Article: Characteristic mapping using HTTGCN in structural materials Journal: International Journal of Reasoning-based Intelligent Systems (IJRIS) 2026 Vol.18 No.14 pp.49 - 69 Abstract: Existing multi-source heterogeneous data fusion methods either flatten the high-order structure into vectors, losing critical cross-mode interactionsor adopt a two-stage 'decomposition-then-prediction, paradigm that prevents end-to-end optimisation. To address these gaps, this paper proposes hierarchical tensor Tucker-graph convolutional network, a novel framework that integrates hierarchical Tucker decomposition with graph convolutional networks in an end-to-end learnable manner. Multi-source monitoring data is organised as a fourth-order tensor; hierarchical Tucker decomposition explicitly extracts high-order correlation features, while a sensor topology graph enables graph convolutional networks to aggregate spatial dependencies. On the Z24 bridge benchmark, the framework reduces the root mean square error of first-order frequency prediction by 15.8% compared to the state-of-the-art spatial-temporal transformer, with p < 0.001 and a large effect size (Cohen's d = 1.92). The framework provides a highly accurate and interpretable path from damage-sensitive data to quantitative performance assessment in building structural health monitoring. Inderscience Publishers - linking academia, business and industry through research

Title: Characteristic mapping using HTTGCN in structural materials

Authors: Weili Jin

Addresses: School of Engineering Management, Shanxi Vocational University of Engineering Science and Technology, Jinzhong, 030619, China

Abstract: Existing multi-source heterogeneous data fusion methods either flatten the high-order structure into vectors, losing critical cross-mode interactionsor adopt a two-stage 'decomposition-then-prediction, paradigm that prevents end-to-end optimisation. To address these gaps, this paper proposes hierarchical tensor Tucker-graph convolutional network, a novel framework that integrates hierarchical Tucker decomposition with graph convolutional networks in an end-to-end learnable manner. Multi-source monitoring data is organised as a fourth-order tensor; hierarchical Tucker decomposition explicitly extracts high-order correlation features, while a sensor topology graph enables graph convolutional networks to aggregate spatial dependencies. On the Z24 bridge benchmark, the framework reduces the root mean square error of first-order frequency prediction by 15.8% compared to the state-of-the-art spatial-temporal transformer, with p < 0.001 and a large effect size (Cohen's d = 1.92). The framework provides a highly accurate and interpretable path from damage-sensitive data to quantitative performance assessment in building structural health monitoring.

Keywords: building structural materials; tensor computation; multi-source data fusion; graph neural network; feature mapping.

DOI: 10.1504/IJRIS.2026.154236

International Journal of Reasoning-based Intelligent Systems, 2026 Vol.18 No.14, pp.49 - 69

Received: 06 Mar 2026
Accepted: 09 Apr 2026
Published online: 17 Jun 2026 *

Title: Characteristic mapping using HTTGCN in structural materials

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