Multi-objective intelligent optimization method for cable force of big data driven cable-stayed bridge in construction stage

Abstract

The invention discloses a multi-objective intelligent optimization method for cable force of a big-data-driven cable-stayed bridge in a construction stage, and relates to the technical field of intelligent optimization of bridge construction. The method sequentially comprises the following steps: determining cable force in an initial construction stage; generating a cable force set in an initial construction stage; performing normal analysis on the finite element model to form a data set; obtaining a possible bipartite point subset; determining an optimal bisection point; constructing a prediction function model; obtaining a rejection degree matrix; cable force sets in the construction stage are graded; acquiring a density operator matrix; a # imgabs0 # generation construction stage cable force set is generated; and acquiring a cable force set at the optimal construction stage. Through big data analysis and an intelligent optimization algorithm, in combination with the multi-objective optimization requirement of the construction stage, the complexity of the construction environment, the material performance and the mechanical behavior is comprehensively considered, and the construction precision and the finished bridge quality of the large-span cable-stayed bridge can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent optimization of bridge construction, and in particular to a multi-objective intelligent optimization method for cable forces in the construction phase of a cable-stayed bridge driven by big data. Background Art

[0002] As an important structural form of modern bridges, cable-stayed bridges are widely used in long-span bridge projects due to their superior stress-bearing performance and economy. However, the construction process of cable-stayed bridges is extremely complicated, especially the distribution and adjustment of cable force, which directly affects the stress distribution, linear control and construction safety of the structure, and is the decisive factor in the overall structural state of the bridge. Therefore, the optimization of cable force during the construction phase has become one of the core issues that need to be urgently solved in the construction of cable-stayed bridges.

[0003] At present, the optimization of cable forces during the construction of cable-stayed bridges mainly relies on methods such as finite element analysis and iterative calculation. Although these methods can meet some engineering needs, they usually assume that the construction conditions are deterministic, ignoring the randomness of the construction environment and structural nonlinearity, which affects the safety and performance of the bridge. In addition, the construction stage involves multiple interrelated goals such as stress balance and deformation control. Traditional single-objective optimization methods are difficult to achieve global coordination under multi-objective constraints, especially the collaborative optimization of large-span bridges is more complicated. With the development of big data technology and intelligent optimization algorithms, bridge construction optimization has ushered in a new opportunity. Big data analysis can capture the mechanical behavior characteristics during the construction process, while intelligent optimization algorithms can obtain effective solutions based on multi-objective balance. However, the application of these technologies in cable-stayed bridge construction is still imperfect, and the lack of a systematic method makes it difficult to achieve the best results in multi-objective optimization during the construction stage.

[0004] In summary, the existing technology has significant defects in optimizing cable force during the construction of cable-stayed bridges, including insufficient consideration of the randomness of the construction environment and mechanical behavior, incomplete optimization objectives, and low optimization efficiency, which limits the intelligence and precision level of the cable-stayed bridge construction process. Therefore, it is urgent to propose an intelligent and reliable construction optimization method to comprehensively improve the scientificity and reliability of cable force distribution during the construction stage of cable-stayed bridges. Summary of the invention

[0005] In order to address the shortcomings of the background technology, the present invention provides a big data driven multi-objective intelligent optimization method for cable force in the construction stage of a cable-stayed bridge. Through big data analysis and intelligent optimization algorithm, combined with the multi-objective optimization needs of the construction stage, it comprehensively considers the complexity of the construction environment, material properties and mechanical behavior, and can effectively improve the construction accuracy and bridge quality of large-span cable-stayed bridges.

[0006] To achieve the above purpose, the present invention adopts the following technical scheme: a big data driven cable force multi-objective intelligent optimization method in the construction stage of a cable-stayed bridge, comprising:

[0007] Step 1: Determination of cable force during initial construction phase

[0008] The finite element model of the cable-stayed bridge is established. There are Π batches of tensioning during the construction process. The initial cable force S of each batch of tensioning is obtained through reverse dismantling simulation. 0 n , n=1,2,...,Π-1,Π, the cable force matrix in the initial construction stage is expressed as follows:

[0009] S 0 =[S 0 1 S 0 2 …S 0 Π-1 S 0 Π ] T ;

[0010] Step 2: Generation of cable force sets during the initial construction phase

