Characteristic and allowable compressive strengths of Dendrocalamus Sericeus bamboo culms with/without node using artificial neural networks

被引:7
作者
Buachart, Chinnapat [1 ]
Hansapinyo, Chayanon [1 ]
Sukontasukkul, Piti [2 ]
Zhang, Hexin [3 ]
Sae-Long, Worathep [3 ]
Chetchotisak, Panatchai [4 ]
O'Brien, Timothy E. [4 ]
机构
[1] Chiang Mai Univ, Fac Engn, Dept Civil Engn, Chiang Mai 50200, Thailand
[2] King Mongkuts Univ Technol North Bangkok, Construct & Bldg Mat Res Ctr, Dept Civil Engn, Bangkok 10800, Thailand
[3] Edinburgh Napier Univ, Sch Comp Engn & Built Environm, Edinburgh EH14 1DJ, Scotland
[4] Rajamangala Univ Technol Isan, Dept Civil Engn, Khon Kaen Campus, Khon Kaen 40000, Thailand
关键词
Compressive strength prediction; Dendrocalamus Sericeus; Indicative properties; Artificial neural network; Characteristic strength; Allowable strength;
D O I
10.1016/j.cscm.2023.e02794
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The strength of construction material is a crucial consideration in the process of structural design and construction. Conventional materials such as concrete or steel have been widely utilized due to their predictable material performance. However, a significant obstacle to the widespread use of bamboo in structural elements lies in the challenge of its standardization. Many previous research studies have explored bamboo's load bearing capacity, but the information remains limited due to variations in species, size, age, physical properties, moisture content, and other factors, making it difficult to predict their load-bearing capacity. This study aims to propose Artificial Neural Network (ANN) models to predict ultimate compressive load and compressive strength of Dendrocalamus Sericeus bamboo culm. Additionally, for structural design purposes, the proposed ANN models were employed to determine the characteristic and allowable compressive strengths. As a first step, experimental data from compressive tests in the literature were used for training and developing the ANN model. To investigate the effect of the node on compressive loading capacities, the test data were separated into two datasets, "Node" samples and "Inter -node" samples. Through the training process, ANN models were finally proposed, and the R -square values for the prediction of ultimate compressive load and compressive strength from the proposed ANN models were significantly higher than those obtained from the linear regression analyses used in the literature. Subsequently, the characteristic and allowable compressive strengths were calculated and compared to the strengths obtained from the experiment data, revealing a difference of approximately only 8.0%. Overall, the ANN models presented in this study offer promising predictive ability for both ultimate compressive load and compressive strength of Dendrocalamus Sericeus bamboo culm, as well as for determining characteristic and allowable strengths. Hence, ANN models are suggested to be adopted as a tool for the design and construction of bamboo buildings.
引用
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页数:14
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