Hao Lyu

Geospatial data contains abundant temporal, spatial, and attribute information related to geographic entities, serving as a critical foundation for the development of new quality productive forces. Achieving effective semantic representation of geographic features is a key prerequisite for enabling intelligent data analysis and application. Spatial relationships are an essential semantic characteristic of geographic features and are among the most commonly introduced characteristics for representation learning in recent studies. However, current research primarily focuses on point-based features (such as POIs) or on converting line and polygon features into point features, leading to inaccuracies in representing spatial proximity between features, which in turn affects the representation outcomes. In this paper, a feature unit delineation method based on geometric shapes is proposed to better construct the proximity relationship graph between features. It enriches the semantic features of feature units using a large language model and trains an edge optimization model to refine the graph structure. Building upon iGCL, a semantic representation learning model for geographic features is constructed. Finally, using Xiamen Island and Nanchang City as study areas, experimental datasets are built by integrating OSM and Amap (Gaode) POI data. The performance of the proposed model is validated and compared with other models through three tasks: semantic overlap calculation, node classification, and node clustering. The results show that the feature representations obtained by the proposed model achieve the best performance across all tasks, fully demonstrating its effectiveness and superiority in geographic feature representation learning, thereby providing a foundation for subsequent intelligent geographic feature tasks.

Crack detection is a long-standing topic in structural health monitoring. Conventional damage detection techniques rely on intensive, time-consuming, resource-intensive intervention. The current trend of crack detection emphasizes using deep neural networks to build an automated pipeline from measured signals to damaged areas. This work focuses on the seismic-wave-based technique of crack detection for plate structures. Previous work proposed an encoder–decoder network to extract crack-related wave patterns from measured wave signals and predict crack existence on the plate. We extend previous work with extensive experiments on different network components and a data preprocessing strategy. The proposed methods are tested on an expanded crack detection dataset. We found that a robust backbone network, such as Densely Connected Convolutional Network (DenseNet) can effectively extract the features characterizing cracks of wave signals, and by using the reference wave field for normalization, the accuracy of detecting small cracks can be further improved.

Physics-informed neural network (PINN) is a data-driven solver for partial and ordinary differential equations(ODEs/PDEs). It provides a unified framework to address both forward and inverse problems. However, the complexity of the objective function often leads to training failures. This issue is particularly prominent when solving high-frequency and multi-scale problems. We proposed using transfer learning to boost the robustness and convergence of training PINN, starting training from low-frequency problems and gradually approaching high-frequency problems. Through two case studies, we discovered that transfer learning can effectively train PINN to approximate solutions from low-frequency problems to high-frequency problems without increasing network parameters. Furthermore, it requires fewer data points and less training time. We elaborately described our training strategy, including optimizer selection, and suggested guidelines for using transfer learning to train neural networks for solving more complex problems.

Crack detection is a long-standing topic in structural health monitoring. Conventional damage detection techniques rely on intensive, time-consuming, resource-intensive intervention. The current trend of crack detection emphasizes using deep neural networks to build an automated pipeline from measured signals to damaged areas. This work focuses on the seismic-wave-based technique of crack detection for plate structures. Previous work proposed an autoencoder network to extract crack-related wave patterns from measured wave signals and predict crack existence on the plate. We extend previous work with extensive experiments on different network components and a data preprocessing strategy. The proposed methods are tested on an expanded crack detection dataset. We found that a robust backbone network, such as DenseNet can effectively extract the features characterizing cracks of wave signals, and by using the reference wave field for normalization, the accuracy of detecting small cracks can be further improved.

The identification of structural damages takes a more and more important role within the modern economy, where often the monitoring of an infrastructure is the last approach to keep it under public use. Conventional monitoring methods require specialized engineers and are mainly time-consuming. This research paper considers the ability of neural networks to recognize the initial or alteration of structural properties based on the training processes. The presented model, a spatially asymmetric encoder–decoder network, is based on 1D-Convolutional Neural Networks (CNN) for wave field pattern recognition, or more specifically the wave field change recognition. The proposed model is used to identify the change within propagating wave fields after a crack initiation within the structure. The paper describes the implemented method and the required training procedure to get a successful crack detection accuracy, where the training data are based on the dynamic lattice model. Although the training of the model is still time-consuming, the proposed new method has an enormous potential to become a new crack detection or structural health monitoring approach within the conventional monitoring methods.