All Issue

2024 Vol.15, Issue 3 Preview Page

General Article

Experimental and numerical exploration of mechanical properties of sawdust concrete due to the addition of waste glass powder
Ehsan Farahinia1
,
Morteza Sheikhi2
,
Davarpanah Tanha Quchan Amirhossein3
,
Arash Rajaee4*
,
Morteza Ghodratnama4
,
Seyed Mostafa Banihashem5
,
Pooyan Pournoori1
1M.Sc. Student, Department of Civil Engineering, Khayyam University of Mashhad, Mashhad, Iran
2Assistant Professor, Department of Civil Engineering, Khayyam University of Mashhad, Mashhad, Iran
3M.Sc. Student, Department of Civil Engineering, Egbal Lahoori Institute of Higher Education, Mashhad, Iran
4M.Sc. Student, Department of Civil Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
5Ph.D. Student, Department of Civil Engineering, University of Texas at Arlington, Texas, USA
*Corresponding Author
30 September 2024. pp. 328-353
PDF XML Citation
Abstract

This paper investigates the use of sawdust as fine aggregate and waste glass powder (WGP) as a replacement for cement and their influence on the properties of hardened concrete. Sawdust, also known as wood dust, is the by-product generated when wood is cut or drilled using a saw or other cutting tools, and waste glass powder (WGP) exhibits pozzolanic properties, leading to the production of durable, affordable, and sustainable concrete. Both sawdust and waste glass powder (WGP) pose environmental concerns. In this research, we produced eco-friendly, lightweight, and cost-effective sawdust concrete mixtures with different dosages of WGP (15%, 20%, and 30%). The results reveal that with increasing the content of sawdust and WGP, a significant drop can be seen in the compressive strength of mixtures. However, an average of 15% reduction in density was seen in the samples compared to the control mixture, and the strength of all samples, particularly those containing WGP, increased as the curing time extended. Furthermore, to reduce costs and avoid time wastage, artificial neural network (ANN) and genetic expression programming (GEP) prediction models were used to predict the compressive strengths. The results of this modeling confirm that using ANN and GEP models for prognosticating compressive strength has a robust and reliable approach.

Keywords
cement concrete
sawdust
waste glass powder (WGP)
gep modeling
artificial neural network (ANN)
References
1

M. Záleská, M. Pavlíková, J. Pokorný, O. Jankovský, Z. Pavlík, and R. Černý, Structural, mechanical and hygrothermal properties of lightweight concrete based on the application of waste plastics. Construction and Building Materials. 180 (2018), pp. 1-11.

10.1016/j.conbuildmat.2018.05.250
2

P. Pournoori, A. Davarpanah TQ, A. Rajaee, M. Ghodratnama, S. Abrishami, and A.R. Masoodi, Experimental exploration of fracture behavior (pure mode III) in eco-friendly steel fiber-reinforced self-compacting concrete with waste tempered glass as coarse aggregates. Scientific Reports. 14(1) (2024), 9043.

10.1038/s41598-024-58912-z
3

F. Colangelo, R. Cioffi, B. Liguori, and F. Iucolano, Recycled polyolefins waste as aggregates for lightweight concrete, Composites Part B: Engineering. 106 (2016), pp. 234-241.

10.1016/j.compositesb.2016.09.041
4

U.J. Alengaram, B.A. Al Muhit, and M.Z. bin Jumaat, Utilization of oil palm kernel shell as lightweight aggregate in concrete-A review. Construction and building materials. 38 (2013), pp. 161-172.

10.1016/j.conbuildmat.2012.08.026
5

R. Demirboğa, İ. Örüng, and R. Gül, Effects of expanded perlite aggregate and mineral admixtures on the compressive strength of low-density concretes. Cement and Concrete Research. 31(11) (2001), pp. 1627-1632.

10.1016/S0008-8846(01)00615-9
6

T. Ganiron, Effect of sawdust as fine aggregate in concrete mixture for building construction. International Journal of Advanced Science and Technology. 63(1) (2014), pp. 73-82.

10.14257/ijast.2014.63.07
7

A. Sawant, A. Sharma, R. Rahate, N. Mayekar, and M. Ghadge, Partial replacement of sand with sawdust in concrete. Int Res J Eng Technol. 5 (2018), pp. 3098-3101.

8

B. Alsubari, P. Shafigh, M.Z. Jumaat, H.H. Ghayeb, and S. Alqawzai, Self-compacting concrete containing high volume palm oil fuel ash: cost, sustainability, and applications. International Journal of Sustainable Building Technology and Urban Development. 13(2) (2022), pp. 148-154.

9

R. Siddique, Properties of concrete made with volcanic ash. Resources, Conservation and Recycling. 66 (2012), pp. 40-44.

10.1016/j.resconrec.2012.06.010
10

E. Özbay, M. Erdemir, and H.İ. Durmuş, Utilization and efficiency of ground granulated blast furnace slag on concrete properties-A review. Construction and Building Materials. 105 (2016), pp. 423-434.

