Error-domain Model Falsification Research Articles

Improved Finite Element Model Updating of a Highway Viaduct Using Acceleration and Strain Data.

Most finite element model updating (FEMU) studies on bridges are acceleration-based due to their lower cost and ease of use compared to strain- or displacement-based methods, which entail costly experiments and traffic disruptions. This leads to a scarcity of comprehensive studies incorporating strain measurements. This study employed the strain- and acceleration-based FEMU analyses performed on a more than 50-year-old multi-span concrete highway viaduct. Mid-span strains under heavy vehicles were considered for the strain-based FEMU, and frequencies and mode shapes for the acceleration-based FEMU. The analyses were performed separately for up to three variables, representing Young's modulus adjustment factors for different groups of structural elements. FEMU studies considered residual minimisation and the error-domain model falsification (EDMF) methodology. The residual minimisation utilised four different single-objective optimisations focusing on strains, frequencies, and mode shapes. Strain- and frequency-based FEMU analyses resulted in an approximately 20% increase in the overall superstructure's design stiffness. This study shows the benefits of the intuitive EDMF over residual minimisation for FEMU, where information gained from the strain data, in addition to the acceleration data, manifests more sensible updated variables. EDMF finally resulted in a 25-50% overestimated design stiffness of internal main girders.

Methodology Maps for Model-Based Sensor-Data Interpretation to Support Civil-Infrastructure Management

With increasing urbanization and depleting reserves of raw materials for construction, sustainable management of existing infrastructure will be an important challenge in this century. Structural sensing has the potential to increase knowledge of infrastructure behavior and improve engineering decision making for asset management. Model-based methodologies such as residual minimization (RM), Bayesian model updating (BMU) and error-domain model falsification (EDMF) have been proposed to interpret monitoring data and support asset management. Application of these methodologies requires approximations and assumptions related to model class, model complexity and uncertainty estimations, which ultimately affect the accuracy of data interpretation and subsequent decision making. This paper introduces methodology maps in order to provide guidance for appropriate use of these methodologies. The development of these maps is supported by in-house evaluations of nineteen full-scale cases since 2016 and a two-decade assessment of applications of model-based methodologies. Nineteen full-scale studies include structural identification, fatigue-life assessment, post-seismic risk assessment and geotechnical-excavation risk quantification. In some cases, much, previously unknown, reserve capacity has been quantified. RM and BMU may be useful for model-based data interpretation when uncertainty assumptions and computational constraints are satisfied. EDMF is a special implementation of BMU. It is more compatible with usual uncertainty characteristics, the nature of typically available engineering knowledge and infrastructure evaluation concepts than other methodologies. EDMF is most applicable to contexts of high magnitudes of uncertainties, including significant levels of model bias and other sources of systematic uncertainty. EDMF also provides additional practical advantages due to its ease of use and flexibility when information changes. In this paper, such observations have been leveraged to develop methodology maps. These maps guide users when selecting appropriate methodologies to interpret monitoring information through reference to uncertainty conditions and computational constraints. This improves asset-management decision making. These maps are thus expected to lead to lower maintenance costs and more sustainable infrastructure compared with current practice.

A smart sensor-data-driven optimization framework for improving the safety of excavation operations

Excavation is a complex multistage problem, where field responses of soil properties such as deflections at one stage of the operation depend on responses at the preceding stage. In order to help asset managers make better decisions and thus improve safety, soil properties should be accurately identified using sensor-data collected at the current stage. This task is not easy to accomplish, mainly because of its intrinsic ambiguity. Sensors usually only measure effects (e.g., field responses) but not causes (e.g., soil parameter values). A strategy that helps meet this challenge is to perform inverse analysis to validate soil parameter values. Error-Domain Model Falsification (EDMF) is a methodology that achieves this goal. More precisely, EDMF helps identify good behavior models of excavation by falsifying soil parameter values for which the predictions of the corresponding behavior models cannot explain field-response measurements collected by sensors. However, a remaining challenge is the identification of soil parameter values that are not falsified by EDMF, especially when the computation of the predictions is time-consuming. This paper proposes a new framework that combines EDMF and an optimization algorithm for efficient identification of soil parameter values. Results on a full-scale excavation site in Singapore show that the new framework is robust and accurate, and it has the potential to improve current practice, which relies primarily on surrogate models without uncertainty.

