high-level MPC decisions into commands for the individual zones by solving a projection problem that strikes a trade-off between two potentially conflicting goals: the AHU-level decisions made by the MPC are respected while the climate of the individual zones is maintained within the comfort bounds. The performance of the proposed controller is assessed via simulations in a high-fidelity simulation testbed and compared to that of a rule-based controller that is used in practice. Simulations in multiple weather conditions show the effectiveness of the proposed controller in terms of energy savings, climate control, and computational tractability.

Data-driven cardiovascular flow modelling: examples and opportunities.

High-fidelity blood flow modelling is crucial for enhancing our understanding of cardiovascular disease. Despite significant advances in computational and experimental characterization of blood flow, the knowledge that we can acquire from such investigations remains limited by the presence of uncertainty in parameters, low resolution, and measurement noise. Additionally, extracting useful information from these datasets is challenging. Data-driven modelling techniques have the potential to overcome these challenges and transform cardiovascular flow modelling. Here, we review several data-driven modelling techniques, highlight the common ideas and principles that emerge across numerous such techniques, and provide illustrative examples of how they could be used in the context of cardiovascular fluid mechanics. In particular, we discuss principal component analysis (PCA), robust PCA, compressed sensing, the Kalman filter for data assimilation, low-rank data recovery, and several additional methods for reduced-order modelling of cardiovascular flows, including the dynamic mode decomposition and the sparse identification of nonlinear dynamics. All techniques are presented in the context of cardiovascular flows with simple examples. These data-driven modelling techniques have the potential to transform computational and experimental cardiovascular research, and we discuss challenges and opportunities in applying these techniques in the field, looking ultimately towards data-driven patient-specific blood flow modelling.

Robust predictive control of coupled water tank plant

Liquid level control of the liquid tank is a basic control problem in the process industry. The liquid level control system usually has the characteristics of strong coupling, nonlinearity and large lag. Up to now, PID control is the main method of liquid level control in industry, but it is difficult to obtain high precision control performance. The difficulty of model-based liquid level control such as Robust Model Predictive Control (RMPC) is hard to obtain an accurate model of the plant or need accurate steady-state information which is hard to be obtained in practice. This paper provides a systematic modeling and RMPC method to implement a Coupled Water Tank (CWT) plant output-tracking control in the case without whole steady-state information of the system. First, a data-driven modeling technique based on the Radial Basis Function (RBF) net-type coefficients Multi-Input/Multi-Output (MIMO) AutoRegressive model with eXogenous variable (ARX) is applied to build the RBF-ARX model of the CWT plant. Using the model, a locally linearized model and a polytopic uncertain linear parameter varying (LPV) model are constructed to represent the current and future behavior of the nonlinear CWT system. Based on the two models, an RMPC method is designed to implement the system’s output tracking control under the condition without whole steady-state knowledge of the CWT plant. The stability of the RMPC strategy is guaranteed by using the time-varying parameter-dependent Lyapunov function and feasibility of the Linear Matrix Inequalities (LMIs). Real-time control experiments and comparison with other control methods are carried out on the CWT system, and the results show that the presented method is superior.

COMBINING DATA-DRIVEN AND NUMERICAL MODELLING APPROACHES TO STORM EROSION PREDICTION

Physics-based numerical models play an important role in the estimation of storm erosion, particularly at beaches for which there is little historical data. However, the increasing availability of pre-and post-storm data for multiple events and at a number of beaches around the world has opened the possibility of using data-driven approaches for erosion prediction. Both physics-based and purely data-driven approaches have inherent strengths and weaknesses in their ability to predict storm-induced erosion. It is vital that coastal managers and modelers are aware of these trade-offs as well as methods to maximise the value from each modelling approach in an increasingly data-rich environment. In this study, data from approximately 40 years of coastal monitoring at Narrabeen-Collaroy Beach (SE Australia)has been used to evaluate the individual performance of the numerical erosion models SBEACH and XBeach, and a data-driven modelling technique. The models are then combined using a simple weighting technique to provide a hybrid estimate of erosion.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/v53dZiO8Y60

