Characteristic and allowable compressive strengths of Dendrocalamus Sericeus bamboo culms with/without node using artificial neural networks
Source abstract
Abstract
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 “Internode” 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.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Bamboo properties and applications · Natural Fiber Reinforced Composites · Tree Root and Stability Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (39)
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1. M. Pathak, R. Slade, R. Pichs-Madruga, D. Ürge-Vorsatz, P.R. Shukla, J. Skea, H. Winkler, Working Group III Contribution to the IPCC Sixth Assessment Report (AR6) (Technical Summary), 2022.
2. N. Tanaka, B. Stigson, Cement technology roadmap 2009—carbon emissions reductions up to 2050, Report. Geneva: World Business Council for Sustainable Development, 2009.
3. A new concept of bio-based prestress technology with experimental Proof-of-Concept on Bamboo-Timber composite beams10.1016/j.conbuildmat.2023.132991 ↗
4. The global distribution of bamboos: assessing correlates of introduction and invasion
5. Bamboo as a sustainable material in the construction industry: An overview10.1016/j.matpr.2021.01.125 ↗
6. A low-technology approach toward fabrication of laminated bamboo lumber10.1016/j.conbuildmat.2011.10.046 ↗
7. Bamboo as a sustainable building material—culm characteristics and properties10.3390/su13137376 ↗
8. Performance analysis of concrete slab reinforced with steel and bamboo10.31357/ait.v2i2.5441 ↗
9. Seismic building damage prediction from GIS-based building data using artificial intelligence system10.3389/fbuil.2020.576919 ↗
10. Multi-hazard disaster resilient housing with bamboo-based system10.1016/j.proeng.2018.01.121 ↗
11. Seismic loss estimation and reduction after structural rehabilitation in Chiang Rai city
12. Structural repair prioritization of buildings damaged after earthquake using fuzzy logic model10.20965/jdr.2016.p0559 ↗
13. Tensile properties of Moso bamboo (Phyllostachys pubescens) and its components with respect to its fiber-reinforced composite structure10.1007/s00226-009-0290-1 ↗
14. The effect of bamboo water content on the tensile strength of bamboo
15. M.J. Richard, Assessing the performance of bamboo structural components, PhD dissertation, University of Pittsburgh, Pittsburgh, PA, USA, 2013.
16. Bamboo taper effect on third point loading bending test
17. Evaluation of the compressive strength of bamboo culms under node and internode conditions
18. Identification of barriers, benefits and opportunities of using bamboo materials for structural purposes
19. International Organization for Standardization, ISO 22157–1:2019; Bamboo - Determination of physical and mechanical properties — Part 1, 2019.
20. International Organization for Standardization, ISO 19624:2018; Bamboo structures - Grading of bamboo culms - Basic principles and procedures, 2018.
21. Compression resistance of short members as the basis for structural grading of Guadua angustifolia10.1016/j.conbuildmat.2020.118759 ↗
22. Abdurachman, Structural grading of three sympodial bamboo culms (Hitam, Andong, and Tali) subjected to axial compressive load10.1016/j.engstruct.2018.12.026 ↗
23. Analysis of non-destructive indicating properties for predicting compressive strengths of Dendrocalamus sericeus Munro bamboo culms10.3390/ma16041352 ↗
24. Engineering properties of concrete incorporating waste glass as natural sand substitution with tin can fiber: experimental and ANN application10.1186/s44147-023-00224-6 ↗
25. Artificial neural network based modelling approach for strength prediction of concrete incorporating agricultural and construction wastes10.1016/j.conbuildmat.2018.09.097 ↗
26. ANN, M5P-tree model, and nonlinear regression approaches to predict the compression strength of cement-based mortar modified by quicklime at various water/cement ratios and curing times10.1007/s12517-020-06199-5 ↗
27. Hybrid MARS‑, MEP‑, and ANN‑based prediction for modeling the compressive strength of cement mortar with various sand size and clay mineral metakaolin content10.1007/s43452-022-00519-0 ↗
28. Prediction of FRCM-concrete bond strength with machine learning approach10.3390/su14020845 ↗
29. Artificial neural networks (ANN), MARS, and adaptive network-based fuzzy inference system (ANFIS) to predict the stress at the failure of concrete with waste steel slag coarse aggregate replacement10.1007/s00521-023-08439-7 ↗
30. Inference of mechanical properties and structural grades of bamboo by machine learning methods10.1016/j.conbuildmat.2022.129116 ↗
31. Buckling behavior of Dendrocalamus sericeus Munro bamboo bars: Experiments and application
32. International Organization for Standardization, ISO 22156:2021, Bamboo–Structural Design, 2021.
33. Chapter 4 - Classification
34. Residual strength of reinforced concrete beams under sequential small impact loads10.3390/buildings11110518 ↗
35. Analysis of colliding index on impact behavior of RC columns under repeated impact loading
36. Flexural reinforced concrete members with minimum reinforcement under low-velocity impact load10.21660/2018.46.STR107 ↗
37. Capacity design criteria for seismic resistance of precast concrete columns using steel box connection10.21660/2018.45.7360 ↗
38. Structural use of bamboo. Part 3: Design values10.56330/JCLL5610 ↗
39. Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis10.5395/rde.2013.38.1.52 ↗