Increasing occupant localization precision through identification of footstep-contact dynamics

Information regarding occupants inside buildings has the potential to improve security, energy management, and caregiving. Typical sensing approaches for occupant localization rely on mobile devices and cameras. These systems compromise privacy. Occupant localization using floor-vibration measurements, induced by footsteps, is a non-intrusive sensing method that requires few sensors (one per ~35 m2). Current occupant-localization methodologies that rely on vibration measurements are data-driven techniques. These techniques do not account for the structural behavior of floor slabs leading to ambiguous interpretations of vibrations measurement in the presence of obstructions and varying floor rigidities. In this paper, a model-based approach using error-domain model falsification (EDMF) is used to overcome these limitations. EDMF incorporates information related to physics-based models in the interpretation of vibration measurements to identify a population of possible occupant locations. EDMF accommodates systematic errors and model bias to reject models that contradict measurement data. Uncertainties from multiple sources such as modeling imperfection and walking-gait variability are included explicitly while estimating occupant locations using EDMF. A unique approach to identify footstep-contact dynamics is proposed and evaluated for its ability to improve the precision of occupant localization. The approach involves dividing the floor-slab into zones using knowledge of structural behavior. Clustering measured vibrations to define several footstep-contact severity levels helps reduce uncertainty in walking gait thus improving the accuracy of footstep-contact dynamics to use as loading input into model simulations. The utility of occupant localization using this approach is evaluated using a full-scale case study. Localization precision increased by more than 50% compared with non-zone-based strategies.

Uncertainties in Structural Behavior for Model-Based Occupant Localization Using Floor Vibrations

In sensed buildings, information related to occupant movement helps optimize functions such as security, energy management, and caregiving. Due to privacy needs, non-intrusive sensing approaches for tracking occupants inside buildings, such as vibration sensors, are often preferred over intrusive strategies that involve use of cameras and wearable devices. Current sensor-based occupant-localization approaches are data-driven techniques that do not account for structural behavior and limited to slabs on grade. Varying-rigidity floors and inherent variability in walking gaits lead to ambiguous interpretations of floor vibrations when performing model-free occupant localization. In this paper, an extensive analysis of vibrations induced by a range of occupants is described. Firstly, the need for a structural-behavior model for occupant localization is assessed using two full-scale case studies. Structural behavior is found to significantly influence floor vibrations induced by footstep impacts. Since a simple relationship between distances from footstep-impact to sensor locations cannot be assured, the use of physics-based models is necessary for accurate occupant localization. Secondly, measured data are interpreted using physics-based models and information related to uncertainties from multiple sources. There are two types of uncertainties: modelling uncertainties and measurement uncertainties, including variability in walking gaits. Error-domain model falsification (EDMF) and residual minimization (RM) are model-based approaches for data interpretation. Unlike RM, EDMF explicitly accounts for the presence of systematic errors in parameters and overall model bias. In this paper, model-based occupant localization is carried out using EDMF and RM on a full-scale case study. By explicitly accounting for the presence of uncertainties and the influence of structural behavior, EDMF, unlike RM, accurately reveals possible occupant locations on floor slabs.

