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This paper mainly discusses the mechanical characteristics of steel-reinforced reactive powder concrete (SRRPC) columns with different geometrical dimensions after high-temperature treatment. The bearing capacity, axial stiffness, and ductility of 35 SRRPC column specimens after high-temperature treatment are investigated, considering two parameters: concrete cover thickness (30–100 mm) and slenderness ratio (3–50). The findings indicate that (1) the ultimate bearing capacity increases by 26.0–135.7% as the concrete cover thickness increases and decreases by 1.5–92.0% as the slenderness ratio increases. (2) The axial stiffness increases by 18.9–92.5% as the concrete cover thickness increases and sharply decreases by 31.9–92.6% as slenderness ratio increases, and change degree decreases. (3) As concrete cover thickness and slenderness ratio increase, the displacement ductility coefficient declines, and degree of decrease in displacement ductility coefficient becomes progressively smaller. (4) The bearing capacity, stiffness, and ductility are extremely sensitive to concrete cover thickness, followed by slenderness ratio. Considering confining effect of stirrups and section steel on concrete in the core area, methods for calculating bearing capacity after high-temperature treatment are proposed. From the perspectives of preventing buckling instability and cracking of components after high temperature, two methods for determining the critical concrete cover thickness are given. Finally, the calculation formula of the stability coefficient corresponding to various slenderness ratios after high-temperature exposure is provided, which provides a reference for postfire evaluation and reinforcement of SRRPC structure. [ABSTRACT FROM AUTHOR]

This paper first explores an alternative non-contact method based on computer vision and explainable machine learning (EML) models to identify and predict vehicle overload cost-effectively. First, 1108 sets of data are extracted from traditional contact measurements, non-contact measurements (Optical Character Recognition and thermal imaging), and literature collection to establish a novel and comprehensive database. The missing value imputation and the randomized search are then selected to find the optimal ML model for further analysis. Moreover, two typical theoretical and five ML models are adopted to evaluate the optimal model's performance. Finally, the sHapley Additive exPlanations (SHAP) is applied to interpret the influence factors of the optimal ML model. The results indicate that the divided length between the tire and the ground is the most significant input feature, followed by the tire's inflation pressure, the section height of tire, and the radius. Finally, the proposed model has great application potential for enhancing the efficiency of non-contact vehicle weight-in-motion (WIM) weighing. [ABSTRACT FROM AUTHOR]

This paper first proposes a novel framework for combining deep learning approach and Synthetic Aperture Radar (SAR) technique to evaluate and predict the condition of bridge displacement. The Long short-term memory (LSTM) neural network and the Small Baseline Subsets InSAR (SBAS-InSAR) are used to predict the longitudinal deformation of the bridge. Firstly, the proposed framework based on LSTM is established to obtain the relationship between the longitudinal deformation of the bridge and the influence parameters (such as temperature, temporal baseline, and others). In particular, the residual connection (RC) module is adapted to form the residual long short-term memory (Res-LSTM) neural network to improve prediction accuracy and robustness. A case study in Nanjing is analyzed to verify the performance of the proposed framework. The extracted datasets obtained from Sentienl-1 are divided into the training, validation, and testing dataset, which can satisfy the requirements in the engineering field. The R 2 and MSE of the testing datasets utilizing the proposed model are 93.17% and 15.62. Furthermore, the results indicated that the proposed framework based on Res-LSTM could predict and warn the abnormal structural deformation, extend the lifespan of structures, and minimize structural damage due to excessive deformation. [ABSTRACT FROM AUTHOR]

Graph convolutional networks (GCNs) have been shown to be effective for hyperspectral image (HSI) classification due to their capacity to learn representations of spatial–spectral features. However, the existing GCN-based models heavily rely on predefined receptive fields to capture and aggregate neighbor information for each node, which limits the ability to adaptively selecting the most significant receptive field from graph data. To address the aforementioned problem, in this article, we propose a novel dynamic adaptive sampling GCN (DAS-GCN) algorithm that captures neighbor information through adaptive sampling to allow the receptive field to be dynamically obtained. The basic underlying idea is that the most meaningful receptive field for each target node can be adaptively discovered, and the edge adjacency weights can be adjusted simultaneously after each adaptive sampling operation. Thus, we enable the graph to be dynamically updated and refined. Specifically, the adaptive sampling operation consists of two complementary components; in the first step, the importance of different remote nodes in a large-scale neighborhood is learned, while in the second step, rich underlying spatial–spectral information is extracted from local neighbors and filtered. The proposed model has the ability to learn how to extensively exploit spectral–spatial correlations from both local and remote nodes. Moreover, the proposed DAS-GCN model has a superior ability to leverage node feature information to naturally generalize and efficiently generate node embeddings for unseen data. The experimental results with overall accuracy on four real HSI datasets, i.e., Indian Pines, Pavia university, Houston 2013, and Salinas are 95.63%, 96.40%, 94.70%, and 99.08%, respectively, which clearly demonstrate the advantages of the proposed method compared with other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

While semisupervised methods based on graph convolutional networks (GCNs) can achieve good results in hyperspectral image (HSI) classification, their performance is limited as they rely only on spatial–spectral similarity when constructing local adjacency graphs. Moreover, relying on local adjacency graphs can limit the ability of the methods to ensure the consistency of global feature in complex hyperspectral remote sensing environments. Using the spatial–spectral information is typically not sufficient for providing a reliable similarity measurement to construct a global graph when the spectral variability is large and the spatial distance is long in intraclass pixels. To address this issue, this article presents a novel globally consistent GCN (GCGCN) for HSI classification. According to the proposed method, a local reliable initial graph is first built from inherent spatial–spectral information by considering this graph as a variable to optimize. Then, adaptive global high-order neighbors are explored to capture the underlying rich spatial contextual information by utilizing the graph topological consistent connectivity instead of the common strategy of using only the spatial–spectral similarity measurement. Finally, the adaptive global high-order graph structure and two-layer networks are combined to achieve the global feature smoothing of the same class samples and maintain high global feature consistency. The proposed GCGCN method is evaluated on three real HSI data sets to demonstrate its superiority compared to ten different classification methods. Moreover, the proposed GCGCN method achieves state-of-the-art classification results on three data sets in terms of four classification evaluation metrics, including overall accuracy (OA), kappa coefficient (KC), average accuracy (AA), and class accuracy (CA). [ABSTRACT FROM AUTHOR]

The plane-wave expansion method and coupled-mode theory were used to calculate the reflection spectra of photonic crystal fiber (PCF) Bragg gratings (BGs) filled with liquids (PCF-BGs-liquids). The simulated reflection spectra of the PCF-BGs-liquids agree well with the reported experimental results. The simulated Bragg wavelength shift of the PCF-BGs filled with optical index matched liquid is 100 pm/ °C as the temperature changes from 25 °C to 65 °C. Therefore, PCF-BGs-liquids can be used as high sensitivity temperature sensors. [ABSTRACT FROM AUTHOR]