Generative adversarial networks for structural damage diagnostics

A structure diagnosis optimization system for automatically predicting a condition of at least one civil structure, the structure diagnosis optimization system comprising:

• a computing device having a processor;
• and
• a non-transitory computer-readable medium operably coupled to the processor, the computer-readable medium having computer-readable instructions stored thereon that, when executed by the processor, cause the structure diagnosis optimization system to automatically predict the condition of the at least one civil structure by executing instructions comprising:
• receiving, via at least one sensor communicatively coupled to a computing device, at least one actual sensor response from at least one civil structure, wherein the at least one sensor is in mechanical communication with the at least one civil structure, whereby the at least one actual sensor response comprises at least one actual damaged scenario, at least one actual undamaged scenario, or both;
• augmenting, via at least one generative adversarial network (GAN) architecture of the processor, the at least one actual sensor response with at least one synthetic sensor response, wherein the at least one GAN architecture comprises a Wasserstein Deep Convolutional Generative Adversarial Network with Gradient Penalty (WDCGAN-GP) architecture, a Cycle-Consistent Wasserstein Deep Convolutional Generative Adversarial Network with Gradient Penalty (CycleWDCGAN-GP), or both, wherein the at least one synthetic sensor response comprises at least one synthetic damaged scenario, at least one synthetic undamaged scenario, or both, whereby the at least one actual sensor response, at least one synthetic sensor response, or both are compiled into at least one augmented sensorial dataset;
• training, via at least one Deep-Learning-based Structural Damage Diagnostics (DL-based SDD) architecture of the processor, at least one prediction dataset based on the at least one augmented sensorial dataset;
• comparing, via the processor of the computing device, the at least one trained prediction dataset with at least one unseen sensor response from the at least one civil structure;
• and
• automatically predicting, via the processor of the computing device, the condition of the at least one civil structure on a display device associated with the computing device by:
• based on determination that the at least one unseen sensor response from the at least one sensor matches the at least one actual damaged scenario, at least one synthetic damaged scenario, or both of the at least one trained prediction dataset, transmitting a notification indicative of a damaged condition;
• and
• based on determination that the at least one unseen sensor response from the at least one sensor matches the at least one actual undamaged scenario, at least one synthetic undamaged scenario, or both of the at least one trained prediction dataset, transmitting a notification indicative of an undamaged condition.

A method for automatically diagnosing a condition of at least one alternative civil structure, the method comprising the steps of:

• receiving, via at least one sensor communicatively coupled to a computing device, at least one actual sensor response from at least one civil structure, wherein the at least one sensor is in mechanical communication with the at least one civil structure, whereby the at least one sensor response comprises at least one actual damaged scenario, at least one actual undamaged scenario, or both;
• augmenting, via at least one generative adversarial network (GAN) architecture of the processor, the at least one actual sensor response with at least one synthetic sensor response, wherein the at least one GAN architecture comprises a Wasserstein Deep Convolutional Generative Adversarial Network with Gradient Penalty (WDCGAN-GP architecture, a Cycle-Consistent Wasserstein Deep Convolutional Generative Adversarial Network with Gradient Penalty (CycleWDCGAN-GP), or both, wherein the at least one synthetic sensor response comprises at least one synthetic damaged scenario, at least one synthetic undamaged scenario, or both, whereby the at least one actual sensor response, at least one synthetic sensor response, or both are compiled into at least one augmented sensorial dataset;
• training, via at least one Deep-Learning-based Structural Damage Diagnostics (DL-based SDD) architecture of the processor, at least one prediction dataset based on the at least one augmented sensorial dataset;
• learning, via the Domain-Generalized Cycle-Generative (DGCG) architecture of the processor, at least one domain-invariant representation of at least one domain of the at least one civil structure, wherein the at least one domain comprises the at least one scenario of the at least one actual sensor response, at least one synthetic response, or both of the at least one civil structure, whereby the at least one scenario comprises at least one actual, synthetic, or both damaged scenario, at least one actual, synthetic, or both undamaged scenario, or both;
• applying, via the processor of the computing device, the domain-invariant representation to the at least one alternative civil structure;
• and
• automatically predicting, via the processor of the computing device, a condition of the at least one alternative civil structure on a display device associated with the computing device by:
• based on determination that at least one alternative domain source of the alternative civil structure matches the at least one domain source comprising at least one damaged scenario of the at least one civil structure, transmitting a notification indicative of a damaged condition;
• and
• based on determination that at least one alternative domain source of the alternative civil structure matches the at least one domain source comprising at least one undamaged scenario of the at least one civil structure, transmitting a notification indicative of an undamaged condition.