# US 12,112,101 B2 · Dual physically-driven and data-driven method for reconstructing internal response of bridge

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- Number: US 12,112,101 B2
- Document ID: US12112101B2
- Status: Granted
- Kind: B2
- Application number: 18/410,994
- Filed: 2024-01-11
- Published: 2024-08-29
- Granted: 2024-10-08
- Earliest claimed: 2023-02-22
- Assignees: Hefei University of Technology
- Applicants: Hefei University of Technology
- Inventors: Wenyu He; Wenhan Chen; Dongyang Hu; Yifan Li; Zhidong Li; Zuocai Wang; Jing Zhang
- Web page: https://patentgrep.com/patent/US12112101B2

## CPC

- G06F 30/13: Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
- G06F 17/16: Matrix or vector computation {, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization (matrix transposition G06F7/78)}
- G06F 30/23: using finite element methods [FEM] or finite difference methods [FDM]
- Y02T 90/00: Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- Y02T: CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION

## Abstract

Disclosed is a dual physically-driven and data-driven method for reconstructing internal response of a bridge. The method includes: obtaining acceleration response by an acceleration sensor under an action of an unknown load of the bridge; embedding a physical logic into a neural network based on a frequency response function; putting a physical formula and corresponding boundary conditions and initial conditions into a loss function as penalty terms, and limiting a space of a feasible solution accordingly; and training a neural network model, and predicting acceleration of an unknown point by inputting an acceleration response set of a known point obtained by the sensor into the network. The formula is solved by converting direct solving of a control formula into optimization of the loss function, such that the problems that the internal response of the bridge is difficult to measure and excessively depends measured data can be effectively solved, and accuracy and robustness of internal response prediction of the bridge can be improved.

