Our Research
Real-Time Digital Twins for Smarter Decisions & Resilient Infrastructure
We build hybrid physics–ML twins that learn continuously, optimize automatically and empower operators to act before faults escalate.
Themes in our work
Attribute-Aware Transfer Learning
VAE-GANs and TL inject domain knowledge into data-scarce regions, boosting prediction accuracy for geohazards and extreme events.
Population-Based Damage Analytics
Model ensembles generalize from one bridge to many, enabling rapid post-quake screening across entire networks
Decision-Level Fusion
Multi-sensor outputs are fused probabilistically, delivering explainable, risk-weighted recommendations that operators can trust.
Explore a few of our latest projects:
Featured Project
Transfer Learning with Attributes for Improving the Landslide Spatial Prediction Performance in Sample-Scarce Area Based on Variational Autoencoder Generative Adversarial Network
Owing to the complexity of obtaining the landslide inventory data, it is a challenge to establish a landslide spatial prediction model with limited labeled samples.
This paper proposed a novel strategy, namely transfer learning with attributes (TLAs), to make good use of existing landslide inventory data, a strategy that is based on a variational autoencoder of a generative adversarial network (VAEGAN) for improving the landslide spatial prediction performance in sample-scarce areas. Different from transfer learning (TL), TLAs pre-train the model with the data reconstructed by VAEGAN, so that the models learn in advance the landslide attributes of sample-scarce areas. Accordingly, a database containing a total of 986 landslides in three study areas with 14 landslide-influencing factors was established, and each of the three models, i.e., convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) and gated recurrent units (GRUs), was respectively selected as the feature extractor of the VAEGAN to reconstruct the data with attributes and the prediction model to generate the landslide susceptibility maps to investigate and validate the proposed TLA strategy. The experimental results showed that the TLA strategy increased the mean value of evaluators, such as the area under the receiver-operating characteristic (AUROC), F1-score, precision, recall and accuracy by about 2–7% compared with TL, results that indicated that the generated data have the attribute of specific study areas and the effectiveness of TLA strategy in sample-scarce areas.
Featured Project
Vibration-Based Structural Damage Detection Using 1-D CNN and Transfer Learning
This paper presents a novel vibration-based structural damage detection approach by using a one-dimensional convolutional neural network (1-D CNN) and transfer learning (TL).
The CNN can effectively extract structural damage information from the vibration signals. However, the CNN training needs enough samples, while some damage samples (scenarios) obtained from real structures are limited, which will compromise the CNN ability to detect structural damage. As a solution, the numerical models have potential to provide sufficient CNN training samples; meanwhile, the state-of-the-art TL technique can significantly shorten the network training time and improve the accuracy. Therefore, this paper proposes a new method to detect the damage of a bridge model. The 1-D CNN is firstly trained with the samples of the single damage scenarios of the numerical bridge model. And then it is transferred to the complex scenarios of multi-damage (double or triple simultaneously), random size structures, and experimental model. The results demonstrate that: with the TL, the accuracy of damage detection is increased by about 47% at most, and the convergence speed is increased by at least 50%; in particular, the TL can inhibit over-fitting, and for the real bridge case, the accuracy also increased by 44.4%. It is demonstrated that: the TL can effectively improve the damage detection accuracy and convergence effect, and the application of this method to the random size structures also proves its generalization.
Featured Project
Structural Damage Detection Based on Decision-Level Fusion with Multi-Vibration Signals
When a structure is damaged, its vibration signals change. If a single vibration signal is used for structural damage detection (SDD), it may sometimes lead to low detection accuracy.
To avoid this phenomenon, this paper presents a SDD method based on decision-level fusion (DLF) with multi-vibration signals. In this study, acceleration (ACC), strain (E), displacement (DIS), and the fusion signal of all three of these signals (ACC, E and DIS), are studied. The damage information can be extracted from the vibration signal of a structure by using convolution neural networks (CNN). The above four vibration signals are used as the inputs to train four CNN models, and each model outputs a corresponding result. Finally, a DLF strategy is used to fuse the detection results of each CNN. To demonstrate the effectiveness and correctness of the proposed method, a steel frame bridge is investigated with numerical simulations and vibration experiments. The research shows that the damage detection method based on DLF with multi-vibration signals can effectively improve the accuracy of the CNN damage detection.
