Concrete Prestress Evaluation via Gaussian Mixture Model

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Solution Overview

Problem

Existing evaluation methods for prestress concrete structures fail to accurately characterize the prestress performance due to ignoring uncertainty and non-uniformity in effective prestress distribution, leading to inadequate assessment of structural service performance and safety.

Innovation Solution

A method is developed to estimate the real-time probability distribution of effective prestress in concrete structures using sequential sampling theory, Monte Carlo simulations, and Gaussian mixture models, allowing for the calculation of evaluation characteristic values that account for uncertainty and non-uniformity.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If the average value of detected data is used as the evaluation characteristic value, then the evaluation process is simple, but the uncertainty and non-uniformity of effective prestress distribution are ignored, leading to inaccurate characterization of prestress performance

Engineering Contradiction:

Improveevaluation process complexityVSAvoidprestress performance characterization accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent transforms the evaluation from using a single average value parameter to using a comprehensive probability distribution model that incorporates multiple parameters (mean, variance, skewness, kurtosis) to fully characterize the effective prestress distribution, thereby improving measurement precision while maintaining reasonable evaluation complexity

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces the simple statistical averaging method with a Monte Carlo simulation-based probability distribution model, substituting a deterministic calculation approach with a probabilistic simulation approach that better captures the uncertainty and non-uniformity of prestress distribution

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Quantity of substance

If detection technology is applied to obtain prestress data, then some effective prestress information can be obtained, but the data is limited by structure type and detection efficiency, resulting in scattered data from a small amount of rebars

Engineering Contradiction:

Improveamount of prestress data obtainedVSAvoiddetection efficiency and structure type limitation

Core Design Contradiction:

Quantity of substanceVSDifficulty of detecting and measuring

Solution Approach 1:

The patent performs preliminary Monte Carlo simulations using design parameters and influence factors to establish a theoretical probability distribution model before actual detection, allowing the system to predict prestress distribution characteristics and guide the detection process to minimize the number of required measurements

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces a probability distribution model as an intermediary between the limited detection data and the overall prestress evaluation, using the model to extrapolate from scattered rebar measurements to characterize the entire prestress system, thereby overcoming the limitation of small sample size

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If sequential sampling theory is used to estimate real-time probability distribution, then accurate evaluation characteristic values can be calculated, but the evaluation method becomes more complex

Engineering Contradiction:

Improveevaluation characteristic value accuracyVSAvoidevaluation method complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent implements sequential sampling that stops when a predetermined confidence level is achieved, performing only the necessary number of simulations rather than exhaustive sampling, thereby obtaining accurate evaluation characteristic values while controlling the complexity and computational effort

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The patent uses feedback from the sequential sampling process to dynamically adjust the evaluation, where each sampling round provides information that refines the probability distribution estimate, allowing the method to converge to accurate results efficiently without requiring complex predetermined sampling plans

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12283354B2Method for estimating effective prestress and calculating evaluation characteristic value of concrete structure

Publication Date: 2025.04.22 CENT RES INST OF BUILDING & CONSTR CO LTD MCC GRP

AI summary

A concrete structure effective prestress estimation and evaluation characteristic value calculation method which includes calculating effective prestress probability distribution of a concrete structure prestress rebar, establishing a Gaussian mixture model of effective prestress of the concrete structure, carrying out normalization processing and normal significance judgment on the Gaussian mixture model, sampling and estimating the effective prestress probability distribution of the structure with normal distribution in theoretical distribution, calculating effective prestress evaluation characteristic values of the components under the normal distribution condition, grouping processing and normal significance judging of the Gaussian mixture model, sampling and estimating the structural effective prestress probability distribution of the N-sub-distribution Gaussian mixture model by theoretical distribution, calculating effective prestress evaluation characteristic values of the components under the condition of the N-sub-distribution Gaussian mixture model.