[0011] For the initial construction stage, the cable force aggregate The generation of cable force is set to [-ΔS 0 n ,ΔS 0 n ], randomly select the cable force change value Δs 0 n(N) , and obtain the cable force change matrix Δs 0 (N) It is expressed as follows:

[0012] Δs 0 (N) =[Δs 0 1(N) Δs 0 2(N) …Δs 0 Π-1 ( N) Δs 0 Π(N) ] T

[0013] Then we can get the cable force set M in the initial construction stage: 0 It is expressed as follows:

[0014]

[0015] In the formula, Δs 0 n(N) ~U([-ΔS 0 n ,ΔS 0 n]), ΔS 0 n =0.1×S 0 n , N={1,2,...,499,500}, M 0 N =S 0 +Δs 0 (N) , C 0 n(N) =S 0 n +Δs 0 n(N) ;

[0016] Step 3: Finite element model analysis to form a data set

[0017] Use each initial construction stage cable force set individual Perform forward analysis to obtain the bridge cable force matrix A (1) N and the bridge anchor point elevation matrix A (2) N ;

[0018] M 0 1 ,M 0 2 ,...,M 0 499 ,M 0 500 The corresponding A (t) 1 ,A (t) 2 ,...,A (t) 499 ,A (t) 500 As the original data set, t = 1, 2, repeatedly randomly extract all individuals from the original data set with replacement Recorded as Composed training set X s , corresponding to A (t) N A (t) Ns Form the training subset Y (t) s , suppose that the and the corresponding A (t) N There are L pairs and they are represented by M Test φN and A Test(t) φN They are composed of test sets X Tests With the test subset Y Test(t) s , φ=1,2,...L-1,L, which is expressed as follows:

[0019]

[0020] Y (t) Ns =[A (t) 1s A (t) 2s …A (t) 499s A (t) 500s ]

[0021]

[0022] Y Test(t) s =[A Test(t) 1s A Test(t) 2s …A Test(t) L-1s A Test(t) Ls ];

[0023] Step 4: Obtaining a subset of possible binary points

[0024] Set binary features For each training set X s As a binary node Perform binary division, and each binary node is represented as P indicates the level of the node, L P Indicates the sequence number of the node in this layer;

[0025] For a binary node from In the random sampling, 5 different binary features are selected to obtain feature subsets It is expressed as follows:

[0026]

[0027] Then we get the corresponding characteristic tension subset It is expressed as follows:

[0028]

[0029] Subset the feature Arrange each row in order of size and calculate the middle value of two adjacent elements in each row as a possible bisection point w={1,2,3,4,5},d={1,2,...,498,499},get the possible bisection point subset It is expressed as follows:

[0030]

[0031] Step 5: Determine the optimal bisection point

[0032] For possible bisection points In a binary node, if M 0 Ns In but If M 0 Ns In but Thus we get and Medium 0 Ns The corresponding A (t) Ns The verification matrix is ​​constructed separately and

[0033] Will and A in (t) Ns A (t)left δs With A (t)right ζs , δ=1,2,...m-1,m, ζ=1,2,...n-1,n, then A (t)left δs and A (t)right ζs The average values ​​are and It is expressed as follows:

[0034]

[0035] Then you can get A (t)left δs and and A (t)right ζs and The errors are as well as Since t=1,2, it can be divided into two cases as follows:

[0036]

[0037] Possible Equinox The corresponding weighted error It is expressed as follows:

[0038]

[0039] Traverse all possible bisection points When the corresponding weighted error When is the minimum, take the possible bisection point The optimal bisection point for this bisection node;

[0040] Step 6: Prediction function model construction

[0041] by As a prediction node, M in each prediction node 0 Ns The corresponding A (t) Ns The average value is the possible prediction value That is, the prediction function sub-model PRE is completed (t) s The construction of

[0042] Bring in the test set X Test s The predicted value A can be obtained Test(t) (Pre)φs The prediction subset Y Test(t) (Pre)s It is expressed as follows:

[0043] Y Test(t) (Pre)s =[A Test(t) (Pre)1s A Test(t) (Pre)2s … A Test(t) (Pre)L-1s A Test(t) (Pre)Ls ]

[0044] Find the test subset Y Test(t) s With the prediction subset Y Test(t) (Pre)s Error SE last It is expressed as follows:

[0045]

[0046] If the error SE last If the accuracy requirement is met, it is judged to be an effective prediction function sub-model PRE (t) s , otherwise, repeat step 5;