10.1016/j.conbuildmat.2015.12.153
11

V. Corinaldesi, G. Moriconi, and T.R. Naik, Characterization of marble powder for its use in mortar and concrete. Construction and Building Materials. 24(1) (2010), pp. 113-117.

10.1016/j.conbuildmat.2009.08.013
12

S.A. Khedr and M.N. Abou-Zeid, Characteristics of silica-fume concrete. Journal of Materials in Civil Engineering. 6(3) (1994), pp. 357-375.

10.1061/(ASCE)0899-1561(1994)6:3(357)
13

M.S. Baradaran, R. Qazanfari, and S. Baradaran, Study of soil reinforcement in the east of Mashhad using glass granule. Materials Research Express. 10(5) (2023), 055202.

10.1088/2053-1591/acd5af
14

H.S. Abed, Z.H. Al-Saffar, and A.J. Hamad, Synergistic effect of the silica fume and glass powder as pozzolanic materials in cement mortar. International Journal of Sustainable Building Technology and Urban Development. 15(1) (2024), pp. 43-56.

15

D. Paul and B.K. Rugmini, Utilisation of glass powder in reinforced concrete beams for a green environment. International Journal of Sustainable Building Technology and Urban Development. 14(4) (2023), pp. 437-454.

16

A.A. Aliabdo, M. Abd Elmoaty, and A.Y. Aboshama, Utilization of waste glass powder in the production of cement and concrete. Construction and Building Materials. 124 (2016), pp. 866-877.

10.1016/j.conbuildmat.2016.08.016
17

K. Ibrahim, Recycled waste glass powder as a partial replacement of cement in concrete containing silica fume and fly ash. Case Studies in Construction Materials. 15 (2021), e00630.

10.1016/j.cscm.2021.e00630
18

S. BaridJavan, M. Sheikhi, P. Pournoori, and A. Rajaee, Glass powder and PVC granules as partial replacement of cement and aggregate; An experimental study, presented at the 7th. International Conference on Applied Researches in Science and Engineering. (2023). Aachen, Germany.

19

R. Siddique, M. Singh, S. Mehta, and R. Belarbi, Utilization of treated saw dust in concrete as partial replacement of natural sand. Journal of cleaner production. 261 (2020), 121226.

10.1016/j.jclepro.2020.121226
20

W. Ahmed, R.A. Khushnood, S.A. Memon, S. Ahmad, W.L. Baloch, and M. Usman, Effective use of sawdust for the production of eco-friendly and thermal-energy efficient normal weight and lightweight concretes with tailored fracture properties. Journal of Cleaner Production. 184 (2018), pp. 1016-1027.

10.1016/j.jclepro.2018.03.009
21

Y. Cheng, W. You, C. Zhang, H. Li, and J. Hu, The implementation of waste sawdust in concrete. Engineering. 5(12) (2013), 943.

10.4236/eng.2013.512115
22

O.J. Oyedepo, S.D. Oluwajana, and S.P. Akande, Investigation of properties of concrete using sawdust as partial replacement for sand. Civil and Environmental Research. 6(2) (2014), pp. 35-42.

23

D.Y. Osei and E.N. Jackson, Compressive strength of concrete using sawdust as aggregate. International Journal of Scientific & Engineering Research. 7(4) (2016), pp. 1349-1353.

24

F. Olutoge, Investigations on sawdust and palm kernel shells as aggregate replacement. ARPN Journal of Engineering and Applied Sciences. 5(4) (2010), pp. 7-13.

25

A. Raheem, B. Olasunkanmi, and C. Folorunso, Saw dust ash as partial replacement for cement in concrete. Organization, Technology & Management in Construction: An International Journal. 4(2) (2012), pp. 474-480.

10.5592/otmcj.2012.2.3
26

C. Marthong, Sawdust ash (SDA) as partial replacement of cement. International Journal of Engineering Research and Applications. 2(4) (2012), pp. 1980-1985.

27

S.O. Folagbade and A.S. Olatunji, Effect of Glass Waste Powder on the Compressive Strength and Permeation Properties of Concrete Containing Portland Cement and Sawdust Ash. Civil and Environmental Research. 12(4) (2020), pp. 36-48.

28

P. Turgut and H.M. Algin, Limestone dust and wood sawdust as brick material. Building and Environment. 42(9) (2007), pp. 3399-3403.

10.1016/j.buildenv.2006.08.012
29

C. Bilim, C.D. Atiş, H. Tanyildizi, and O. Karahan, Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network. Advances in Engineering Software. 40(5) (2009), pp. 334-340.

