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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Confidence Intervals01:21

Confidence Intervals

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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
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Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
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Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

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In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
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Estimation of Prediction Intervals for Performance Assessment of Building Using Machine Learning.

Khurram Shabbir1,2, Muhammad Umair1, Sung-Han Sim1

  • 1Department of Global Smart City, Sungkyunkwan University, Suwon 16419, Republic of Korea.

Sensors (Basel, Switzerland)
|July 13, 2024
PubMed
Summary
This summary is machine-generated.

This study uses artificial neural networks and a novel QD-LUBE method for accurate seismic performance assessment. It enhances building resilience and disaster management through reliable uncertainty quantification in structural health monitoring.

Keywords:
QD-LUBEmachine learning in SHMprediction intervaluncertainty quantification in SHM

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Area of Science:

  • Structural Engineering
  • Artificial Intelligence
  • Earthquake Engineering

Background:

  • Uncertainty quantification is crucial for seismic performance assessment and structural health monitoring (SHM).
  • Traditional methods for damage assessment often lack robustness in quantifying uncertainties.
  • Long-term ground motion effects on buildings require advanced analytical tools.

Purpose of the Study:

  • To estimate prediction intervals (PI) for seismic performance assessment using artificial neural networks (ANN).
  • To implement the quality-driven lower upper bound estimation (QD-LUBE) for global probabilistic damage assessment.
  • To enhance the reliability and robustness of post-earthquake assessments and early warning systems.

Main Methods:

  • Application of artificial neural networks (ANN) for prediction interval estimation.
  • Utilization of the distribution-free quality-driven lower upper bound estimation (QD-LUBE) method.
  • Validation through fragility curve analysis for structural damage assessment.

Main Results:

  • The QD-LUBE method provides accurate uncertainty quantification, outperforming traditional methods like bootstrap.
  • The distribution-free machine learning model demonstrates high reliability in seismic risk assessment.
  • Fragility curve analysis confirms the efficacy of the proposed methods in assessing structural damage.

Conclusions:

  • The study highlights the effectiveness of ANN and QD-LUBE in improving seismic performance assessment.
  • Enhanced building resilience and disaster management strategies are achievable through advanced SHM.
  • The research offers comprehensive insights for structural damage mitigation and early warning systems.