[0047] Merge all valid prediction function sub-models PRE (t)s The prediction result A (t) (Pre)s And take the average value to get A (t) (Pre) , that is, complete the prediction function model PRE (t) all The construction of

[0048] Step 7: Obtaining the rejection matrix

[0049] Known Cable force collection during construction It is expressed as follows:

[0050]

[0051] Assume that the prediction function model PRE (t) all The obtained The cable force aggregate of the construction stage The target prediction value The known target ideal value is A (t) (wanted) ,get Rejection Since t=1,2, it can be divided into two cases as follows:

[0052]

[0053] Therefore, the Rejection degree matrix of cable force set in the next construction stage It is expressed as follows:

[0054]

[0055] Step 8: Cable force assembly and grading during construction phase

[0056] When a Cable force collection during construction Rejection and Are less than or equal to Target rejection and and exists Less than Then it is called Complete victory Recorded as

[0057] For each The cable force aggregate of the construction stage exist:

[0058]

[0059] in, for The number of for the number of

[0060] but Collection It is expressed as follows:

[0061]

[0062] For The cable force collection is graded during the construction phase. The individual is recorded as The number is θ F(1) ,χ=1,2,...,θ F(1) -1,θ F(1) , stored in the first-level hierarchical set F(1), expressed as follows:

[0063]

[0064] Assume that there are l levels in total. For the Πth level classification set F(Π), Π=2,3,...,l-1,l, examine each individual in the classification set F(Π-1) of Statistics Collection middle The number of individuals is θ F(Π) , denoted as Stored in the hierarchical set F(Π), expressed as follows:

[0065]

[0066] Continue grading until the l-th level grading set is obtained;

[0067] Step 9: Obtaining the density operator matrix

[0068] For the H-th level hierarchical set F(H), according to The size of F(H) Sort by, H=1,2...,l-1,l,λ=1,2,...,θ F(H) -1,θ F(H) ,set up The density operator It is expressed as follows:

[0069]

[0070] Get the density operator of the H-th level hierarchical set F(H) It is expressed as follows:

[0071]

[0072] Step 10: Generate the Cable force collection during construction

[0073] No. The cable force set of each generation construction stage is the individual Random pairing is performed. If the number of individuals in a certain level of classification set F(H) is an odd number, the individual with the smallest density operator is counted into the next level of classification set for pairing, forming 250 pairs. and Each pair and generate and Constructing the offspring matrix It is expressed as follows:

[0074]

[0075] r~U([0,1])

[0076] The descendant matrix With Generation population The merger constitutes Temporary population The temporary population individuals are denoted as L=1,2,...,999,1000, which is expressed as follows:

[0077]

[0078] For Temporary population Repeat steps 7 to 9 to store each level of collection in the Generation population, until it is stored in a certain level collection If the number of individuals in the cable force set of the first construction stage exceeds 500, the hierarchical set of this level is stored according to the size of the density operator, with the larger density operator being given priority, and the first The cable force set in the construction stage is It is expressed as follows:

[0079]

[0080] Step 11: Obtaining the optimal cable force set during construction

[0081] Repeat steps 7 to 10 to get Generation population And satisfy The first Generation population The first-level classification set F(1) is taken as the optimal cable force set in the construction stage.

[0082] Compared with the prior art, the invention has the following beneficial effects: the invention combines big data analysis and intelligent optimization algorithms, comprehensively considers the complexity of cable-stayed bridge construction, can significantly improve the accuracy and efficiency of cable force optimization during the construction phase, and proposes an efficient and intelligent optimization method for the multi-objective optimization requirements of cable-stayed bridge construction, provides scientific support for the control of the bridge construction phase, the cable force and line shape optimization of the completed bridge, improves the construction accuracy and quality of the bridge, ensures the structural safety and performance of the bridge, and has the following advantages:

[0083] 1. The optimization method is systematic and efficient: By comprehensively analyzing a large amount of data during the construction phase, the cable force distribution and linear control of the completed bridge are optimized at the same time. Without the need for step-by-step debugging, the optimal cable force scheme during the construction phase can be quickly obtained, significantly improving the optimization efficiency of the cable force during the construction phase;

[0084] 2. The optimization results are closer to reality: the optimization fully considers various influencing factors such as construction environment, material properties and nonlinear effects, realizes more accurate cable force distribution and linear control, and can truly and reliably reflect cable force changes and structural responses;

