Quality evaluation method for finished prefabricated beam bridges integrating fuzzy reasoning and neural network

Abstract

The present invention discloses a method for evaluating the quality of finished prefabricated beam bridges that integrates fuzzy reasoning and neural networks. The method belongs to the field of comprehensive evaluation and includes: constructing an intelligent evaluation system for the quality of finished prefabricated beam bridges, including primary and secondary indicators; using fuzzy numbers to describe quantitative and qualitative indicators and constructing a fuzzy judgment matrix; calculating an initial weight vector using the analytic hierarchy process and defuzzification techniques; calculating an improved regularization term that balances data-driven and expert priors; and using a weighted neural network to optimize and weight the parameters of each component, outputting a quality score for the finished prefabricated beam bridge. The present invention aims to construct an intelligent evaluation system for the quality of finished prefabricated beam bridges. By integrating deep learning technology, it achieves collaborative evaluation of expert knowledge and data-driven methods, improving the accuracy and robustness of the quality evaluation of finished prefabricated beam bridges and providing an efficient and reliable technical means for intelligent evaluation of the quality of prefabricated beam bridges.

Description

Technical Field

[0001] The present invention belongs to the field of comprehensive evaluation, and in particular relates to a method for evaluating the quality of finished products of prefabricated beam bridges by integrating fuzzy reasoning and neural networks. Background Art

[0002] With the continuous development of infrastructure construction, prefabricated beam bridges have been widely used in engineering practice due to their advantages such as short construction period, high engineering quality, and low environmental impact. However, the quality of the finished product is directly related to the safety and service life of the bridge, so quality evaluation has always been a key issue in project management.

[0003] Traditional quality assessment methods rely primarily on expert experience and manual testing, such as the Analytic Hierarchy Process (AHP) and the Fuzzy Comprehensive Evaluation Method (Fuzzy Comprehensive Evaluation Method). These methods determine the importance of each indicator by constructing a judgment matrix and using expert scoring to calculate a comprehensive evaluation score. However, these methods suffer from strong subjectivity, unstable results, and difficulty fully accounting for the complex relationships between indicators and data uncertainty. In recent years, the development of data-driven technologies and deep learning has led to a surge in interest in neural network-based evaluation methods. These methods can automatically learn implicit patterns in data and enable nonlinear modeling and prediction of complex systems. However, in practical engineering projects, sample data is often insufficient, and relying solely on neural networks is prone to overfitting. Furthermore, it is difficult to fully utilize the valuable experience accumulated by experts in constructing evaluation indicator systems. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a method for evaluating the quality of finished assembled beam bridges by integrating fuzzy reasoning and neural networks, comprising:

[0005] Constructing an intelligent evaluation system for the quality of finished prefabricated beam bridges, which includes primary and secondary indicators;

[0006] According to the evaluation system, fuzzy numbers are used to describe quantitative and qualitative indicators and a fuzzy judgment matrix is constructed;

[0007] According to the fuzzy judgment matrix, an initial weight vector is calculated using the analytic hierarchy process and defuzzification technology;

[0008] Calculating an improved regularization term that balances data-driven and expert priors based on the initial weight vector;

[0009] According to the improved regularization term, a weighted neural network is used to perform weight optimization and weighted summation on the parameters of each component, and a quality score of the finished product of the prefabricated beam bridge is output.

[0010] Preferably, the first-level indicators include precast concrete components, ordinary rubber bearings, skateboard rubber bearings, spherical bearings, pot bearings, anchors, unit-type multi-directional displacement comb-shaped plate bridge expansion devices, modular expansion devices, metal bellows and plastic bellows;

[0011] The secondary indicators are specific evaluation parameters under each primary indicator.