Validating model-based data interpretation methods for quantification of reserve capacity

Optimal performance of civil infrastructure is an important aspect of liveable cities. A judicious combination of physics-based models with monitoring data in a validated methodology that accounts for uncertainties is explored in this paper. This methodology must support asset managers when they need to extrapolate current performance to meet future needs. Three model-based data-interpretation methodologies, residual minimization, Bayesian model updating and error-domain model falsification (EDMF), are compared according to their ability to provide accurate interpretations of monitoring data. These comparisons are made using a full-scale case study, a steel-concrete composite bridge in USA. Validation of data interpretation is carried out using cross-validation (leave-one-out and hold-out). A joint-entropy metric is used to evaluate the extent to which the data that is used for validation contains information that is independent of data used for interpreting structural behaviour. Once accurately updated and validated knowledge of structural behaviour is available, it is employed to make predictions of remaining fatigue-life of the bridge. Validated identification of structural behaviour helps ensure accurate predictions of capacity of bridges beyond their design lives. EDMF and a modified form of Bayesian model updating are analytically and numerically equivalent, while EDMF has several practical advantages. Both methods provide accurate identification and safe estimations of the remaining fatigue life of the bridge. Such enhanced understanding of structural behaviour leads to appropriate decisions regarding civil infrastructure assets.

Comparative study of the effects of three data‐interpretation methodologies on the performance of geotechnical back analysis

SummaryBack analysis can provide engineers with important information for better decision‐making. Over the years, research on back analysis has focused mainly on optimisation techniques, while comparative studies of data‐interpretation methodologies have seldom been reported. This paper examines the use of three data‐interpretation methodologies on the performance of geotechnical back analysis. In general, there are two types of approaches for interpreting model predictions using field measurements, deterministic versus population‐based, both of which are considered in this study. The methodologies that are compared are (a) error‐domain model falsification (EDMF), (b) Bayesian model updating and (c) residual minimisation. Back analyses of an excavation case history in Singapore using the three methodologies indicate that each has strengths and limitations. Residual minimisation, though easy to implement, shows limited capabilities of interpreting measurement data with large uncertainty errors. EDMF provides robustness against incomplete information of the correlation structure. This is achieved at the expense of precision, as EDMF yields wider confidence intervals of the identified parameter values and predicted quantities compared with Bayesian model updating. In this regard, a modified EDMF implementation is proposed, which can improve upon the limitations of the traditional EDMF method, thus enhancing the quality of the identification outcomes.

Data-Interpretation Methodologies for Practical Asset-Management

Monitoring and interpreting structural response using structural-identification methodologies improves understanding of civil-infrastructure behavior. New sensing devices and inexpensive computation has made model-based data interpretation feasible in engineering practice. Many data-interpretation methodologies, such as Bayesian model updating and residual minimization, involve strong assumptions regarding uncertainty conditions. While much research has been conducted on the scientific development of these methodologies and some research has evaluated the applicability of underlying assumptions, little research is available on the suitability of these methodologies to satisfy practical engineering challenges. For use in practice, data-interpretation methodologies need to be able, for example, to respond to changes in a transparent manner and provide accurate model updating at minimal additional cost. This facilitates incremental and iterative increases in understanding of structural behavior as more information becomes available. In this paper, three data-interpretation methodologies, Bayesian model updating, residual minimization and error-domain model falsification, are compared based on their ability to provide robust, accurate, engineer-friendly and computationally inexpensive model updating. Comparisons are made using two full-scale case studies for which multiple scenarios are considered, including incremental acquisition of information through measurements. Evaluation of these scenarios suggests that, compared with other data-interpretation methodologies, error-domain model falsification is able to incorporate, iteratively and transparently, incremental information gain to provide accurate model updating at low additional computational cost.