[0085] 3. Intelligent optimization process: It can realize automatic calculation through programming, avoid tedious manual intervention, improve the intelligence level of the construction process, and ensure the efficiency and accuracy of the calculation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION

[0087] The technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0088] like Figure 1 As shown, a big data driven cable force multi-objective intelligent optimization method for the construction stage of a cable-stayed bridge includes the following steps:

[0089] Step 1: Determination of cable force during initial construction phase

[0090] A finite element model of a cable-stayed bridge is established. It is assumed that there are π batches of tensioning during the construction of the bridge. Through reverse dismantling simulation, the initial cable force of each batch of tensioning during the construction stage can be obtained as S.0 n , n=1,2,...,Π-1,Π, then the cable force matrix in the initial construction stage is expressed as follows:

[0091] S 0 =[S 0 1 S 0 2 … S 0 Π-1 S 0 Π ] T .

[0092] Step 2: Generation of cable force sets during the initial construction phase

[0093] For the initial construction stage, the cable force aggregate The generation of cable force is set to [-ΔS 0 n ,ΔS 0 n ], randomly select the cable force change value Δs in each cable force change range 0 n(N) , and obtain the cable force change matrix Δs 0 (N) It is expressed as follows:

[0094] Δs 0 (N) =[Δs 0 1(N) Δs 0 2(N) … Δs 0 Π-1(N) Δs 0 Π(N) ] T

[0095] In the formula, Δs 0 n(N) ~U([-ΔS 0 n ,ΔS 0 n ]), ΔS 0 n =0.1×S 0 n ,U([-ΔS 0 n ,ΔS 0 n ]) indicates that in the interval [-ΔS 0 n ,ΔS 0 n] uniform distribution within, N = {1,2,…,499,500}, M 0 N =S 0 +Δs 0 (N) .

[0096] Then we can get the cable force set M in the initial construction stage: 0 It is expressed as follows:

[0097]

[0098] In the formula, C 0 n(N) =S 0 n +Δs 0 n(N) .

[0099] Step 3: Finite element model analysis to form a data set

[0100] Use each initial construction stage cable force set individual Perform forward analysis to obtain the bridge cable force matrix A (1) N and the bridge anchor point elevation matrix A (2) N .

[0101] M 0 1 ,M 0 2 ,…,M 0 499 ,M 0 500 The corresponding A (t) 1 ,A (t) 2 ,...,A (t) 499 ,A (t) 500 As the original data set, t = 1, 2, repeated 750 times, all 500 initial construction stage cable force set individuals were randomly selected from the original data set with replacement Recorded as Composed training set X s , corresponding to A (t) N A (t) Ns Form the training subset Y (t) s , s={1,2,...,749,750}, let and the corresponding A(t) N There are L pairs and they are represented by M Test φN and A Test(t) φN They are composed of test sets X Test s With the test subset Y Test(t) s , φ=1,2,...L-1,L, which is expressed as follows:

[0102]

[0103] Y (t) Ns =[A (t) 1s A (t) 2s … A (t) 499s A (t) 500s ]

[0104]

[0105] Y Test(t) s =[A Test(t) 1s A Test(t) 2s … A Test(t) L-1s A Test(t) Ls ].

[0106] Step 4: Obtaining a subset of possible binary points

[0107] set up is a binary feature, For each training set X s As a binary node Perform 20 layers of binary division, and each binary node is represented as P indicates the level of the node, L P Indicates the sequence number of the node in this layer, P = 0, 1, 2...8, 9, L P =1,2,...,2 P -1,2 P .

[0108] For a binary node from In the random sampling, 5 different binary features are selected to obtain feature subsets It is expressed as follows:

[0109]

[0110] Then we get the corresponding characteristic tension subset It is expressed as follows:

[0111]

[0112] Subset the feature Arrange each row in order of size and calculate the middle value of two adjacent elements in each row as a possible bisection point w={1,2,3,4,5},d={1,2,...,498,499},get the possible bisection point subset It is expressed as follows:

[0113]

[0114] Step 5: Determine the optimal bisection point

[0115] For possible bisection points In a binary node, if M 0 Ns In but If M 0 Ns In but Thus we get and Medium 0 Ns The corresponding A (t) Ns The verification matrix is ​​constructed separately and

[0116] Will and A (t) Ns A (t)left δs With A (t)right ζs , δ=1,2,...m-1,m, ζ=1,2,...n-1,n, then A (t)left δs and A (t)right ζs The average values ​​are and It is expressed as follows:

[0117]

[0118] Then you can get A (t)left δs and and A(t)right ζs and The errors are as well as Since t=1,2, it can be divided into two cases as follows:

[0119]

[0120] In the formula, ||·|| F is the matrix norm.

