Effects of Prediction Error Bias on Model Calibration and Response Prediction of a 10-Story Building

2016 ◽  
pp. 279-291 ◽  
Author(s):  
Iman Behmanesh ◽  
Seyedsina Yousefianmoghadam ◽  
Amin Nozari ◽  
Babak Moaveni ◽  
Andreas Stavridis
Keyword(s):  
Prediction Error ◽  
Model Calibration ◽  
Response Prediction ◽  
Error Bias ◽  
Story Building

scholarly journals Testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models

2016 ◽  
Vol 20 (5) ◽  
pp. 1925-1946 ◽  
Author(s):  
Nikolaj Kruse Christensen ◽  
Steen Christensen ◽  
Ty Paul A. Ferre
Keyword(s):  
Electrical Resistivity ◽  
Hydraulic Conductivity ◽  
Prediction Error ◽  
Model Calibration ◽  
Hydrologic Model ◽  
Geophysical Data ◽  
Model Parameters ◽  
Calibration Data ◽  
Groundwater Model ◽  
And Inversion

Abstract. In spite of geophysics being used increasingly, it is often unclear how and when the integration of geophysical data and models can best improve the construction and predictive capability of groundwater models. This paper uses a newly developed HYdrogeophysical TEst-Bench (HYTEB) that is a collection of geological, groundwater and geophysical modeling and inversion software to demonstrate alternative uses of electromagnetic (EM) data for groundwater modeling in a hydrogeological environment consisting of various types of glacial deposits with typical hydraulic conductivities and electrical resistivities covering impermeable bedrock with low resistivity (clay). The synthetic 3-D reference system is designed so that there is a perfect relationship between hydraulic conductivity and electrical resistivity. For this system it is investigated to what extent groundwater model calibration and, often more importantly, model predictions can be improved by including in the calibration process electrical resistivity estimates obtained from TEM data. In all calibration cases, the hydraulic conductivity field is highly parameterized and the estimation is stabilized by (in most cases) geophysics-based regularization. For the studied system and inversion approaches it is found that resistivities estimated by sequential hydrogeophysical inversion (SHI) or joint hydrogeophysical inversion (JHI) should be used with caution as estimators of hydraulic conductivity or as regularization means for subsequent hydrological inversion. The limited groundwater model improvement obtained by using the geophysical data probably mainly arises from the way these data are used here: the alternative inversion approaches propagate geophysical estimation errors into the hydrologic model parameters. It was expected that JHI would compensate for this, but the hydrologic data were apparently insufficient to secure such compensation. With respect to reducing model prediction error, it depends on the type of prediction whether it has value to include geophysics in a joint or sequential hydrogeophysical model calibration. It is found that all calibrated models are good predictors of hydraulic head. When the stress situation is changed from that of the hydrologic calibration data, then all models make biased predictions of head change. All calibrated models turn out to be very poor predictors of the pumping well's recharge area and groundwater age. The reason for this is that distributed recharge is parameterized as depending on estimated hydraulic conductivity of the upper model layer, which tends to be underestimated. Another important insight from our analysis is thus that either recharge should be parameterized and estimated in a different way, or other types of data should be added to better constrain the recharge estimates.

scholarly journals Response prediction of multi-story building using backpropagation neural networks method

2019 ◽  
Vol 276 ◽  
pp. 01011
Author(s):  
Reni Suryanita ◽  
Harnedi Maizir ◽  
Yohannes Firzal ◽  
Hendra Jingga ◽  
Enno Yuniarto
Keyword(s):  
Structural Analysis ◽  
Structural Response ◽  
Building Design ◽  
Response Prediction ◽  
Soil Condition ◽  
Displacement Velocity ◽  
Floor Plan ◽  
Backpropagation Neural Network ◽  
Seismic Location ◽  
Story Building

