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Timber damage identification using dynamic broad network and ultrasonic signals

https://doi.org/10.1016/j.engstruct.2022.114418

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Wood hole-damage detection and classification via contact ultrasonic testing

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Wood consumption and fixations of carbon dioxide and carbon from timber housing techniques: A Brazilian panorama

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Application of the modified damage index method to timber beams

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A novel unsupervised deep learning model for global and local health condition assessment of structures

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Structural damage identification based on autoencoder neural networks and deep learning

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A systematic review of convolutional neural network-based structural condition assessment techniques

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A two-step interval structural damage identification approach based on model updating and set-membership technique

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Feature extraction of wood-hole defects using empirical mode decomposition of ultrasonic signals

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inferredcites →https://doi.org/10.1061/(asce)cf.1943-5509.0000528openalex-snapshot

Damage Assessment of Connections Used in Cross-Laminated Timber Subject to Cyclic Loads

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Timber moisture detection using wavelet packet decomposition and convolutional neural network

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Timber Health Monitoring using piezoelectric sensor and machine learning

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Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

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Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture

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Technology pathway of efficient and climate-friendly cooling in buildings: Towards carbon neutrality

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Wavelet packet energy–based damage identification of wood utility poles using support vector machine multi-classifier and evidence theory

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A machine learning–based approach for adaptive sparse time–frequency analysis used in structural health monitoring

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Bolt damage identification based on orientation-aware center point estimation network

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Experimental study on Chinese ancient timber-frame building by shaking table test

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Surface Cracking and Fractal Characteristics of Bending Fractured Polypropylene Fiber-Reinforced Geopolymer Mortar

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Experimental Study on Damage Detection in Timber Specimens Based on an Electromechanical Impedance Technique and RMSD-Based Mahalanobis Distance

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A Feasibility Study on Timber Damage Detection Using Piezoceramic-Transducer-Enabled Active Sensing

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4781147openalex-extracted

Identification of Damage in Timber I-Beams by Means of Continuous Wavelet Transform of Deflection Measured by Digital Image Correlation

inferred← citeshttps://doi.org/10.1016/j.conbuildmat.2023.133062openalex-snapshot

Time–frequency analysis of ultrasonic signals for quality assessment of bonded concrete

inferred← citeshttps://doi.org/10.1016/j.conbuildmat.2024.135466openalex-snapshot

Damage identification for railway tracks using ultrasound guided wave and hybrid probabilistic deep learning

inferred← citeshttps://doi.org/10.1016/j.conbuildmat.2025.143880openalex-snapshot

The potential application of ultrasonic testing in the in-situ assessment of Dou components in Chinese timber heritage conservation

inferred← citeshttps://doi.org/10.1016/j.eng.2024.08.023openalex-snapshot

Smart Techniques Promoting Sustainability in Construction Engineering and Management

inferred← citeshttps://doi.org/10.1016/j.engfailanal.2025.109709openalex-snapshot

Prevention of failure of heritage buildings through diagnostic testing using the example of testing of roof structure elements from the 15th century

inferred← citeshttps://doi.org/10.1016/j.engstruct.2022.115575openalex-snapshot

Time-frequency fusion features-based incremental network for smartphone measured structural seismic response classification

inferred← citeshttps://doi.org/10.1016/j.engstruct.2024.118353openalex-snapshot

Quantitative analysis of debonding gaps in concrete-filled steel tubes on the Qinghai-Tibet Plateau under severely harsh conditions

inferred← citeshttps://doi.org/10.1016/j.istruc.2026.111480openalex-snapshot

Structural damage identification based on image compressed sensing and unsupervised learning

inferred← citeshttps://doi.org/10.1016/j.jsv.2024.118646openalex-snapshot

Vibration-based methods for local damage identification of breathing cracks in truss-like structures

inferred← citeshttps://doi.org/10.1016/j.measurement.2024.115961openalex-snapshot

Identification of damage in timber I-beams using continuous wavelet transform of deflection measured with digital image correlation

inferred← citeshttps://doi.org/10.1080/17480272.2025.2604624openalex-snapshot

Machine learning for structural response assessment in timber engineering: a review on mechanical performance, durability, and fire resistance

inferred← citeshttps://doi.org/10.1109/sibgrapi55357.2022.9991803openalex-snapshot

Fault Classification on Melamine Faced Panels Using Local Binary Pattern

inferred← citeshttps://doi.org/10.1134/s1061830924600217openalex-snapshot

Application Progress and Prospect of Defect Detection Technology for Timber Structure Members

inferred← citeshttps://doi.org/10.1177/16878132221122770openalex-snapshot

Review of artificial intelligence-based bridge damage detection

inferred← citeshttps://doi.org/10.12783/shm2023/37014openalex-snapshot

HYBRID PROBABILISTIC DEEP LEARNING FOR DAMAGE IDENTIFICATION

inferred← citeshttps://doi.org/10.31857/s0130308224040061openalex-snapshot

Calculation of the DAC curve for images reconstructed by digital aperture focusing method

inferred← citeshttps://doi.org/10.3389/fphy.2023.1290880openalex-snapshot

Dynamic displacement estimation of structures using one-dimensional convolutional neural network

inferred← citeshttps://doi.org/10.3390/app12189370openalex-snapshot

Intelligent Timber Damage Monitoring Using PZT-Enabled Active Sensing and Intrinsic Multiscale Entropy Analysis

inferred← citeshttps://doi.org/10.3390/app13010140openalex-snapshot

Structural Damage Detection Based on One-Dimensional Convolutional Neural Network

inferred← citeshttps://doi.org/10.59440/ceer/174568openalex-snapshot

Restrictions and New Possibilities of Technical and Conservation Diagnostics of Wood in Heritage Buildings

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