[0012] Preferably, the step of using fuzzy numbers to describe quantitative and qualitative indicators and constructing a fuzzy judgment matrix includes:

[0013] Constructing a fuzzy judgment matrix of the first-level indicators, wherein each element in the fuzzy judgment matrix uses a triangular fuzzy number to represent the importance between the indicators;

[0014] A fuzzy judgment matrix of the secondary indicators is constructed, and each element in the fuzzy judgment matrix uses a triangular fuzzy number to represent the importance between the indicators.

[0015] Preferably, the step of calculating the initial weight vector using the analytic hierarchy process and defuzzification technology includes:

[0016] Using the fuzzy geometric mean method to calculate each row in the fuzzy judgment matrix to obtain a fuzzy weight;

[0017] Defuzzifying the fuzzy weights using a gravity center method to obtain deterministic weights;

[0018] The deterministic weight is normalized to obtain an initial weight vector.

[0019] Preferably, the process of calculating the improved regularization term that balances data-driven and expert priors includes:

[0020] Construct an L1 regularization term to constrain the deviation between the weight and the initial weight;

[0021] Construct an L2 regularization term to promote the sparsity of weights;

[0022] Construct a dynamic regularization term to balance the impact of the output score on the weight optimization process;

[0023] The L1 regularization term, the L2 regularization term and the dynamic regularization term are combined to obtain an improved regularization term.

[0024] Preferably, the process of using a weighted neural network to perform weight optimization and weighted summation on the parameters of each component and outputting a quality score of the finished assembled beam bridge comprises:

[0025] Initialize the neural network parameters according to the initial weight vector and generate the current weight through the Softmax function;

[0026] Calculating a prediction score based on the current weight;

[0027] Constructing a loss function and combining it with the improved regularization term to update the network parameters through the back propagation algorithm;

[0028] Based on the optimized weights, new inputs are predicted and the quality score of the finished prefabricated beam bridge is output.

[0029] Preferably, the secondary indicators of the precast concrete components in the primary indicators include appearance, geometric dimensions, concrete compressive strength, steel bar protective layer thickness, deflection, strain and cracks.

[0030] Preferably, the secondary indicators of ordinary rubber bearings in the said first-level indicators include appearance quality, dimensional deviation, compressive elastic modulus, shear elastic modulus, shear adhesion performance, ultimate compressive strength, shear elastic modulus after aging, tangent value of rotation, internal quality and internal quality.

[0031] Preferably, the secondary indicators of the skateboard rubber bearing in the said primary indicators include appearance quality, dimensional deviation, compressive elastic modulus, ultimate compressive strength, surface friction coefficient between the skateboard and the stainless steel plate, internal quality and internal quality.

[0032] Preferably, the secondary indicators of the spherical bearing in the said primary indicators include size and deviation, appearance quality of the bearing material and bearing assembly.

[0033] Compared with the prior art, the present invention has the following advantages and technical effects:

[0034] The present invention constructs a quality evaluation system for prefabricated beam bridge products that covers key evaluation indicators such as prefabricated concrete components, ordinary rubber bearings, skateboard rubber bearings, spherical bearings, pot bearings, anchors, and unit-type multi-directional displacement comb-shaped plate bridge expansion devices. This system can accurately and comprehensively reflect the quality of the finished bridge products. At the same time, by utilizing expert knowledge to construct a fuzzy judgment matrix, and combining the hierarchical analysis method and defuzzification technology to obtain initial weights, which are then introduced into a weighted neural network for data-driven learning, it ensures that rapid convergence and accurate prediction can be achieved even when sample data is limited. This method effectively balances the advantages of expert priors and data training, improves the robustness and interpretability of the model, and provides an efficient and reliable technical means for the intelligent evaluation of the quality of prefabricated beam bridge products. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0036] Figure 1Schematic diagram of a method flow in an embodiment of the present invention. DETAILED DESCRIPTION

[0037] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0038] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0039] Example 1

[0040] like Figure 1 As shown, this embodiment provides a method for evaluating the quality of finished products of prefabricated beam bridges by integrating fuzzy reasoning and neural networks, including:

[0041] Step S1, constructing an intelligent evaluation system for the quality of finished prefabricated beam bridge products, which includes primary indicators and secondary indicators;

[0042] Step S2, using fuzzy numbers to describe quantitative and qualitative indicators and constructing a fuzzy judgment matrix;

[0043] Step S3, calculating the initial weight vector using the analytic hierarchy process and defuzzification technology;

[0044] Step S4, calculating an improved regularization term that balances data-driven and expert priors;

[0045] Step S5: Use a weighted neural network to perform weight optimization and weighted summation on the parameters of each component, and output a quality score for the finished prefabricated beam bridge.

[0046] Furthermore, the first-level indicators include precast concrete components, ordinary rubber bearings, skateboard rubber bearings, spherical bearings, pot bearings, anchors, unit-type multi-directional displacement comb-shaped plate bridge expansion devices, modular expansion devices, metal bellows and plastic bellows.

[0047] Furthermore, the first-level indicators for precast concrete components include appearance, geometric dimensions, concrete compressive strength, steel cover thickness, deflection, strain, and cracks;

[0048] The first-level indicators of ordinary rubber bearings include appearance quality, dimensional deviation, compressive elastic modulus, shear elastic modulus, shear adhesion performance, ultimate compressive strength, shear elastic modulus after aging, tangent value, internal quality (anatomy) and internal quality (peeling);

[0049] The first-level indicators of skateboard rubber bearings include appearance quality, dimensional deviation, compressive elastic modulus, ultimate compressive strength, surface friction coefficient between skateboard and stainless steel plate, internal quality (dissection) and internal quality (peeling);

[0050] The first-level indicators for spherical bearings include secondary indicators such as size and deviation, appearance quality of bearing materials, and bearing assembly;

[0051] The first-level indicators of pot-type bearings include second-level indicators such as appearance, size and bearing assembly height;

[0052] The first-level indicators of anchors include appearance (size, cracks), static load anchoring performance, cyclic load performance, fatigue load performance, Rockwell hardness, strand shrinkage, anchor mouth (including anchor pad) friction loss rate and tensioning process performance;

[0053] The first-level indicators of the unitized multi-directional displacement comb-shaped plate bridge expansion device include secondary indicators such as appearance, dimensional deviation and waterproof performance;

[0054] The first-level indicators of modular telescopic devices include secondary indicators such as tensile strength, bending performance and impact performance;

[0055] The first-level indicators of metal bellows include appearance, size, resistance to external loads (uniformly distributed loads and local transverse loads) and anti-leakage performance (after bending and after local transverse loads);

[0056] The first-level indicators of plastic corrugated pipes include appearance, ring stiffness, local lateral load, flexibility, impact resistance, specifications, tensile properties, longitudinal load, ash content, anti-aging performance, oxidation induction time, pull-out force and sealing.

[0057] Furthermore, in step S2, fuzzy numbers are used to describe quantitative and qualitative indicators, and a fuzzy judgment matrix is constructed as follows:

[0058] Step S21: Constructing a fuzzy judgment matrix of the first-level indicators

[0059] The finished product quality evaluation system of prefabricated beam bridge has a total of m first-level indicators, which are recorded as , ,…, , construct the fuzzy judgment matrix of the first-level indicators , where each element represents the importance between indicators and is represented by triangular fuzzy numbers. Fuzzy judgment matrix for:

[0060]

[0061] Among them, the diagonal elements (1,1,1) indicate that the indicator is equally important to itself; the off-diagonal elements Indicates expert opinions on indicators Relative to the indicator fuzzy evaluation.

[0062] Step S22: Constructing the fuzzy judgment matrix of the secondary indicators

[0063] For each first-level indicator (i=1,2,…,m), below which there are Secondary indicators, denoted as , ,…, The second-level indicators under the first-level indicator i, the fuzzy judgment matrix for:

[0064]

[0065] Among them, the diagonal elements (1,1,1) indicate that the indicator is equally important to itself; the off-diagonal elements Indicates expert opinions on indicators Relative to the indicator fuzzy evaluation.