10.1016/j.advengsoft.2008.05.005
30

H. Naderpour, A. Kheyroddin, and G.G. Amiri, Prediction of FRP-confined compressive strength of concrete using artificial neural networks. Composite Structures. 92(12) (2010), pp. 2817-2829.

10.1016/j.compstruct.2010.04.008
31

S. Gupta, Using artificial neural network to predict the compressive strength of concrete containing nano-silica. Civil Engineering and Architecture. 1(3) (2013), pp. 96-102.

10.13189/cea.2013.010306
32

L. Shi, S. Lin, Y. Lu, L. Ye, and Y. Zhang, Artificial neural network based mechanical and electrical property prediction of engineered cementitious composites. Construction and Building Materials. 174 (2018), pp. 667-674.

10.1016/j.conbuildmat.2018.04.127
33

A.A. Shahmansouri, M. Yazdani, S. Ghanbari, H.A. Bengar, A. Jafari, and H.F. Ghatte, Artificial neural network model to predict the compressive strength of eco-friendly geopolymer concrete incorporating silica fume and natural zeolite. Journal of Cleaner Production. 279 (2021), 123697.

10.1016/j.jclepro.2020.123697
34

A.S. Hosseini, P. Hajikarimi, M. Gandomi, F.M. Nejad, and A.H. Gandomi, Optimized machine learning approaches for the prediction of viscoelastic behavior of modified asphalt binders. Construction and Building Materials. 299 (2021), 124264.

10.1016/j.conbuildmat.2021.124264
35

A.H. Gandomi and A.H. Alavi, Applications of computational intelligence in behavior simulation of concrete materials. Computational Optimization and Applications in Engineering and Industry. (2011), pp. 221-243.

10.1007/978-3-642-20986-4_9
36

A. Ahmad, K. Chaiyasarn, F. Farooq, W. Ahmad, S. Suparp, and F. Aslam, Compressive strength prediction via gene expression programming (GEP) and artificial neural network (ANN) for concrete containing RCA. Buildings. 11(8) (2021), 324.

10.3390/buildings11080324
37

P.G. Asteris, P.C. Roussis, and M.G. Douvika, Feed-forward neural network prediction of the mechanical properties of sandcrete materials. Sensors. 17(6) (2017), 1344.

10.3390/s17061344
38

T.-A. Nguyen, H.-B. Ly, H.-V. T. Mai, and V.Q. Tran, Prediction of later-age concrete compressive strength using feedforward neural network. Advances in Materials Science and Engineering. 2020 (2020), pp. 1-8.

10.1155/2020/9682740
39

S.Y. Hiew, K.B. Teoh, S.N. Raman, D. Kong, and M. Hafezolghorani, Prediction of ultimate conditions and stress-strain behaviour of steel-confined ultra-high-performance concrete using sequential deep feed-forward neural network modelling strategy. Engineering Structures. 277 (2023), 115447.

10.1016/j.engstruct.2022.115447
40

J. Zhu and Y. Wang, Convolutional neural networks for predicting creep and shrinkage of concrete. Construction and Building Materials. 306 (2021), 124868.

10.1016/j.conbuildmat.2021.124868
41

S.H. Lee, H.Y. Kim, H.K. Shin, Y. Jang, and Y.H. Ahn, Introducing a model for evaluating concrete structure performance using deep convolutional neural network. International Journal of Sustainable Building Technology and Urban Development. 8(3) (2017), pp. 285-295.

10.12972/susb.20170027
42

I. Umeonyiagu and C. Nwobi-Okoye, Predicting flexural strength of concretes incorporating river gravel using multi-layer perceptron networks: A case study of eastern Nigeria. Nigerian Journal of Technology. 34(1) (2015), pp. 12-20.

10.4314/njt.v34i1.2
43

J. Abellán-García, Four-layer perceptron approach for strength prediction of UHPC. Construction and Building Materials. 256 (2020), 119465.

10.1016/j.conbuildmat.2020.119465
44

D. Ghunimat, A.E. Alzoubi, A. Alzboon, and S. Hanandeh, Prediction of concrete compressive strength with GGBFS and fly ash using multilayer perceptron algorithm, random forest regression and k-nearest neighbor regression. Asian Journal of Civil Engineering. 24(1) (2023), pp. 169-177.

10.1007/s42107-022-00495-z
45

M. Duan, Y. Qin, Y. Li, Y. Wei, K. Geng, H. Zhou, and R. Liu, Mechanical properties and multi-layer perceptron neural networks of polyacrylonitrile fiber reinforced concrete cured outdoors. Structures. 56 (2023), 104954.

10.1016/j.istruc.2023.104954
46

M. Sonebi, A. Cevik, S. Grünewald, and J. Walraven, Modelling the fresh properties of self-compacting concrete using support vector machine approach. Construction and Building Materials. 106 (2016), pp. 55-64.