## Claims

### Claim 1

A dual physically-driven and data-driven method for reconstructing internal response of a bridge, the method comprising:
step 1, arranging sensors on m nodes of the bridge, respectively, wherein the sensors are configured to collect acceleration response of the bridge, wherein m is positive integers, and m denotes number of external node;
step 2, modeling the bridge by utilizing a finite element model to obtain the finite element model of the bridge, applying a random load to the finite element model of the bridge, and obtaining an acceleration response matrix {umlaut over (X)}k=[u1, u2, . . . , ui, . . . , um]T of m external nodes and a frequency response function matrix Hk=[H1, H2, . . . , Hi, . . . , Hm]T thereof, and an acceleration response matrix {umlaut over (X)}u=[u1′, u2′, . . . uj′, . . . un′]T of n internal nodes of the bridge and a frequency response function matrix Hu=[H1′, H2′, . . . Hj′, . . . Hn′]T thereof, wherein ui denotes acceleration response with a length of w at an ith external node, uj′ denotes acceleration response with a length of w at a jth internal node, Hi denotes frequency response with a length of w at an mth external node, Hj denotes frequency response with a length of w at a nth internal node, n denotes number of internal node, w denotes length, n and w are positive integers, let a total acceleration response set be {umlaut over (X)}={{umlaut over (X)}k, {umlaut over (X)}u}, and let a total frequency response function set be H={Hk, Hu};
step 3, constructing a convolutional neural network (CNN) model sequentially, the CNN model comprising an input layer, convolutional layers with a-layer, maximum pooling layers with (a−1)-layer, fully connected layers with e-layer, wherein a and e are positive integers, and an output layer, adding a maximum pooling layer between two adjacent convolutional layers, and utilizing a relu activation function between the fully connected layers;
inputting {umlaut over (X)}k as an input set into the CNN model, sequentially performing convolution and pooling by the convolutional layers and the maximum pooling layers that are cross-connected, and outputting a feature vector with a dimension of b×c×d from an ath convolutional layer; wherein b, c, and d are positive integers, b denotes a number of channels for signal input, c denotes a number of channels for convolution generation, and d denotes a size of a convolution kernel;
flattening a feature vector set with the dimension of b×c×d, and obtaining a feature vector set with a dimension of b×c; and
sequentially processing the feature vector set with the dimension of b×c by the fully connected layers, and obtaining an internal node response prediction vector with a dimension of w×n expressed as {umlaut over (X)}net;
step 4, constructing a difference function;
step 4.1: performing dimensionality reduction on acceleration responses in {umlaut over (X)}k, performing Fourier transform, and obtaining acceleration response matrices {umlaut over (X)}k(w)=[u1(w), u2(w), . . . , ui(w), . . . um(w)]T of m external nodes on a frequency domain with a dimension of m×w, wherein ui(w) denotes acceleration response on a frequency domain with a length of w at an ith node;
step 4.2, performing Fourier transform on {umlaut over (X)}net, and obtaining a prediction vector {umlaut over (X)}net(w)=[u1′(w), u2′(w), . . . , uj′(w), . . . , un′(w)]T of n internal response nodes on a frequency domain with a dimension of n×w, wherein uj′(w) denotes acceleration response on a frequency domain with a length of w at a jth internal node predicted by the CNN mode;
step 4.3: constructing a relation formula between external node response and internal node response in structural dynamics by formula (1):
{
X
..
net
(
w
)
=
T
aku
(
ω
)
⁢
X
..
k
(
ω
)
T
aku
(
ω
)
=
H
u
(
H
k
)
+
(
1
)
wherein in formula (1), Taku(ω) denotes a transfer matrix, performing pseudo-inverse multiplication on an acceleration frequency response function matrix Hu of an internal node and an acceleration frequency response function matrix Hk of an external node, obtaining a true acceleration response transfer matrix Taku(w), and computing a difference f by formula (2):
f={umlaut over (X)}net(w)−Taku(ω){umlaut over (X)}k(w) (2)
step 4.4, performing pseudo-inverse multiplication on {umlaut over (X)}net(w) and {umlaut over (X)}k(w), and obtaining a predicted acceleration response transfer matrix Tnet(w)=[a1(w), a2(w), . . . , aj(w), am(w)]; wherein aj(w) denotes an acceleration response transfer function with a length n at a jth external node; and
computing a difference T by formula (3):
T=Tnet(w)−Taku(ω) (3)
step 5: generating a total loss La11 of the CNN model by formula (4):
La11=L1+L2+L3+L4 (4)
wherein in formula (4), L1 denotes a first loss and is obtained by formula (5), L2 denotes a second loss and is obtained by formula (6), L3 denotes a third loss and is obtained by formula (7), and L4 denotes a fourth loss and is obtained by formula (8);
L1=(f−f0)T(f−f0) (5)
L2=(T−f0)T(T−f0) (6)
L3=({umlaut over (X)}net0−{umlaut over (X)}0)T({umlaut over (X)}net0−{umlaut over (X)}0) (7)
L4=({umlaut over (X)}net−{umlaut over (X)}u′)T({umlaut over (X)}net−{umlaut over (X)}u′) (8)
wherein in formulas (5)-(8), f0 denotes an all-zero matrix with a dimension of n×w, {umlaut over (X)}net0 denotes predicted response output by an acceleration response matrix under an initial condition through the CNN model, let the acceleration response matrix under the initial condition be an all-zero matrix with a dimension of w×1×m, and {umlaut over (X)}0 denotes acceleration response of {umlaut over (X)}u under the initial condition; and
step 6: based on the total acceleration response set {umlaut over (X)}={{umlaut over (X)}k, {umlaut over (X)}u} and the total frequency response function set H={Hk, Hu}, training the CNN model with an Adam optimizer, computing a total loss La11 and updating parameters of the CNN model until a maximum training time is reached or the total loss La11 converges, and obtaining a trained CNN model for reconstructing response of any internal node of the bridge.

### Claim 2 (depends on 1)

An electronic device, comprising a memory and a processor, wherein the memory is configured to store programs supporting the processor in execution of a method for reconstructing internal response of a bridge according to claim 1, and wherein the processor is configured to execute the programs stored in the memory.

### Claim 3 (depends on 1)

A non-transitory computer-readable storage medium, storing computer programs that, when executed by a processor, the computer programs direct the processor to execute steps of a method for reconstructing internal response of a bridge according to claim 1.