[0066] Furthermore, in step S3, the initial weight vector is calculated using the analytic hierarchy process and defuzzification technology as follows:

[0067] The fuzzy geometric mean method is used to calculate each row in the fuzzy judgment matrix of the first and second level indicators.

[0068] Fuzzy weight of each first-level indicator :

[0069]

[0070] For each first-level indicator The j-th secondary index under , its fuzzy weight for:

[0071]

[0072] The center of gravity method is used to defuzzify the fuzzy weights of the first-level indicators and the second-level indicators, and convert the fuzzy weights into deterministic weights.

[0073] The i-th first-level indicator The deblurring formula is:

[0074] ,

[0075] in, , , Respectively The lower, median, and upper bounds of .

[0076] The i-th first-level indicator The deblurring formula is:

[0077] ,

[0078] in, , , Respectively The lower, median, and upper bounds of .

[0079] The certainty weights of the first-level and second-level indicators are normalized to meet the following requirements:

[0080] ,

[0081] The comprehensive initial weight of each secondary indicator is composed of two levels of weights, namely:

[0082] ,

[0083] Calculate the initial weight vector based on the comprehensive initial weight of the secondary indicators:

[0084]

[0085] Where N is the total number of all secondary indicators.

[0086] Furthermore, in step S4, an improved regularization term that balances data-driven and expert priors is calculated as follows:

[0087] In order to balance data-driven learning and expert prior knowledge in the neural network training process and prevent the model from overfitting, an improved regularization term is constructed. . Improve the regularization term It consists of three parts: the L1 regularization term is used to penalize the deviation between the weight and the initial weight during the optimization process; the L2 regularization term is used to promote the sparsity of the weight; and the dynamic regularization term is used to balance the impact of the output score on the weight optimization process.

[0088] Step S41, construct L1 regularization term:

[0089]

[0090] in, is the L1 regularization hyperparameter.

[0091] Step S42, constructing an L2 regularization term:

[0092]

[0093] in, is the L2 regularization hyperparameter.

[0094] Step S43, construct a dynamic regularization term:

[0095]

[0096] in, is a dynamic regularization hyperparameter, and y is the quality score of the prediction.

[0097] Step S44, construct an improved regularization term:

[0098]

[0099] Furthermore, in step S5, the weighted neural network is used to perform weight optimization and weighted summation on the parameters of each component, and the quality score of the finished assembled beam bridge is output as follows:

[0100] Step S51, generating the current weight;

[0101] The input vector of the quality evaluation system for prefabricated beam bridge products is:

[0102]

[0103] According to the initial weight vector , use logarithmic changes to initialize the neural network parameters:

[0104] ,

[0105] Generate the current weight through the Softmax function:

[0106] ,

[0107] Step S52, calculating the output layer;

[0108] Based on the generated weight , the prediction score Q is calculated based on the weighted sum:

[0109]

[0110] Step S53: construct loss function and update parameters

[0111] The original loss function is the mean square error:

[0112]

[0113] in, and They represent the predicted score and true score of the kth sample, respectively, and m is the number of samples.

[0114] Combined with the improved regularization term constructed in step S4 , the total loss function is:

[0115]

[0116] Using the back-propagation algorithm, update the network parameters by gradient descent , the update formula is:

[0117] ,

[0118] in, is the learning rate.

[0119] The calculation of the gradient relies on the chain rule:

[0120]

[0121] Using the properties of the Softmax function,

[0122] , ,

[0123] Iteratively update to the total loss function Convergence, get the optimized parameters and corresponding weights .