[0121] Possible Equinox The corresponding weighted error It is expressed as follows:

[0122]

[0123] Traverse all possible bisection points When the corresponding weighted error When is the minimum, take the possible bisection point The optimal bisection point for this bisection node.

[0124] Step 6: Prediction function model construction

[0125] by As a prediction node, M in each prediction node 0 Ns The corresponding A (t) Ns The average value is the possible prediction value That is, the prediction function sub-model PRE is completed (t) s 's construction.

[0126] Bring in the test set X Test s The predicted value A can be obtained Test(t) (Pre)φs The prediction subset Y Test(t) (Pre)s It is expressed as follows:

[0127] Y Test(t) (Pre)s =[A Test(t) (Pre)1s A Test(t) (Pre)2s … A Test(t) (Pre)L-1s A Test(t) (Pre)Ls ]

[0128] Find the test subset Y Test(t) s With the prediction subset Y Test(t)(Pre)s Error SE last It is expressed as follows:

[0129]

[0130] If the error SE last If the accuracy requirement is met, it is judged to be an effective prediction function sub-model PRE (t) s , otherwise, repeat step 5.

[0131] Merge all valid prediction function sub-models PRE (t) s The prediction result A (t) (Pre)s And take the average value to get A (t) (Pre) , that is, complete the prediction function model PRE (t) all 's construction.

[0132] Step 7: Obtaining the rejection matrix

[0133] Known Cable force collection during construction It is expressed as follows:

[0134]

[0135] Assume that the prediction function model PRE (t) all The obtained The cable force aggregate of the construction stage The target prediction value The known target ideal value is A (t) (wanted) , can be obtained Rejection Since t=1,2, it can be divided into two cases as follows:

[0136]

[0137] Therefore, the Rejection degree matrix of cable force set in the next construction stage It is expressed as follows:

[0138]

[0139] Step 8: Cable force assembly and grading during construction phase

[0140] When a Cable force collection during construction Rejection and Are less than or equal to Target rejection and and exists Less than Then it is called Complete victory Recorded as

[0141] For each The cable force aggregate of the construction stage exist:

[0142]

[0143] in, for The number of for The number of

[0144] but Collection It is expressed as follows:

[0145]

[0146] For The cable force collection is graded during the construction phase. The individual is recorded as The number is θ F(1) ,χ=1,2,...,θ F(1) -1,θ F(1) , stored in the first-level hierarchical set F(1), expressed as follows:

[0147]

[0148] Assume that there are l levels in total. For the Πth level classification set F(Π), Π=2,3,...,l-1,l, examine each individual in the classification set F(Π-1) of Statistics Collection middle The number of individuals is θ F(Π) , denoted as Stored in the hierarchical set F(Π), expressed as follows:

[0149]

[0150] The classification is continued until the lth level classification set is obtained.

[0151] Step 9: Obtaining the density operator matrix

[0152] For the H-th level hierarchical set F(H), according to The size of F(H) Sort by, H=1,2...,l-1,l,λ=1,2,...,θ F(H) -1,θ F(H) ,set up The density operator It is expressed as follows:

[0153]

[0154] Get the density operator of the H-th level hierarchical set F(H) It is expressed as follows:

[0155]

[0156] Step 10: Generate the Cable force collection during construction

[0157] No. The cable force set of each generation construction stage is the individual Random pairing is performed. If the number of individuals in a certain level of classification set F(H) is an odd number, the individual with the smallest density operator is counted into the next level of classification set for pairing, forming 250 pairs. and Each pair and Can generate and Constructing the offspring matrix It is expressed as follows:

[0158]

[0159] Among them, r~U([0,1]).

[0160] The descendant matrix With Generation population The merger constitutes Temporary population The temporary population individuals are denoted as L=1,2,...,999,1000, which is expressed as follows:

[0161]

[0162] For Temporary population Repeat steps 7 to 9 to store each level of collection in the Generation population, until it is stored in a certain level collection If the number of individuals in the cable force set of the first construction stage exceeds 500, the hierarchical set of this level is stored according to the size of the density operator, with the larger density operator being given priority, and the first The cable force set in the construction stage is It is expressed as follows:

[0163]

[0164] Step 11: Obtaining the optimal cable force set during construction

[0165] Repeat steps 7 to 10 to get Generation population And satisfy The first Generation population The first-level classification set F(1) is taken as the optimal cable force set in the construction stage.