Model-Based Occupant Tracking Using Slab-Vibration Measurements

Sensor-based occupant tracking has the potential to enhance knowledge of the utilization of buildings. Occupancy-tracking strategies using footstep-induced floor vibrations may be beneficial for thermal-load prediction, security enhancement and care-giving without undermining privacy. Current floor-vibration-based occupant-tracking methodologies are based on data-driven techniques that do not include a physics-based model of the structural behavior of the floor slab. These techniques suffer from ambiguous interpretations when signals are affected by complex configurations of structural and non-structural elements such as beams and walls. Using a physics-based model for data-interpretation enables deployment of sparse number of sensors in contexts of non-uniform structural configurations. In this paper, an application of physics-based data interpretation using error-domain model falsification (EDMF) is presented to track an occupant within an office environment through footstep-induced floor vibrations. EDMF is a population-based approach that incorporates various sources of uncertainty, including bias, arising from measurements and modeling. EDMF involves the rejection of simulated model responses that contradict footstep-induced floor vibration measurements. Thus, EDMF provides a set of candidate locations from an initial population of possible occupant locations. A sequential analysis that accommodates information from previous footsteps is then used to enhance candidate locations and identify trajectories among candidates. In this way, incorporating structural behavior in interpreting vibration measurements induced by occupant footsteps has the potential to identify accurately the trajectory of an occupant in buildings with complex configurations, thereby providing tracking information without undermining privacy.

Measurement-based support for post-earthquake assessment of buildings

After a damaging earthquake, assessment of the residual seismic capacity is required for large parts of the building stock. Increased vulnerability of structures together with the threat of immediate aftershocks call for rapid and objective decision making. Structural identification has the potential to reduce parameter-value uncertainties of physics-based models through interpreting measurement data. Significant amounts of uncertainty are associated with the non-linear behaviour of structures during extreme events such as earthquakes. Therefore, a structural identification methodology that accommodates multiple sources of systematic modelling uncertainties is used. Error-domain model falsification (EDMF) enables structural identification through combining damage grades observed by visual inspection with fundamental frequencies that are derived from ambient vibrations. Parametric uncertainties of a hysteretic model are reduced with the two information sources in order to extrapolate the vulnerability of the building regarding future earthquakes. The applicability of the methodology is shown using measurements made on a mixed reinforced-concrete unreinforced-masonry building tested on a shaking table. Based on nonlinear time-history analyses involving single-degree-of-freedom models, EDMF leads to more precise, yet robust, vulnerability predictions of earthquake-damaged buildings when compared with prediction ranges that are obtained without data interpretation.

A model-based data-interpretation framework for post-earthquake building assessment with scarce measurement data

Recent earthquake events throughout the world have once again exposed the vulnerability of buildings with respect to earthquakes. It is unlikely and unsustainable to design and - especially in regions with low-to-moderate seismic hazard – to retrofit all buildings to remain within elastic displacement ranges during earthquakes with high return periods. Therefore, post-earthquake assessment plays a fundamental role in the resilience of cities, given the potential to reduce time between an earthquake event and the clearance for (renewed) occupancy of a building. In this paper, a framework for model-based data interpretation of measurements of earthquake-damaged structures is presented. The framework allows engineers to combine ambient-vibration measurements and visual inspection to reduce parametric uncertainty of a high-fidelity model using the error-domain model-falsification methodology. For building types that have limited stiffness contributions from non-structural elements (i.e. shear-wall buildings) and for which non-ductile failure modes (such as out-of-plane failure) can be excluded, reduction in natural frequency and damage grades derived from visual inspection provide global measurement sources for structural identification. The application of the proposed methodology to a shear-resisting building tested on a shake table illustrates that vulnerability-curve predictions provide accurate damage estimates for subsequent earthquakes with probabilities between 50% and 100% for five measured scenarios. In complete absence of baseline information regarding the initial building state and the earthquake signal, parametric uncertainty is reduced by up to 76%. This study thus demonstrates usefulness for certain building types to enhance post-seismic vulnerability predictions.