The active ground motion in Indonesia might cause a catastrophic collapse of the building which leads to casualties and property damages. Therefore, it is imperative to design the structural response of building against seismic hazard correctly. Seismic-resistant building design process requires structural analysis to be performed to obtain the necessary building responses. However, the structural analysis could be difficult and time-consuming. This study aims to predict the structural response includes displacement, velocity, and acceleration of multi-story building with the fixed floor plan using Backpropagation Neural Network (BPNN) method. By varying the building height, soil condition, and seismic location in 47 cities in Indonesia, 6345 datasets were obtained and fed into the BPNN model for the learning process. The trained BPNN is capable of predicting the displacement, velocity, and acceleration responses with up to 96% of the expected rate.

scholarly journals A framework for testing the use of electric and electromagnetic data to reduce the prediction error of groundwater models

2015 ◽  
Vol 12 (9) ◽  
pp. 9599-9653 ◽  
Author(s):  
N. K. Christensen ◽  
S. Christensen ◽  
T. P. A. Ferre
Keyword(s):  
Hydraulic Conductivity ◽  
Tikhonov Regularization ◽  
Prediction Error ◽  
Model Calibration ◽  
Groundwater Modeling ◽  
Hydrologic Model ◽  
Geophysical Data ◽  
Model Parameters ◽  
Calibration Data ◽  
And Inversion

Abstract. Despite geophysics is being used increasingly, it is still unclear how and when the integration of geophysical data improves the construction and predictive capability of groundwater models. Therefore, this paper presents a newly developed HYdrogeophysical TEst-Bench (HYTEB) which is a collection of geological, groundwater and geophysical modeling and inversion software wrapped to make a platform for generation and consideration of multi-modal data for objective hydrologic analysis. It is intentionally flexible to allow for simple or sophisticated treatments of geophysical responses, hydrologic processes, parameterization, and inversion approaches. It can also be used to discover potential errors that can be introduced through petrophysical models and approaches to correlating geophysical and hydrologic parameters. With HYTEB we study alternative uses of electromagnetic (EM) data for groundwater modeling in a hydrogeological environment consisting of various types of glacial deposits with typical hydraulic conductivities and electrical resistivities covering impermeable bedrock with low resistivity. It is investigated to what extent groundwater model calibration and, often more importantly, model predictions can be improved by including in the calibration process electrical resistivity estimates obtained from TEM data. In all calibration cases, the hydraulic conductivity field is highly parameterized and the estimation is stabilized by regularization. For purely hydrologic inversion (HI, only using hydrologic data) we used Tikhonov regularization combined with singular value decomposition. For joint hydrogeophysical inversion (JHI) and sequential hydrogeophysical inversion (SHI) the resistivity estimates from TEM are used together with a petrophysical relationship to formulate the regularization term. In all cases, the regularization stabilizes the inversion, but neither the HI nor the JHI objective function could be minimized uniquely. SHI or JHI with regularization based on the use of TEM data produced estimated hydraulic conductivity fields that bear more resemblance to the reference fields than when using HI with Tikhonov regularization. However, for the studied system the resistivities estimated by SHI or JHI must be used with caution as estimators of hydraulic conductivity or as regularization means for subsequent hydrological inversion. Much of the lack of value of the geophysical data arises from a mistaken faith in the power of the petrophysical model in combination with geophysical data of low sensitivity, thereby propagating geophysical estimation errors into the hydrologic model parameters. With respect to reducing model prediction error, it depends on the type of prediction whether it has value to include geophysical data in the model calibration. It is found that all calibrated models are good predictors of hydraulic head. When the stress situation is changed from that of the hydrologic calibration data, then all models make biased predictions of head change. All calibrated models turn out to be a very poor predictor of the pumping well's recharge area and groundwater age. The reason for this is that distributed recharge is parameterized as depending on estimated hydraulic conductivity of the upper model layer which tends to be underestimated. Another important insight from the HYTEB analysis is thus that either recharge should be parameterized and estimated in a different way, or other types of data should be added to better constrain the recharge estimates.

scholarly journals Author response: Prediction error induced motor contagions in human behaviors