[0166] Example

[0167] This embodiment is implemented for the Yongjiang Bridge section of Ningbo-Wenzhou Expressway, and the specific data are as follows:

[0168] S1. Establish a finite element model of a cable-stayed bridge and obtain the cable force matrix S in the initial construction stage through reverse dismantling simulation. 0 as follows:

[0169]

[0170] S2, give the cable force variation range [-ΔS 0 n ,ΔS 0 n ]as follows:

[0171]

[0172] Randomly select a cable force change value Δs in each cable force change range 0 n(N) Get the cable force change matrix Δs 0 (N) , some data are given here as follows:

[0173]

[0174] Then we can get the cable force set M in the initial construction stage: 0 , some data are given here as follows:

[0175]

[0176] S3, using each initial construction stage cable force set individual M 0 NPerform forward analysis to obtain the bridge cable force matrix A (1) N and the bridge anchor point elevation matrix A (2) N , some data are given here as follows:

[0177]

[0178] M 0 1 ,M 0 2 ,...,M 0 499 ,M 0 500 The corresponding A (t) 1 ,A (t) 2 ,...,A (t) 499 ,A (t) 500 As the original data set, t = 1, 2, repeated 750 times, all 500 initial construction stage cable force set individuals were randomly selected from the original data set with replacement Recorded as Composed training set X s , where the training set X is given 1 Some of the data are as follows:

[0179]

[0180] The corresponding A (t) N , denoted as A (t) Ns , forming the training subset Y (t) s , where the training subset Y is given (1) 1 and Y (2) 1 Some of the data are as follows:

[0181]

[0182] Suppose that the number of and the corresponding A (t) N There are L pairs, represented by M Test φN and A Test(t) φN ,φ=1,2,...L-1,L, forming the test set X Test s With the test subset Y Test(t)s , here is the test set X Test 1 Some of the data are as follows:

[0183]

[0184] And the test subset Y Test ( 1) 1 and Y Test ( 2) 1 Some of the data are as follows:

[0185]

[0186] S4. Set is a binary feature, For each training set X s As a binary node Perform 20 layers of binary division, and each binary node is represented as

[0187] For example, for a binary node from In the random sampling, 5 different binary features are selected to obtain feature subsets Then we get the corresponding characteristic tension subset Some of the data are given here as follows:

[0188]

[0189] Subset the feature Arrange each row in order of size and calculate the middle value of two adjacent elements in each row as a possible bisection point Get a subset of possible bisection points Some of the data are given here as follows:

[0190]

[0191] S5. For the possible bisection point 3082.91, in the following bisection nodes:

[0192]

[0193] Available and as follows:

[0194]

[0195] Get the verification matrix and as follows:

[0196]

[0197] Verification Matrix and as follows:

[0198]

[0199] average value and as follows:

[0200]

[0201] average value and as follows:

[0202]

[0203] The available errors are as follows:

[0204]

[0205] Possible Equinox The corresponding weighted error as follows:

[0206]

[0207] Traverse all possible bisection points When the corresponding weighted error When is the minimum, take the possible bisection point The optimal bisection point for this bisection node.

[0208] S6, As a prediction node, M in each prediction node 0 Ns The corresponding A (t) Ns The average value is the possible prediction value That is, the prediction function sub-model PRE is completed (t) s 's construction.

[0209] Substitute the test set X given in step 3 Test 1 Part of the data gets the predicted subset Y Test(1) (Pre)1 and Y Test(2) (Pre)1 , some data are given here as follows:

[0210]

[0211] Find the test subset YTest(t) s With the prediction subset Y Test(t) (Pre)s Error SE last as follows:

[0212]

[0213] Complete the prediction function model PRE (t) all 's construction.

