Application of Artificial Neural Networks to Predict Insulation Properties of Lightweight Concrete
Source abstract
Abstract
Predicting the properties of concrete before its design and application process allows for refining and optimizing its composition. However, the properties of lightweight concrete are much harder to predict than those of normal weight concrete, especially if the forecast concerns the insulating properties of concrete with artificial lightweight aggregate (LWA). It is possible to use porous aggregates and precisely modify the composition of lightweight concrete (LWC) with specific insulating properties. In this case, it is advisable to determine the parameters of the components and perform preliminary laboratory tests, and then use theoretical methods (e.g., artificial neural networks (ANNs) to predict not only the mechanical properties of lightweight concrete, but also its thermal insulation properties. Fifteen types of lightweight concrete, differing in light filler, were tested. Lightweight aggregates with different grain diameters and lightweight aggregate grains with different porosity were used. For the tests, expanded glass was applied as a filler with very good thermal insulation properties and granulated sintered fly ash, characterized by a relatively low density and high crushing strength in the group of LWAs. The aim of the work is to demonstrate the usefulness of an ANN for the determination of the relationship between the selection of the type and quantity of LWA and porosity, density, compressive strength, and thermal conductivity (TC) of the LWC.
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Research context
OpenAlex topics: Concrete and Cement Materials Research · Innovative concrete reinforcement materials · Concrete Properties and Behavior
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Civil and Environmental Engineering, Gdańsk University of Technology, ul. Narutowicza 11/12, 80-233 Gdańsk, Poland
Faculty of Applied Physics and Mathematics, Gdańsk University of Technology, ul. Narutowicza 11/12, 80-233 Gdańsk, Poland
References (37)
Show source-provided references
1. Mechanical and Thermal Properties of Lightweight Concrete Made from Expanded Glass
2. Chen, G., Li, F., Jing, P., Geng, J., and Si, Z. (2021). Effect of pore structure on thermal conductivity and mechanical properties of autoclaved aerated concrete. Materials, 14.10.3390/ma14020339 ↗
3. Development of flowable ultra-lightweight concrete using expanded glass aggregate, silica aerogel, and prefabricated plastic bubbles10.1016/j.jobe.2020.101399 ↗
4. Kurpińska, M., Karwacki, J., Maurin, A., and Kin, M. (2021). Measurements of thermal conductivity of LWC cement composites using simplified laboratory scale method. Materials, 14.10.3390/ma14061351 ↗
5. Behaviour of a concrete wall containing micro-encapsulated PCM after a decade of its construction10.1016/j.solener.2019.12.003 ↗
6. Impact of lightweight aggregate on concrete thermal properties
7. The Effect of Physical Properties on Thermal Conductivity of Lightweight Aggregate10.1016/j.proeps.2015.08.022 ↗
8. Lightweight concrete based on crushed foam glass aggregate10.1088/1757-899X/1083/1/012038 ↗
9. Theoretical backgrounds of non-tempered materials production based on new raw materials10.1088/1757-899X/327/4/042064 ↗
10. Temperature-dependent thermal conductivity of high strength lightweight raw perlite aggregate concrete10.1007/s10765-010-0826-5 ↗
11. Yousefi, A., Tang, W., Khavarian, M., Fang, C., and Wang, S. (2020). Thermal and mechanical properties of cement mortar composite containing recycled expanded glass aggregate and nano titanium dioxide. Appl. Sci., 10.10.3390/app10072246 ↗
12. Temperature Effect on the Thermal Conductivity of Expanded Polystyrene Foamed Concrete: Experimental Investigation and Model Correction10.1155/2019/8292379 ↗
13. Kurpinska, M., Grzyl, B., and Kristowski, A. (2019). Cost analysis of prefabricated elements of the ordinary and lightweight concrete walls in residential construction. Materials, 12.10.3390/ma12213629 ↗
14. Kurpińska, M., and Haustein, E. (2021). Experimental Study of the Resistance to Influence of Aggressive Liquids on Lightweight Concrete. Materials, 14.10.3390/ma14154185 ↗
15. Kurpińska, M., and Ferenc, T. (2020). Experimental and numerical investigation of mechanical properties of light weight concretes (LWCs) with various aggregates. Materials, 13.10.3390/ma13163474 ↗
