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% Liew, p-Ritz

D0=e2*h^3/12/(1-miu12*miu21);

D_d = diag(sqrt(D_D)*L*L/pi/pi*sqrt(rho*h/D0));

[D_d,ii1] = sort(D_d); ii = ii1(1:numberOfModes); %sort the elements of A in ascending order. V_Dd=V_D(:,ii1);

VV = V(:,ii);

activeDofW=setdiff((1:numberNodes)',fixedNodeW); NNN=size(activeDofW);

VVV(1:numberNodes,1:12)=0; for i=1:numberOfModes

VVV(activeDofW,i)=VV(1:NNN,i); end

Frequency_Healthy=D(1:15) aaaa=Frequency_Healthy' Frequency_Damaged=D_d(1:15);

Cầu giàn thép liên tục (Cầu Bến Thủy)

clc;clear;close all Code=1;

Node=Node _Sources_beam_BenThuy(Code); Nodes(:,2:end) = Nodes(:,2:end)./100;

% Check the node coordinates as follows: figure; plotnodes(Nodes,'numbering','off');

% Element types -> {EltTypID EltName} Types= {1 'beam'

2 'truss';};

% Dac trung mat cat

A1 = 0.0138 ; Iz1 = 0.056290253 ; Iy1 = 0.282335199 ; % V1a - M? th??ng

A2 = 0.0138 ; Iz2 = 0.056290253 ; Iy2 = 0.282335199 ; % V2- Thanh ??ng ??u nh?p A3 = 0.0098 ; Iz3 = 0.042128938 ; Iy3 = 0.312185856 ; % V3- Thanh ??ng gi?a nh?p A4 = 0.018 ; Iz4 = 0.918132592 ; Iy4 = 8.47690707 ; % V4- Thanh xiên ??u nh?p A5 = 0.0138 ; Iz5 = 0.056290253 ; Iy5 = 0.282335199 ; % V4a- Thanh xiên gi?a nh?p A6 = 0.0044 ; Iz6 = 0.016765098 ; Iy6 = 0.334116109 ; % Gi?ng ngang

A7 = 0.0056 ; Iz7 = 0.021765094 ; Iy7 = 0.467769366 ; % H? gi?ng

% Sections=[SecID A ky kz Ixx Iyy Izz yt yb zt zb] Sections=[

A1	Inf Inf 0	Iz1 Iy1 0	0
2	A2	Inf Inf 0	Iz2 Iy2 0	0
3	A3	Inf Inf 0	Iz3 Iy3 0	0
4	A4	Inf Inf 0	Iz4 Iy4 0	0
5	A5	Inf Inf 0	Iz5 Iy5 0	0
6	A6	Inf Inf 0	Iz6 Iy6 0	0
7	A7	Inf Inf 0	Iz7 Iy7 0	0];

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Elements= Elements_Sources_beam_BenThuy(Code);

% Materials=[MatID E nu];

E = 2.05e11; %Elastic Modul 2.05*10^11 N/m2 v =0.3; % Poisson 0.3

p = 7850; % Density 7850 kg/m3 Materials= [1 E v p]; % steel

% Elements=[EltID TypID SecID MatID n1 n2 n3]

% Check node and element definitions as follows: hold('on'); plotelem(Nodes,Elements,Types,'Numbering','off'); title('Nodes and elements');

% Degrees of freedom

% Assemble a column matrix containing all DOFs at which stiffness is

% present in the model: DOF=getdof(Elements,Types); seldof=[ [101;301

108;109;308;309

120;320

132;332

143;144;343;344

151]+0.03

[101;301

151]+0.01

[101;301

108;109;308;309

120;320

132;332

143;144;343;344

151]+0.02];

DOF=removedof(DOF,seldof);

% Assembly of stiffness matrix K [K,M]=asmkm(Nodes,Elements,Types,Sections,Materials,DOF);

% bvib manual nMode=20;

[phi,omega]=eigfem(K,M,nMode);

% Display eigenfrequenties disp('Lowest eigenfrequencies [Hz]'); a=omega/2/pi;

idx=(1:15);

% Display eigenfrequenties

% disp('Lowest eigenfrequencies [Hz]'); Show_animate = 0;

for show_01 = 1:10

if Show_animate == 0

% Disp Max figure;

plotdisp(Nodes,Elements,Types,DOF,phi(:,show_01),'DispMax','off') title(['Eigenmode', num2str(show_01), '**** Frequency: ' , num2str(a(show_01))]) elseif Show_animate == 1

% Animate eigenmodes figure;

animdisp(Nodes,Elements,Types,DOF,phi(:,show_01)) title(['Eigenmode', num2str(show_01)])

elseif Show_animate == 2 close all

continue

end end figure;

subplot(2,2,1),plotdisp(Nodes,Elements,Types,DOF,phi(:,1),'DispMax','off'); subplot(2,2,2),plotdisp(Nodes,Elements,Types,DOF,phi(:,2),'DispMax','off'); subplot(2,2,3),plotdisp(Nodes,Elements,Types,DOF,phi(:,3),'DispMax','off'); subplot(2,2,4),plotdisp(Nodes,Elements,Types,DOF,phi(:,4),'DispMax','off');