[0214] S7, known Cable force collection during construction Some of the data are given here as follows:

[0215]

[0216] Assume that the prediction function model PRE (t) all The obtained The cable force aggregate of the construction stage The target prediction value and as follows:

[0217]

[0218] Known target ideal value A (t) (wanted) as follows:

[0219]

[0220] Available Rejection Therefore, the Rejection degree matrix of cable force set in the next construction stage as follows:

[0221]

[0222] S8, take the part given in step 7 As an example, the cable force data of the first construction stage is Cable force collection during construction Rejection and Are less than or equal to Target rejection and and exists Less than Then it is called Complete victory Recorded as

[0223] For The cable force collection is graded during the construction phase. The individual is recorded as The number is θ F(1) ,χ=1,2,...,θ F(1) -1,θ F(1) , stored in the first-level hierarchical set F(1) as follows:

[0224]

[0225] There are 5 levels in total, and each 2, 3, 4, and 5 level classification is as follows:

[0226]

[0227] S9. For the H-th level hierarchical set F(H), according to The size of F(H) Sort and get the density operator of the H-th level hierarchical set F(H) as follows:

[0228]

[0229] Here, since there is only one The cable force aggregate of the construction stage No sorting is required, and the density operators are all ∞.

[0230] S10, taking the part given in step 7 Taking the cable force data of the construction stage as an example, the child matrix as follows:

[0231]

[0232] The descendant matrix With Generation population The merger constitutes Temporary population Some of the data are given here as follows:

[0233]

[0234] For Temporary population Repeat steps 7 to 9 to get Cable force collection during construction Some of the data are as follows:

[0235]

[0236] S11, repeat steps 7 to 10 to obtain Generation population Some of the data are as follows:

[0237]

[0238] satisfy The first Generation population The first-level classification set F(1) is taken as the optimal cable force set in the construction stage. Some data are given here as follows:

[0239]