Robust model updating methodology for estimating worst-case load capacity of existing bridges

High-value infrastructure elements, such as bridges, are typically over-designed. Model updating techniques are useful for estimating the reserve load capacity (beyond safety factors) of bridges and this improves sustainability through good asset management decision making. In these approaches, measurement data are used to infer model parameter values that influence behaviour. Several approaches have been proposed for structural model updating. Population approaches, such as Bayesian updating, reflect the intrinsically ambiguous nature of diagnosis as well as a range of uncertainty sources. However, many do not consider bias in model formulations, which results in biased identification of parameter values and subsequent predictions. Also, discrete sampling methods may be too coarse to be able to identify critical parts of the population where worst case reserve capacity is determined. In this paper, a new methodology is proposed to update models of structural systems using measurements. This methodology has been employed to identify the most critical set of parameter values for the bending ultimate limit state (ULS) verification and the serviceability limit state (SLS) verification of an existing reinforced concrete bridge after gaining information from measurements. This methodology is based on the error-domain model falsification approach, in which measurements are used to falsify models rather than calibrate them. Through a full-scale case study, it is shown that the new methodology is able to provide a parameter value set that is more accurate for ULS and SLS verification than those identified with residual minimization. Furthermore, it is shown that residual minimization (traditional curve fitting) may result in unsafe estimates of reserve capacity.

Outlier-Detection Methodology for Structural Identification Using Sparse Static Measurements.

The aim of structural identification is to provide accurate knowledge of the behaviour of existing structures. In most situations, finite-element models are updated using behaviour measurements and field observations. Error-domain model falsification (EDMF) is a multi-model approach that compares finite-element model predictions with sensor measurements while taking into account epistemic and stochastic uncertainties—including the systematic bias that is inherent in the assumptions behind structural models. Compared with alternative model-updating strategies such as residual minimization and traditional Bayesian methodologies, EDMF is easy-to-use for practising engineers and does not require precise knowledge of values for uncertainty correlations. However, wrong parameter identification and flawed extrapolation may result when undetected outliers occur in the dataset. Moreover, when datasets consist of a limited number of static measurements rather than continuous monitoring data, the existing signal-processing and statistics-based algorithms provide little support for outlier detection. This paper introduces a new model-population methodology for outlier detection that is based on the expected performance of the as-designed sensor network. Thus, suspicious measurements are identified even when few measurements, collected with a range of sensors, are available. The structural identification of a full-scale bridge in Exeter (UK) is used to demonstrate the applicability of the proposed methodology and to compare its performance with existing algorithms. The results show that outliers, capable of compromising EDMF accuracy, are detected. Moreover, a metric that separates the impact of powerful sensors from the effects of measurement outliers have been included in the framework. Finally, the impact of outlier occurrence on parameter identification and model extrapolation (for example, reserve capacity assessment) is evaluated.

Population-based structural identification for reserve-capacity assessment of existing bridges

Transportation networks provide an essential contribution to addressing the needs of reliable and safe mobility in urban environments. The core of these networks is made up of infrastructure such as roads and bridges that often, have not been designed to meet current needs. Optimal management requires an accurate knowledge of how existing structures behave. This helps avoid unnecessary replacement and expensive interventions when cheaper and more sustainable alternatives are available. Structural-model updating takes advantage of measurements and more qualitative observations to identify suitable behaviour model classes and values for parameters that influence real behaviour. Error domain model falsification (EDMF) has been proposed as a robust population-based methodology to identify sets of models by comparing finite-element model predictions with measurements at sensor locations. This paper introduces a methodology, which is compatible with EDMF, to assess the reserve capacity of bridges for serviceability and ultimate limit states. A case study—the structural identification of a reinforced-concrete bridge in Singapore—illustrates the framework developed for the estimation of reserve capacity. Several analyses with increasing levels of model detail using design and updated values of relevant parameters are presented. Traffic-load specifications of design-stage codes (British Code—1978) and current codes (Eurocodes) are compared. Results show that typical conservative practices carried out during design and construction have led to an as-built reserve capacity of 60%. A large proportion of the as-built reserve capacity has been exploited to accommodate dramatically increased values of traffic-load specifications that are provided by current Singapore codes which have caused a reduction in reserve capacity to 20%. Such a reduction may be less significant in countries where code specifications have not changed as much. Finally, it is shown that advanced methods of analysis and assessment are more suitable than design-stage approaches to quantify the reserve capacity.

