Real-time topology optimization via learnable mappings

In traditional topology optimization, the computing time required to iteratively update the material distribution within a design domain strongly depends on the complexity or size of the problem, limiting its application in real engineering contexts. This work proposes a multi-stage machine learning strategy that aims to predict an optimal topology and the related stress fields of interest, either in 2D or 3D, without resorting to any iterative analysis and design process. The overall topology optimization is treated as regression task in a low-dimensional latent space, that encodes the variability of the target designs. First, a fully-connected model is employed to surrogate the functional link between the parametric input space characterizing the design problem and the latent space representation of the corresponding optimal topology. The decoder branch of an autoencoder is then exploited to reconstruct the desired optimal topology from its latent representation. The deep learning models are trained on a dataset generated through a standard method of topology optimization implementing the solid isotropic material with penalization, for varying boundary and loading conditions. The underlying hypothesis behind the proposed strategy is that optimal topologies share enough common patterns to be compressed into small latent space representations without significant information loss. Results relevant to a 2D Messerschmitt-B\"olkow-Blohm beam and a 3D bridge case demonstrate the capabilities of the proposed framework to provide accurate optimal topology predictions in a fraction of a second

A Combined Model-Order Reduction and Deep Learning Approach for Structural Health Monitoring under Varying Operational and Environmental Conditions

Nowadays, the aging, deterioration, and failure of civil structures are challenges of paramount importance, increasingly motivating the search of advanced Structural Health Monitoring (SHM) tools. In this work, we propose a SHM strategy for online structural damage detection and localization, combining Deep Learning (DL) and Model-Order Reduction (MOR). The developed data-based procedure is driven by the analysis of vibration and temperature recordings, shaped as multivariate time series and collected on the fly through pervasive sensor networks. Damage detection and localization are treated as a supervised classification task considering a finite number of predefined damage scenarios. During a preliminary offline phase, for each damage scenario, a collection of synthetic structural responses and temperature distributions, is numerically generated through a physics-based model. Several loading and thermal conditions are considered, thanks to a suitable parametrization of the problem, which controls the dependency of the model on operational and environmental conditions. Because of the huge amount of model evaluations, MOR techniques are employed in order to relieve the computational burden that is associated to the dataset construction. Finally, a deep neural network, featuring a stack of convolutional layers, is trained by assimilating both vibrational and thermal data. During the online phase, the trained DL network processes new incoming recordings in order to classify the actual state of the structure, thus providing information regarding the presence and localization of the damage, if any. Numerical performances of the proposed approach are assessed on the monitoring of a two-storey frame under low intensity seismic excitation

Health Monitoring of Civil Structures: A MCMC Approach Based on a Multi-Fidelity Deep Neural Network Surrogate

To meet the need for reliable real-time monitoring of civil structures, safety control and optimization of maintenance operations, this paper presents a computational method for the stochastic estimation of the degradation of the load bearing structural properties. Exploiting a Bayesian framework, the procedure sequentially updates the posterior probability of the damage parameters used to describe the aforementioned degradation, conditioned on noisy sensors observations, by means of Markov chain Monte Carlo (MCMC) sampling algorithms. To enable the analysis to run in real-time or quasi real-time, the numerical model of the structure is replaced with a data-driven surrogate used to evaluate the (conditional) likelihood function. The proposed surrogate model relies on a multi-fidelity (MF) deep neural network (DNN), mapping the damage and operational parameters onto sensor recordings. The MF-DNN is shown to effectively leverage information between multiple datasets, by learning the correlations across models with different fidelities without any prior assumption, ultimately alleviating the computational burden of the supervised training stage. The low fidelity (LF) responses are approximated by relying on proper orthogonal decomposition for the sake of dimensionality reduction, and a fully connected DNN. The high fidelity signals, that feed the MCMC within the outer-loop optimization, are instead generated by enriching the LF approximations through a deep long short-term memory network. Results relevant to a specific case study demonstrate the capability of the proposed procedure to estimate the distribution of damage parameters, and prove the effectiveness of the MF scheme in outperforming a single-fidelity based method