[0240] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other forms of assembly without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all changes within the meaning and range of equivalents of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0241] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A big data driven multi-objective intelligent optimization method for cable force in the construction phase of a cable-stayed bridge, characterized by: The following steps are involved: Step 1: Determination of cable force during initial construction phase The finite element model of the cable-stayed bridge is established. There are Π batches of tensioning during the construction process. The initial cable force size S0 of each batch of tensioning is obtained through reverse dismantling simulation. n , n=1,2,...,Π-1,Π, the cable force matrix in the initial construction stage is expressed as follows: S0=[S0 1 S0 2 … S0 Π-1 S0 Π ] T ; Step 2: Generation of cable force sets during the initial construction phase For the initial construction stage, the cable force aggregate The generation of cable force is set to [-ΔS0 n ,ΔS0 n ], randomly select the cable force change value Δs0 n(N) , and obtain the cable force change matrix Δs0 (N) It is expressed as follows: Δs0 (N) =[Δs0 1(N) Δs0 2(N) … Δs0 Π-1(N) Δs0 Π(N) ] T Then the cable force set M0 in the initial construction stage is expressed as follows: where Δs0 n(N) ~U([-ΔS0 n ,ΔS0 n ), ΔS0 n = 0.1×S0 n , N = {1, 2,..., 499, 500}, M0 N = S0 + Δs0 (N) , C0 n(N) = S0 n + Δs0 n(N) ; Step 3: Finite element model analysis to form a data set Use each initial construction stage cable force set individual Perform forward analysis to obtain the bridge cable force matrix A (1) N and the bridge anchor point elevation matrix A (2) N ; M0 1 ,M0 2 ,...,M0 499 ,M0 500 The corresponding A (t) 1 ,A (t) 2 ,...,A (t) 499 ,A (t) 500 As the original data set, t=1,2, repeat randomly extracting all individuals from the original data set with replacement Recorded as Composed training set X s , corresponding to A (t) N A (t) Ns Form the training subset Y (t) s , suppose that the and the corresponding A (t) N There are L pairs and they are represented by M Test φN and A Test(t) φN They are composed of test sets X Test s With the test subset Y Test(t) s , φ=1,2,...L-1,L, which is expressed as follows: AND (t) Ns =[A (t) 1s TO (t) 2s …TO (t) 499s TO (t) 500s ] AND Test(t) s =[A Test(t) 1s TO Test(t) 2s …TO Test(t) L-1s TO Test(t) Ls ]; Step 4: Obtaining a subset of possible binary points Set binary features For each training set X s As a binary node Perform binary division, and each binary node is represented as P indicates the level of the node, L P Indicates the sequence number of the node in this layer; For a binary node from In the random sampling, 5 different binary features are selected to obtain feature subsets It is expressed as follows: Then we get the corresponding characteristic tension subset It is expressed as follows: Subset the feature Arrange each row in order of size and calculate the middle value of two adjacent elements in each row as a possible bisection point w={1,2,3,4,5},d={1,2,...,498,499},get the possible bisection point subset It is expressed as follows: Step 5: Determine the optimal bisection point For possible bisection points In the binary node, if M0 Ns In but If M0 Ns In but Thus we get and Medium M0 Ns The corresponding A (t) Ns The verification matrix is ​​constructed separately and Will and A (t) Ns A (t)left δs With A (t)right ζs , δ=1,2,...m-1,m, ζ=1,2,...n-1,n, then A (t)left δs and A (t)right ζs The average values ​​are and It is expressed as follows: Then you can get A (t)left δs and and A (t)right ζs and The errors are as well as Since t=1,2, it can be divided into two cases as follows: Possible Equinox The corresponding weighted error It is expressed as follows: Traverse all possible bisection points When the corresponding weighted error When is the minimum, take the possible bisection point The optimal bisection point for this bisection node; Step 6: Prediction function model construction by As a prediction node, M0 in each prediction node Ns The corresponding A (t) Ns The average value is the possible prediction value That is, the prediction function sub-model PRE is completed (t) s The construction of Bring in the test set X Test s The predicted value A can be obtained Test(t) (Pre)φs The prediction subset Y Test(t) (Pre)s It is expressed as follows: AND Test(t) (Pre)s =[A Test(t) (Pre)1s TO Test(t) (Pre)2s … TO Test(t) (Pre)L-1s TO Test(t) (Pre)Ls ] Find the test subset Y Test(t) s With the prediction subset Y Test(t) (Pre)s Error SE last It is expressed as follows: If the error SE last If the accuracy requirement is met, it is judged to be an effective prediction function sub-model PRE (t) s , otherwise, repeat step 5; Merge all valid prediction function sub-models PRE (t) s The prediction result A (t) (Pre)s And take the average value to get A (t) (Pre) , that is, complete the prediction function model PRE (t) all The construction of Step 7: Obtaining the rejection matrix Known Cable force collection during construction It is expressed as follows: Assume that the prediction function model PRE (t) all The obtained The cable force aggregate of the construction stage The target prediction value The known target ideal value is A (t) (wanted) ,get Rejection Since t=1,2, it can be divided into two cases as follows: Therefore, the Rejection degree matrix of cable force set in the next construction stage It is expressed as follows: Step 8: Cable force assembly and grading during construction phase When a Cable force collection during construction Rejection and Are less than or equal to Target rejection and and exists Less than Then it is called Complete victory Recorded as A, B∈N; For each The cable force aggregate of the construction stage exist: in, for The number of for the number of but Collection It is expressed as follows: For The cable force collection is graded during the construction phase. The individual is recorded as The number is θ F(1) ,χ=1,2,...,θ F(1) -1,θ F(1) , stored in the first-level hierarchical set F(1), expressed as follows: Assume that there are l levels in total. For the Πth level classification set F(Π), Π=2,3,...,l-1,l, examine each individual in the classification set F(Π-1) of Statistics Collection middle The number of individuals is θ F(Π) , denoted as Stored in the hierarchical set F(Π), expressed as follows: Continue grading until the l-th level grading set is obtained; Step 9: Obtaining the density operator matrix For the H-th level hierarchical set F(H), according to The size of F(H) Sort by, H=1,2...,l-1,l,λ=1,2,...,θ F(H) -1,θ F(H) ,set up The density operator It is expressed as follows: Get the density operator of the H-th level hierarchical set F(H) It is expressed as follows: Step 10: Generate the Cable force collection during construction No. The cable force set of each generation construction stage is the individual Random pairing is performed. If the number of individuals in a certain level of classification set F(H) is an odd number, the individual with the smallest density operator is counted into the next level of classification set for pairing, forming 250 pairs. and Each pair and generate and Constructing the offspring matrix It is expressed as follows: r～U([0,1]) The descendant matrix With Generation population The merger constitutes Temporary population The temporary population individuals are denoted as It is expressed as follows: For Temporary population Repeat steps 7 to 9 to store each level of collection in the Generation population, until it is stored in a certain level collection If the number of individuals in the cable force set of the first construction stage exceeds 500, the hierarchical set of this level is stored according to the size of the density operator, with the larger density operator being given priority, and the first The cable force set in the construction stage is It is expressed as follows: Step 11: Obtaining the optimal cable force set during construction Repeat steps 7 to 10 to get Generation population And satisfy The first Generation population The first-level classification set F(1) is taken as the optimal cable force set in the construction stage.

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