Comparing Structural Identification Methodologies for Fatigue Life Prediction of a Highway Bridge

Accurate measurement-data interpretation leads to increased understanding of structural behaviour and enhanced asset-management decision making. In this paper, four data-interpretation methodologies, residual minimization, traditional Bayesian model updating, modified Bayesian model updating (with an $L_\infty$-norm-based Gaussian likelihood function) and error-domain model falsification (EDMF), a method that rejects models that have unlikely differences between predictions and measurements, are compared. In the modified Bayesian model updating methodology, a correction is used in the likelihood function to account for the effect of a finite number of measurements on posterior probability-density functions. The application of these data-interpretation methodologies for condition assessment and fatigue-life prediction is illustrated on a highway steel-concrete composite bridge having four spans with a total length of 219m. A detailed 3D finite-element plate and beam model of the bridge and weigh-in-motion data are used to obtain the time-stress response at a fatigue critical location along the bridge span. The time stress-response, presented as a histogram, is compared to measured strain responses either to update prior knowledge of model parameters using residual minimization and Bayesian methodologies or to obtain candidate model instances using the EDMF methodology. It is concluded that the EDMF and modified Bayesian model updating methodologies provide robust prediction of fatigue-life compared with residual minimization and traditional Bayesian model updating in the presence of correlated non-Gaussian uncertainty. EDMF has additional advantages due to ease of understanding and applicability for practising engineers, thus enabling incremental asset-management decision making over long service lives. Finally, parallel implementations of EDMF using grid sampling have lower computations times than implementations using adaptive sampling.

Optimal Multi-Type Sensor Placement for Structural Identification by Static-Load Testing.

Assessing ageing infrastructure is a critical challenge for civil engineers due to the difficulty in the estimation and integration of uncertainties in structural models. Field measurements are increasingly used to improve knowledge of the real behavior of a structure; this activity is called structural identification. Error-domain model falsification (EDMF) is an easy-to-use model-based structural-identification methodology which robustly accommodates systematic uncertainties originating from sources such as boundary conditions, numerical modelling and model fidelity, as well as aleatory uncertainties from sources such as measurement error and material parameter-value estimations. In most practical applications of structural identification, sensors are placed using engineering judgment and experience. However, since sensor placement is fundamental to the success of structural identification, a more rational and systematic method is justified. This study presents a measurement system design methodology to identify the best sensor locations and sensor types using information from static-load tests. More specifically, three static-load tests were studied for the sensor system design using three types of sensors for a performance evaluation of a full-scale bridge in Singapore. Several sensor placement strategies are compared using joint entropy as an information-gain metric. A modified version of the hierarchical algorithm for sensor placement is proposed to take into account mutual information between load tests. It is shown that a carefully-configured measurement strategy that includes multiple sensor types and several load tests maximizes information gain.

Leak Detection of Water Supply Networks Using Error-Domain Model Falsification

AbstractPressurized freshwater fluid distribution networks are key strategic infrastructure elements. On average, 20% of water is lost by way of leaks around the world. This illustrates the need fo...

Data-Interpretation Methodologies for Non-Linear Earthquake Response Predictions of Damaged Structures

Seismic exposure of buildings presents difficult engineering challenges. The principles of seismic design involve structures that sustain damage and still protect inhabitants. Precise and accurate knowledge of the residual capacity of damaged structures is essential for informed decision-making regarding clearance for occupancy after major seismic events. Unless structures are permanently monitored, modal properties derived from ambient vibrations are most likely the only source of measurement data that is available. However, such measurement data is linearly elastic and limited to a low number of vibration modes. Structural identification using hysteretic behavior models that exclusively relies on linear measurement data is a complex inverse engineering task that is further complicated by modeling uncertainty. Three structural identification methodologies that involve probabilistic approaches to data interpretation are compared: error-domain model falsification, Bayesian model updating with traditional assumptions as well as modified Bayesian model updating. While noting the assumptions regarding uncertainty definitions, the accuracy and robustness of identification and subsequent predictions are compared. A case study demonstrates limits on non-linear parameter identification performance and identification of potentially wrong prediction ranges for inappropriate model uncertainty distributions.

