Experimental buckling performance of eucalyptus-based oriented oblique laminated strand lumber columns under centric and eccentric compression

Chen, Jiawei; Xiong, Haibei; Wang, Zhifang; Yang, Linqing

doi:10.1016/j.conbuildmat.2020.120072

Cited by 9 publications

(2 citation statements)

References 28 publications

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“…With the rapid improvement of engineered wood products, timber has also been widely constructed for tall buildings and long-span structures which offers more functions in human society. [1][2][3] However, timber is a biomass construction material, resulting that its mechanical properties are highly influenced by environmental variations and biological actions. Internal cavity eroded by termites is a common degradation pattern, that may cause unexpected structural failures of timber structures.…”

Section: Introductionmentioning

confidence: 99%

An innovative deep neural network–based approach for internal cavity detection of timber columns using percussion sound

Chen

Xiong

Sang

et al. 2021

Structural Health Monitoring

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Timber structures have been a dominant form of construction throughout most of history and continued to serve as a widely used staple of civil infrastructure in the modern era. As a natural material, wood is prone to termite damages, which often cause internal cavities for timber structures. Since internal cavities are invisible and greatly weaken structural load-bearing capacity, an effective method to timber internal cavity detection is of great importance to ensure structural safety. This article proposes an innovative deep neural network (DNN)–based approach for internal cavity detection of timber columns using percussion sound. The influence mechanism of percussion sound with the volume change of internal cavity was studied through theoretical and numerical analysis. A series of percussion tests on timber column specimens with different cavity volumes and environmental variations were conducted to validate the feasibility of the proposed DNN-based approach. Experimental results show high accuracy and generality for cavity severity identification regardless of percussion location, column section shape, and environmental effects, implying great potentials of the proposed approach as a fast tool for determining internal cavity of timber structures in field applications.

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Section: Introductionmentioning

confidence: 99%

An innovative deep neural network–based approach for internal cavity detection of timber columns using percussion sound

Chen

Xiong

Sang

et al. 2021

Structural Health Monitoring

Self Cite

View full text Add to dashboard Cite

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“…Timber structures have been increasingly utilized in building construction due to their excellent seismic performance and environmental friendliness, which mean flexible building function, especially with the improvement of engineering wood products (Cao et al, 2019;Chen et al, 2020;Sun et al, 2020). A variety of novel structural systems have been designed and built into landmarks.…”

Section: Introductionmentioning

confidence: 99%

A Novel Piezoceramic-Based Sensing Technology Combined With Visual Domain Networks for Timber Damage Quantification

et al. 2021

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Early detection of timber damage is essential for the safety of timber structures. In recent decades, wave-based approaches have shown great potential for structural damage assessment. Current damage assessment accuracy based on sensing signals in the time domain is highly affected by the varied boundary conditions and environmental factors in practical applications. In this research, a novel piezoceramic-based sensing technology combined with a visual domain network was developed to quantitatively evaluate timber damage conditions. Numerical and experimental studies reveal the stress wave propagation properties in different cases of timber crack depths. Through the spectrogram visualization process, all sensing signals in the time domain were transferred to images which contain both time and frequency features of signals collected from different crack conditions. A deep neural network (DNN) was adopted for image training, testing, and classification. The classification results show high efficiency and accuracy for identifying crack conditions for timber structures. The proposed technology can be further integrated with a fielding sensing system to provide real-time monitoring of timber damage in field applications.

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Field and 3D Numerical Investigations of the Performances of Stiffened Deep Cement Mixing Column-Supported Embankments Built on Soft Soil

Phutthananon

Jongpradist²,

Wonglert³

et al. 2022

Arab J Sci Eng

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Experimental buckling performance of eucalyptus-based oriented oblique laminated strand lumber columns under centric and eccentric compression

Cited by 9 publications

References 28 publications

An innovative deep neural network–based approach for internal cavity detection of timber columns using percussion sound

An innovative deep neural network–based approach for internal cavity detection of timber columns using percussion sound

A Novel Piezoceramic-Based Sensing Technology Combined With Visual Domain Networks for Timber Damage Quantification

Field and 3D Numerical Investigations of the Performances of Stiffened Deep Cement Mixing Column-Supported Embankments Built on Soft Soil

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