% Animate eigenmodes figure;

animdisp(Nodes,Elements,Types,DOF,phi(:,1)) title('Eigenmode 1')

for i = 1:nMode fre_FEM=a(i);

disp(['Mode ',num2str(i),' - Frequency: ', num2str(a(i))]) end

ANN

clc;

clear; close all; rng default tic

% filename ='ANN_Benthuy_04'; filename ='Case2_Benthuy.xlsx'; sheetname1 ='Sheet1'; sheetname2 ='Sheet2';

input = xlsread(filename,sheetname1,'A1:Z10000'); %call datas from sheetname1 target = xlsread(filename,sheetname2,'A1:Z10000'); %call datas from sheetname2 inputs=input';

targets=target';

% x=inputs;

x = awgn(inputs,90,'measured'); t = targets;

trainFcn ='trainlm'; % Levenberg-Marquardt backpropagation.

% Create a Fitting Network hiddenLayerSize = 10;

net = fitnet(hiddenLayerSize,trainFcn); net=feedforwardnet(hiddenLayerSize,trainFcn); net.divideParam.trainRatio =70/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 15/100; net.trainParam.epochs = 1000;

[net] = train(net,x,t) y = net(x);

performance = perform(net,t,y)

nntraintool('plot','plotregression');

set(findall(gcf,'-property','FontSize'),'FontSize',10); for i1 = 1:4

subplot(2,2,i1) grid on

end nntraintool('plot','plotperform');

set(findall(gcf,'-property','FontSize'),'FontSize',10); grid on;

nntraintool('plot','plottrainstate');

set(findall(gcf,'-property','FontSize'),'FontSize',10); grid on;

nntraintool('plot','ploterrhist');

set(findall(gcf,'-property','FontSize'),'FontSize',10); grid on;

timeElapsed = toc

Giao diện của ANN truyền thống trên nền tảng chương trình MATLAB

ANNPSO

%% Empty space clc

clear

rng default tic;

%% Read data

% Read the input file

filename ='Case2_Benthuy.xlsx'; sheet_input ='Sheet1'; sheet_output ='Sheet2';

input = xlsread(filename,sheet_input,'A1:Z10000'); target = xlsread(filename,sheet_output,'A1:Z10000'); inputs = awgn(input',100,'measured');

% inputs=input'; targets=target';

% Select number neuron of input, output and hidden layer

number_input=length(inputs(:,1)); number_output=length(targets(:,1)); number_hidden=100;

%% STEP 1: TRAIN DATA WITH ANN

% Chose your train function in ANN trainFcn ='trainlm';

net = feedforwardnet(number_hidden,trainFcn); net = configure(net,inputs,targets);

net = fitnet(number_hidden,trainFcn);

% Close train window net.trainParam.showWindow = 0;

% Setup Division of Data for Training, Validation, Testing net.divideParam.trainRatio = 70/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 15/100;

% Train

[net] = train(net,inputs,targets)

% error NMSE PSO optimized NN outputs=net(inputs); error_1=outputs - targets;

err_1st = mean(error_1.^2)/mean(var(targets',1));%.^ means square.

x=inputs; t = targets; y = net(x);

perf = perform(net,t,y)

% mean is used to calculate mean values. For example. A= [1 2 3 4]

% mean (A)=(1+2+3+4)/4;

%var returns the variance of the elements of A along the first array

%dimension; see equation of var on Mathwork. err_1st=norm(err_1st)

disp (['the differen between output and target is: ' num2str(err_1st)])

% get the normal NN weights and bias weight_bias = getwb(net);

% setting up parameters parameters.MaxIt = 50;

parameters.nPop = 100; parameters.Lower_Boundary = min(weight_bias); parameters.Upper_Boundary = max(weight_bias);

% parameters.Lower_Boundary = -1.5;

% parameters.Upper_Boundary = 1.5; parameters.c1 = 2;

parameters.c2 = 2;

parameters.w = 1;

parameters.w_damp = 0.99; parameters.nn=err_1st;

% Max epoch max_epoch = 1000;

for epoch = 1 : max_epoch

% numWeightElements(nWE)

nWE=number_hidden*number_input+number_output*number_hidden+number_hidden+number_outp ut;

%% STEP 2: TRAIN WITH PSO

% disp('STEP 2: TRAIN WITH PSO')

[FinalCost,FinalPosition,BestCosts] = PSO (parameters,weight_bias,nWE, number_hidden,number_input,number_output,net,inputs,targets);

xo = FinalPosition(1,:); xbest = xo;

ybest = FinalCost;

%% STEP 3: TRAIN NN SECOND TIME disp('STEP 3: TRAIN NN SECOND TIME')

% Setup Division of Data for Training, Validation, Testing net_2nd.divideParam.trainRatio = 70/100; net_2nd.divideParam.valRatio = 15/100; net_2nd.divideParam.testRatio = 15/100;

% Chose your train function in ANN

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