Falsifiability

Summary

Falsifiability is a deductive standard of evaluation of scientific theories and hypotheses, introduced by the philosopher of science Karl Popper in his book The Logic of Scientific Discovery (1934). A theory or hypothesis is falsifiable (or refutable) if it can be logically contradicted by an empirical test. Popper proposed falsifiability as the cornerstone solution to both the problem of induction and the problem of demarcation. He insisted that, as a logical criterion, falsifiability is distinct from the related concept "capacity to be proven wrong" discussed in Lakatos' falsificationism. Even being a logical criterion, its purpose is to make the theory predictive and testable, and thus useful in practice. Popper contrasted falsifiability to the intuitively similar concept of verifiability that was then current in logical positivism. His argument goes that the only way to verify a claim such as "All swans are white" would be if one could theoretically observe all swans, which is

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https://en.wikipedia.org/wiki/Falsifiability

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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.

2018

Hierarchical sensor placement using joint entropy and the effect of modeling error

Maria Papadopoulou, Ian Smith

Good prediction of the behavior of wind around buildings improves designs for natural ventilation in warm climates. However wind modeling is complex, predictions are often inaccurate due to the large uncertainties in parameter values. The goal of this work is to enhance wind prediction around buildings using measurements through implementing a multiple-model system-identification approach. The success of system-identification approaches depends directly upon the location and number of sensors. Therefore, this research proposes a methodology for optimal sensor configuration based on hierarchical sensor placement involving calculations of prediction-value joint entropy. Computational Fluid Dynamics (CFD) models are generated to create a discrete population of possible wind-flow predictions, which are then used to identify optimal sensor locations. Optimal sensor configurations are revealed using the proposed methodology and considering the effect of systematic and spatially distributed modeling errors, as well as the common information between sensor locations. The methodology is applied to a full-scale case study and optimum configurations are evaluated for their ability to falsify models and improve predictions at locations where no measurements have been taken. It is concluded that a sensor placement strategy using joint entropy is able to lead to predictions of wind characteristics around buildings and capture short-term wind variability more effectively than sequential strategies, which maximize entropy.

MDPI AG2014

Evaluating predictive performance of sensor configurations in wind studies around buildings

Maria Papadopoulou, Ian Smith

A great challenge associated with urban growth is to design for energy efficient and healthy built environments. Exploiting the potential for natural ventilation in buildings might improve pedestrian comfort and lower cooling loads, particularly in warm and tropical climates. As a result, predicting wind behavior around naturally ventilated buildings has become important and one of the most common prediction approaches is computational fluid dynamics (CFD) simulation. While accurate wind prediction is essential, simulation is complex and predictions are often inconsistent with field measurements. Discrepancies are due to the large uncertainties associated with modeling assumptions, as well as the high spatial and temporal climatic variability that influences sensor data. This paper proposes metrics to estimate the expected predictive performance of sensor configurations and assesses their usefulness in improving simulation predictions. The evaluations are based on the premise that measurement data are best used for falsifying model instances whose predictions are inconsistent with the data. The potential of the predictive performance metrics is demonstrated using full-scale high-rise buildings in Singapore. The metrics are applied to assess previously proposed sensor configurations. Results show that the performance metrics successfully evaluate the robustness of sensor configurations with respect to reducing uncertainty of wind predictions at other unmeasured locations. (C) 2016 Elsevier Ltd. All rights reserved.

Elsevier2016