2018 ◽  
Author(s):  
Tsuyoshi Ikegami ◽  
Gowrishankar Ganesh ◽  
Tatsuya Takeuchi ◽  
Hiroki Nakamoto
Keyword(s):  
Prediction Error ◽  
Response Prediction ◽  
Author Response ◽  
Human Behaviors

scholarly journals Entropy Coder for Audio Signals

2015 ◽  
Vol 61 (2) ◽  
pp. 219-224 ◽  
Author(s):  
Grzegorz Ulacha ◽  
Ryszard Stasinski
Keyword(s):  
Bias Correction ◽  
Prediction Error ◽  
Ordinary Least Squares ◽  
Prediction Errors ◽  
Mean Square ◽  
Least Mean Square ◽  
Audio Signals ◽  
Adaptation Algorithm ◽  
Lossless Coding ◽  
Error Bias

Abstract In the paper an effective entropy coder designed for coding of prediction errors of audio signals is presented. The coder is implemented inside a greater structure, which signal modeling part is a lossless coding backward adaptation algorithm consisting of cascaded Ordinary Least Squares (OLS), three Normalized Least Mean Square (NLMS), and prediction error bias correction sections. The technique performance is compared to that of four other lossless codecs, including MPEG-4 Audio Lossless (ALS) one, and it is shown that indeed, on the average the new method is the best. The entropy coder is an advanced context adaptive Golomb one followed by two context adaptive arithmetic coders.

scholarly journals Enhancing Machine Learning Models With Prior Physical Knowledge to Aid in VIV Response Prediction

2021 ◽  
Author(s):  
Leixin Ma ◽  
Themistocles L. Resvanis ◽  
J. Kim Vandiver
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Reynolds Number ◽  
Prediction Error ◽  
Prediction Models ◽  
Response Prediction ◽  
Vibration Response ◽  
Response Amplitude ◽  
Physical Knowledge ◽  
Input Parameters

Abstract Practical engineering prediction models for flow-induced vibration are needed in the design of structures in the ocean. Research has shown that structural vibration response may be influenced by a large number of physical input parameters, such as damping and Reynolds number. Practical response prediction tools used in design are inevitably a compromise between complexity and simplicity of use. Modern machine learning tools may be used to identify which input parameters are most important. Standard machine learning techniques enable the researcher to compile a list of the most important input parameters, ranked or ordered by the effect of each on the prediction error of the model. When all inputs are treated as equals, blind application of machine learning may lead to predictions that are inconsistent with prior physical knowledge. To address this problem, we conducted a parameter selection process using a prior knowledge-based, trend-informed neural network architecture. This approach was used to identify features important to the prediction of the cross-flow vibration response amplitude of long flexible cylinders, given the known prior effect of Reynolds number and damping. The model balances the usual goal of minimizing the model prediction error, but doing so in a manner that closely follows the extensive knowledge we have of the influence of Reynolds number and damping parameter on response. The resulting neural network model was able to reveal additional insights, including the role of mode number shifting, mode dominance and travelling waves in the regulation of VIV response amplitude.

Robust Resource Allocation for Calibration and Validation Tests

2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Chenzhao Li ◽  
Sankaran Mahadevan
Keyword(s):  
Resource Allocation ◽  
Model Calibration ◽  
Optimization Problem ◽  
Simulated Annealing Algorithm ◽  
Synthetic Data ◽  
Response Prediction ◽  
System Response ◽  
Consistent System ◽  
Calibration And Validation ◽  
Validation Tests

Model calibration and validation are two activities in system model development, and both of them make use of test data. Limited testing budget creates the challenge of test resource allocation, i.e., how to optimize the number of calibration and validation tests to be conducted. Test resource allocation is conducted before any actual test is performed, and therefore needs to use synthetic data. This paper develops a test resource allocation methodology to make the system response prediction “robust” to test outcome, i.e., insensitive to the variability in test outcome; therefore, consistent system response predictions can be achieved under different test outcomes. This paper analyzes the uncertainty sources in the generation of synthetic data regarding different test conditions, and concludes that the robustness objective can be achieved if the contribution of model parameter uncertainty in the synthetic data can be maximized. Global sensitivity analysis (Sobol’ index) is used to assess this contribution, and to formulate an optimization problem to achieve the desired consistent system response prediction. A simulated annealing algorithm is applied to solve this optimization problem. The proposed method is suitable either when only model calibration tests are considered or when both calibration and validation tests are considered. Two numerical examples are provided to demonstrate the proposed approach.

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