16. Thienel, K.-C. (2018). Gefügedichter und Haufwerksporiger Leichtbeton, Der Bundeswehr Universität München.
17. Prediction of lightweight concrete strength by categorized regression, MLR and ANN10.12989/cac.2013.12.2.151 ↗
18. Modeling slump of ready mix concrete using artificial neural network10.14716/ijtech.v6i2.213 ↗
19. Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming10.1155/2016/7648467 ↗
20. An Artificial Neural Network Model to Predict the Thermal Properties of Concrete Using Different Neurons and Activation Functions10.1155/2019/3831813 ↗
21. Prediction of Fresh and Hardened Properties of Self-Consolidating Concrete Using Neurofuzzy Approach10.1061/(ASCE)0899-1561(2009)21:11(672) ↗
22. Neuro-fuzzy systems in determining light weight concrete strength10.1007/s11771-019-4223-3 ↗
23. Aneja, S., Sharma, A., Gupta, R., and Yoo, D.Y. (2021). Bayesian regularized artificial neural network model to predict strength characteristics of fly-ash and bottom-ash based geopolymer concrete. Materials, 14.10.3390/ma14071729 ↗
24. Using Artificial Neural Network to Predict the Compressive Strength of Concrete containing Nano-silica10.13189/cea.2013.010306 ↗
25. EN 197-1:2011 Cement (2021, November 07). Composition, Specifications and Conformity Criteria for Common Cements, Available online: http://www.puntofocal.gov.ar/notific_otros_miembros/mwi40_t.pdf.
26. EN 12350-2:2019 Testing Fresh Concrete (2021, November 07). Slump-Test. Available online: https://standards.iteh.ai/catalog/standards/cen/cf0e0511-2176-454c-948d-9e515f3a03f1/en-12350-2-2019.
27. EN 206:2013+A1:2016 Concrete (2021, November 07). Requirements, Properties, Manufacturing Compatibility. Available online: https://www.techstreet.com/standards/bs-en-206-2013-a1-2016?product_id=1936327.
28. EN 12350-1:2019 Testing Fresh Concrete (2021, November 07). Sampling and Common Apparatus. Available online: https://civilnode.com/download-standard/10677746504383/bs-en-12350-12019-testing-fresh-concrete-part-1-sampling-and-common-apparatus.
29. EN 12390-4:2019 Testing Hardened Concrete (2021, November 07). Compressive Strength. Specification for Testing Machines. Available online: https://standards.iteh.ai/catalog/standards/cen/10b1c613-819b-42d7-8f94-480cd37a666a/en-12390-4-2019.
30. Lee, N.K., Souri, H., and Lee, H.K. (2014, January 24–28). Neural Network Application Overview in Prediction of Properties of Cement-Based Mortar and Concrete. Proceedings of the 2014 World Congress on Advances in Civil Environmental and Materials Research, Busan, Korea.
31. Kaplan, G., Yaprak, H., Memiş, S., and Alnkaa, A. (2019). Artificial neural network estimation of the effect of varying curing conditions and cement type on hardened concrete properties. Buildings, 9.10.3390/buildings9010010 ↗
32. Strength properties and durability aspects of sintered-fly-ash lightweight aggregate concrete10.17222/mit.2019.101 ↗
33. Predicting performance of self-compacting concrete mixtures using artificial neural networks
34. Comparison of concrete strength prediction techniques with artificial neural network
35. Approximation theory of the MLP model in neural networks10.1017/S0962492900002919 ↗
36. Kurpinska, M., and Kułak, L. (2019). Predicting performance of lightweight concrete with granulated expanded Glass and Ash aggregate by means of using Artificial Neural Networks. Materials, 12.10.3390/ma12122002 ↗
37. On the optimality of neural-network approximation using incremental algorithms10.1109/72.839004 ↗