Effectiveness of multi-gated sequence model for the learning of kinematics and dynamics of an industrial robot

Purpose For efficient trajectory control of industrial robots, a cumbersome computation for inverse kinematics and inverse dynamics is needed, which is usually developed using spatial transformation using Denavit–Hartenberg principle and Lagrangian or Newton–Euler methods, respectively. The model is highly non-linear and needs to deal with uncertainties because of lack of accurate measurement of mechanical parameters, noise and non-inclusion of joint friction, which results in some inaccuracies in predicting accurate torque trajectories. To get a guaranteed closed form solution, the robot designers normally follow Pieper’s recommendation and compromise with the mechanical design. While this may be acceptable for the industrial robots where the aesthetic look is not that important, it is not for humanoid and social robots. To help solve this problem, this study aims to propose an alternative machine learning-based computational approach based on a multi-gated sequence model for finding appropriate mapping between Cartesian space to joint space and motion space to joint torque space. Design/methodology/approach First, the authors generate sufficient data required for the sequence model, using forward kinematics and forward dynamics by running N number of nested loops, where N is the number of joints of the robot. Subsequently, to develop a learning-based model based on sequence analysis, the authors propose to use long short-term memory (LSTM) and hence, train an LSTM model, the architecture details of which have been discussed in the paper. To make LSTM learning algorithms perform efficiently, the authors need to detect and eliminate redundant features from the data set, which the authors propose to do using an elegant statistical tool called Pearson coefficient. Findings To validate the proposed model, the authors have performed rigorous experiments using both hardware and simulation robots (Baxter/Anukul robot) available in their laboratory and KUKA simulation robot data set made available from Neural Learning for Robotics Laboratory. Through several characteristic plots, it has been shown that a sequence-based LSTM model of deep learning architecture with non-redundant features could help the robots to learn smooth and accurate trajectories more quickly compared to data sets having redundancy. Such data-driven modeling techniques can change the future course of direction of robotics research for solving the classical problems such as trajectory planning and motion planning for manipulating industrial as well as social humanoid robots. Originality/value The present investigation involves development of deep learning-based computation model, statistical analyses to eliminate redundant features, data creation from one hardware robot (Anukul) and one simulation robot model (KUKA), rigorously training and testing separately two computational models (specially configured two LSTM models) – one for learning inverse kinematics and one for learning inverse dynamics problem – and comparison of the inverse dynamics model with the state-of-the-art model. Hence, the authors strongly believe that the present paper is compact and complete to get published in a reputed journal so that dissemination of new ideas can benefit the researchers in the area of robotics.

Optimization-Based Data-Enabled Modeling Technique for HVAC Systems Components

Most of the energy consumed by the residential and commercial buildings in the U.S. is dedicated to space cooling and heating systems, according to the U.S. Energy Information Administration. Therefore, the need for better operation mechanisms of those existing systems become more crucial. The most vital factor for that is the need for accurate models that can accurately predict the system component performance. Therefore, this paper’s primary goal is to develop a new accurate data-driven modeling and optimization technique that can accurately predict the performance of the selected system components. Several data-enabled modeling techniques such as artificial neural networks (ANN), support vector machine (SVM), and aggregated bootstrapping (BSA) are investigated, and model improvements through model structure optimization proposed. The optimization algorithm will determine the optimal model structures and automate the process of the parametric study. The optimization problem is solved using a genetic algorithm (GA) to reduce the error between the simulated and actual data for the testing period. The models predicted the performance of the chilled water variable air volume (VAV) system’s main components of cooling coil and fan power as a function of multiple inputs. Additionally, the packaged DX system compressor modeled, and the compressor power was predicted. The testing results held a low coefficient of variation (CV%) values of 1.22% for the cooling coil, and for the fan model, it was found to be 9.04%. The testing results showed that the proposed modeling and optimization technique could accurately predict the system components’ performance.

Improved Transferability of Data-Driven Damage Models Through Sample Selection Bias Correction.

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.