[0124] Step S54: Calculate the final score

[0125] Using the optimized weights Make predictions for the new input X and output the final score Q of the finished quality of the prefabricated beam bridge:

[0126]

[0127] The indicator classification is shown in Table 1 and Table 2-3:

[0128] Table 1

[0129]

[0130] Table 2

[0131]

[0132] Table 3

[0133]

[0134] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for evaluating the quality of finished products of prefabricated beam bridges by integrating fuzzy reasoning and neural networks, characterized in that: include: Constructing an intelligent evaluation system for the quality of finished prefabricated beam bridges, which includes primary and secondary indicators; According to the evaluation system, fuzzy numbers are used to describe quantitative and qualitative indicators and a fuzzy judgment matrix is constructed; According to the fuzzy judgment matrix, an initial weight vector is calculated using the analytic hierarchy process and defuzzification technology; Calculating an improved regularization term that balances data-driven and expert priors based on the initial weight vector; According to the improved regularization term, a weighted neural network is used to perform weight optimization and weighted summation on the parameters of each component, and a quality score of the finished assembled beam bridge is output; The step of calculating the initial weight vector by using the analytic hierarchy process and defuzzification technology includes: Calculating each row in the fuzzy judgment matrix using a fuzzy geometric mean method to obtain a fuzzy weight; Defuzzifying the fuzzy weights using a gravity center method to obtain deterministic weights; Normalizing the deterministic weight to obtain an initial weight vector; The process of calculating the improved regularization term that balances data-driven and expert priors includes: Construct an L1 regularization term to constrain the deviation between the weight and the initial weight; Construct an L2 regularization term to promote the sparsity of weights; Construct a dynamic regularization term to balance the impact of the output score on the weight optimization process; Combining the L1 regularization term, the L2 regularization term, and the dynamic regularization term to obtain an improved regularization term; The process of using a weighted neural network to perform weight optimization and weighted summation on the parameters of each component and outputting a quality score of the assembled beam bridge product includes: Initialize the neural network parameters according to the initial weight vector and generate the current weight through the Softmax function; Calculating a prediction score based on the current weight; Constructing a loss function and combining it with the improved regularization term to update the network parameters through the back propagation algorithm; Based on the optimized weights, new inputs are predicted and the quality score of the finished prefabricated beam bridge is output.

2. The method according to claim 1, characterized in that The first-level indicators include precast concrete components, ordinary rubber bearings, skateboard rubber bearings, spherical bearings, pot bearings, anchors, unit-type multi-directional displacement comb-shaped plate bridge expansion devices, modular expansion devices, metal bellows and plastic bellows; The secondary indicators are specific evaluation parameters under each primary indicator.

3. The method according to claim 1, characterized in that The steps of using fuzzy numbers to describe quantitative and qualitative indicators and constructing a fuzzy judgment matrix include: Constructing a fuzzy judgment matrix of the first-level indicators, wherein each element in the fuzzy judgment matrix uses a triangular fuzzy number to represent the importance between the indicators; A fuzzy judgment matrix of the secondary indicators is constructed, and each element in the fuzzy judgment matrix uses a triangular fuzzy number to represent the importance between the indicators.

4. The method according to claim 1, wherein The secondary indicators of the precast concrete components in the first-level indicators include appearance, geometric dimensions, concrete compressive strength, steel bar protective layer thickness, deflection, strain and cracks.

5. The method according to claim 1, wherein The secondary indicators of ordinary rubber bearings in the said first-level indicators include appearance quality, dimensional deviation, compressive elastic modulus, shear elastic modulus, shear adhesion performance, ultimate compressive strength, shear elastic modulus after aging, tangent value of rotation, internal quality and internal quality.

6. The method according to claim 1, characterized in that The secondary indicators of the skateboard rubber bearing in the said first-level indicators include appearance quality, dimensional deviation, compressive elastic modulus, ultimate compressive strength, surface friction coefficient between the skateboard and the stainless steel plate, internal quality and internal quality.

7. The method according to claim 1, characterized in that The secondary indicators of the spherical bearing in the said primary indicators include size and deviation, appearance quality of the bearing material and bearing assembly.

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