By Brian Walsh & Arturo González

An Overview of the Application of Neural Networks to the Monitoring of Civil Engineering Structures

By Brian Walsh & Arturo González

With thanks thanks to the 6th European Framework Project ARCHES for their generous support

Contents

1. Introduction to neural networks (NNs)

2. Damaged beam simulation

3. Network training

4. Results

• Number of hidden nodes

• Number of input nodes

• Size of training set

1. Introduction to NNs

Synapses

Cell Body

Activation Function

Weighted Connections

1. Introduction to NNs

2. Damaged Beam Simulation

2. Damaged Beam Simulation

Reduced Stiffness

2. Damaged Beam Simulation

3. Network Training

Error BP

4. Results

Net Output Category

Net indicates lowest EI value in correct element

Net indicates lowest EI value in correct element, and healthy elements elsewhere

EIpredicted / EItarget < 1.03

Best performance Category

Location Identified

EI Profile Identified

Severity Estimated

Beam Identified

4. Results

4.1 Number of Nodes in Hidden Layer

4. Results

4.1 Number of Nodes in Hidden Layer

4. Results

4.2 Number of Input Nodes

4. Results

4.3 Size of Training Set

5. Conclusions

• NNs can be an effective tool for damage detection

• NNs sensitive to number of nodes & training patterns

• Further work

Thank you for listening!