An electrical network for evaluating monitoring strategies intended for hydraulic pressurized networks

This paper presents a study of similarities between electrical and hydraulic pressurized networks. The primary objective is to examine whether or not it is possible to use electrical laboratory networks measuring voltage to study leak-region detection strategies measuring flow in water-distribution networks. In this paper, the strategy used to compare the networks is error-domain model falsification, a previously developed methodology for data interpretation that combines engineering knowledge with models and data to enhance decision making. Simulation results obtained for a part of the water-supply network from the city of Lausanne are compared with an analogous electric network. The electrical network is simulated using resistors to mimic the pipes. The consequence is that the electrical model is linear. The resistance values are obtained by computing the hydraulic resistance for each pipe, given by the Hazen-Williams equation. The compatibility of the two networks is evaluated through simulations in three ways: (1) comparing flow predictions obtained by simulating several leak scenarios; (2) comparing the expected identifiability (performance) of the two networks; and (3) comparing sensor placement configurations. The analyses show that even though the models have varying characteristics of underlying physical principles (the electrical model is linear while the hydraulic model is non-linear), the results are within generally accepted engineering limits of similarity (10%). This indicates that measurements on electrical laboratory networks have the potential to illustrate the efficiency and adaptability of leak-detection methodologies for full-scale water-supply and other pressurized hydraulic networks. Finally, two electrical laboratory physical networks, including an electrical model of part of the water network in Lausanne, were constructed and used in case studies to illustrate this adaptability.

Robust system identification and model predictions in the presence of systematic uncertainty

ContextModel-based data-interpretation techniques are increasingly used to improve the knowledge of complex system behavior. Physics-based models that are identified using measurement data are generally used for extrapolation to predict system behavior under other actions. In order to obtain accurate and reliable extrapolations, model-parameter identification needs to be robust in terms of variations of systematic modeling uncertainty introduced when modeling complex systems. Approaches such as Bayesian inference are widely used for system identification. More recently, error-domain model falsification (EDMF) has been shown to be useful for situations where little information is available to define the probability density function (PDF) of modeling errors. Model falsification is a discrete population methodology that is particularly suited to knowledge intensive tasks in open worlds, where uncertainty cannot be precisely defined. ObjectiveThis paper compares conventional uses of approaches such as Bayesian inference and EDMF in terms of parameter-identification robustness and extrapolation accuracy. MethodUsing Bayesian inference, three scenarios of conventional assumptions related to inclusion of modeling errors are evaluated for several model classes of a simple beam. These scenarios are compared with results obtained using EDMF. Bayesian model class selection is used to study the benefit of posterior model averaging on the accuracy of extrapolations. Finally, ease of representation and modification of knowledge is illustrated using an example of a full-scale bridge. ResultsThis study shows that EDMF leads to robust identification and more accurate predictions than conventional applications of Bayesian inference in the presence of systematic uncertainty. These results are illustrated with a full-scale bridge. This example shows that the engineering knowledge necessary to perform parameter identification and remaining-fatigue-life predictions of a complex civil structure is easily represented by the EDMF methodology. ConclusionModel classes describing complex systems should include two components: (1) unknown physical parameters that are identified using measurements; (2) conservative modeling error estimations that cannot be represented only as uncertainties related to physical parameters. In order to obtain accurate predictions, both components need to be included in the model-class definition. This study indicates that Bayesian model class selection may lead to over-confidence in certain model classes, resulting in biased extrapolation.