A Multi-Fidelity Deep Neural Network Approach to Structural Health Monitoring

The structural health monitoring (SHM) of civil structures and infrastructures is becoming a crucial issue in our smart and hyper-connected age. Due to structural aging and to unexpected loading conditions, partially linked to extreme events caused by the climate change, reliable and real-time SHM schemes are currently facing a burst in development and applications. In this work, we propose a procedure that relies upon a surrogate modeling scheme based on a multi-fidelity (MF) deep neural network (DNN), which has been conceived to sense and identify a structural damage under operational (and possibly environmental) variability. By exploiting the sensor recordings from a densely deployed network within a fully stochastic framework, the MF-DNN model is adopted to feed a Markov chain Monte Carlo (MCMC) sampling procedure and update the probability distribution of the structural state, conditioned on noisy observations. As information regarding the health of real structures is usually rather limited, the datasets to train the MF-DNN are generated with physical (e.g., finite element) models: high-fidelity (HF) and low-fidelity (LF) models are adopted to simulate the structural response under the mentioned varying conditions, respectively, in the presence or absence of a structural damage. As far as the architecture of the DNN is concerned, the MF approach is obtained by merging a fully connected LF-DNN and a long short-term memory HF-DNN. The LF-DNN mimics the output of the sensor network in the undamaged condition, while the HF-DNN is exploited to improve the LF model and appropriately catch the structural response in the presence of a pre-defined set of damaged patterns. Thanks to the adaptive enrichment of the LF signals carried out by the MF-DNN, the proposed model updating strategy is reported capable of locating (and possibly quantifying) a damage event

Attention Mechanism-Driven Sensor Placement Strategy for Structural Health Monitoring

Automated vibration-based structural health monitoring (SHM) strategies have been recently proven to be promising in the presence of aging and material deterioration threatening the safety of civil structures. Within such a framework, ensuring high-quality and informative data is a critical aspect that is highly dependent on the deployment of the sensors in the network and on their capability to provide damage-sensitive features to be exploited. This paper presents a novel data-driven approach to the optimal sensor placement devised to identify sensor locations that maximize the information effectiveness for SHM purposes. The optimization of the sensor network is addressed by means of a deep neural network (DNN) equipped with an attention mechanism, a state-of-the-art technique in natural language processing (NLP) that is useful in focusing on a limited number of important components in the information stream. The trained attention mechanism eventually allows for quantifying the relevance of each sensor in terms of the so-called attention scores, thereby enabling to identify the most useful input channels to solve the relevant downstream SHM task. With reference to the damage localization task, framed here as a classification problem handling a set of predefined damage scenarios, the DNN is trained to locate damage on labeled data that had been simulated to emulate the effects of damage under different operational conditions. The capabilities of the proposed method are demonstrated by referring to an eight-story shear building, characterized by damage states possibly located at any story and of unknown severity

SHM under varying environmental conditions: an approach based on model order reduction and deep learning

Data-driven approaches to structural health monitoring (SHM) have been recently shown to be a powerful paradigm, helping to lead to an evolution of traditional scheduled-based maintenance methodologies towards condition-based ones. Nevertheless, only few of them provide monitoring scenarios accounting for the varying loading and environmental conditions, which can potentially lead to misinterpretations of the structural state. In this paper, we propose a damage localization strategy that efficiently exploits vibration and temperature data to account for the effects of temperature fluctuations on the structural response. By allowing for a finite number of a priori defined damage scenarios, deep learning techniques are used to handle the damage localization task as a supervised classification, conditioned on temperature data. The training dataset is generated through a parametrized thermo-mechanical model of the structure, under a prescribed variability of loading and thermal conditions. To relieve the computational burden associated to the data generation process, a parametric order reduction strategy is also exploited. Results relevant to two case studies, a cantilever beam and a portal frame, are adopted to testify the capability of the proposed procedure to locate the damage, also when characterized by a rather small reduction of the local stiffness properties

A Generative Adversarial Network Based Autoencoder for Structural Health Monitoring

Civil structures, infrastructures and lifelines are constantly threatened by natural hazards and climate change. Structural Health Monitoring (SHM) has therefore become an active field of research in view of online structural damage detection and long term maintenance planning. In this work, we propose a new SHM approach leveraging a deep Generative Adversarial Network (GAN), trained on synthetic time histories representing the structural responses of both damaged and undamaged multistory building to earthquake ground motion. In the prediction phase, the GAN generates plausible signals for different damage states, based only on undamaged recorded or simulated structural responses, thus without the need to rely upon real recordings linked to damaged conditions

A Generative Adversarial Network Based Autoencoder for Structural Health Monitoring

International audienceCivil structures, infrastructures and lifelines are constantly threatened by natural 1 hazards and climate change. Structural Health Monitoring (SHM) has therefore become an 2 active field of research in view of online structural damage detection and long term maintenance 3 planning. In this work we propose a new SHM approach leveraging a deep Generative Adversarial 4 Network (GAN), trained on synthetic time histories representing the structural responses of both 5 damaged and undamaged multistory building to earthquake ground motion. In the prediction 6 phase, the GAN generates plausible signals for different damage states, based only on undamaged 7 recorded or simulated structural responses, thus without the need to rely upon real recordings 8 linked to damaged conditions