10.1016/j.conbuildmat.2015.12.035
47

A.M. Abd and S.M. Abd, Modelling the strength of lightweight foamed concrete using support vector machine (SVM). Case Studies in Construction Materials. 6 (2017), pp. 8-15.

10.1016/j.cscm.2016.11.002
48

P. Saha, P. Debnath, and P. Thomas, Prediction of fresh and hardened properties of self-compacting concrete using support vector regression approach. Neural Computing and Applications. 32(12) (2020), pp. 7995-8010.

10.1007/s00521-019-04267-w
49

S. Jueyendah, M. Lezgy-Nazargah, H. Eskandari-Naddaf, and S. Emamian, Predicting the mechanical properties of cement mortar using the support vector machine approach. Construction and Building Materials. 291 (2021), 123396.

10.1016/j.conbuildmat.2021.123396
50

S. Koo, D. Shin, and C. Kim, Application of principal component analysis approach to predict shear strength of reinforced concrete beams with stirrups. Materials. 14(13) (2021), 3471.

10.3390/ma14133471
51

S. Tayfur, N. Alver, S. Abdi, S. Saatcı, and A. Ghiami, Characterization of concrete matrix/steel fiber de-bonding in an SFRC beam: Principal component analysis and k-mean algorithm for clustering AE data. Engineering Fracture Mechanics. 194 (2018), pp. 73-85.

10.1016/j.engfracmech.2018.03.007
52

M.M. Hameed, M.K. AlOmar, W.J. Baniya, and M.A. AlSaadi, Incorporation of artificial neural network with principal component analysis and cross-validation technique to predict high-performance concrete compressive strength. Asian Journal of Civil Engineering. 22 (2021), pp. 1019-1031.

10.1007/s42107-021-00362-3
53

A. Habib, U. Yildirim, and M. Habib, Applying Kernel principal component analysis for enhanced multivariable regression modeling of rubberized concrete properties. Arabian Journal for Science and Engineering. 48(4) (2023), pp. 5383-5396.

10.1007/s13369-022-07435-8
54

B.-T. Huang, Q.-H. Li, and S.-L. Xu, Fatigue deformation model of plain and fiber-reinforced concrete based on Weibull function. Journal of Structural Engineering. 145(1) (2019), 04018234.

10.1061/(ASCE)ST.1943-541X.0002237
55

A.A. Rahman, M.M. Mendez Larrain, and R.A. Tarefder, Development of a nonlinear rutting model for asphalt concrete based on Weibull parameters. International Journal of Pavement Engineering. 20(9) (2019), pp. 1055-1064.

10.1080/10298436.2017.1380807
56

L. Li, J. Guan, P. Yuan, Y. Yin, and Y. Li, A Weibull distribution-based method for the analysis of concrete fracture. Engineering Fracture Mechanics. 256 (2021), 107964.

10.1016/j.engfracmech.2021.107964
57

G. Qu, M. Zheng, X. Wang, R. Zhu, Y. Su, and G. Chang, A freeze-thaw damage evolution equation and a residual strength prediction model for porous concrete based on the Weibull distribution function. Journal of Materials in Civil Engineering. 35(5) (2023), 04023074.

10.1061/(ASCE)MT.1943-5533.0004745
58

P.G. Asteris and V.G. Mokos, Concrete compressive strength using artificial neural networks. Neural Computing and Applications. 32(15) (2020), pp. 11807-11826.

10.1007/s00521-019-04663-2
59

F. Khademi, S.M. Jamal, N. Deshpande, and S. Londhe, Predicting strength of recycled aggregate concrete using artificial neural network, adaptive neuro-fuzzy inference system and multiple linear regression. International Journal of Sustainable Built Environment. 5(2) (2016), pp. 355-369.

10.1016/j.ijsbe.2016.09.003
60

F. Altun, Ö. Kişi, and K. Aydin, Predicting the compressive strength of steel fiber added lightweight concrete using neural network. Computational Materials Science. 42(2) (2008), pp. 259-265.

10.1016/j.commatsci.2007.07.011
61

J. Reinosa, A. Loureiro, R. Gutierrez, and M. Lopez, Artificial neural network prediction of the initial stiffness of semi-rigid beam-to-column connections. in Structures. Elsevier, 56 (2023), 104904.

10.1016/j.istruc.2023.104904
62

H. Abbas, Y.A. Al-Salloum, H.M. Elsanadedy, and T.H. Almusallam, ANN models for prediction of residual strength of HSC after exposure to elevated temperature. Fire Safety Journal. 106 (2019), pp. 13-28.

10.1016/j.firesaf.2019.03.011
63

N. Suwal and S. Guner, Plastic hinge modeling of reinforced concrete Beam-Column joints using artificial neural networks. Engineering Structures. 298 (2024), 117012.

10.1016/j.engstruct.2023.117012
64

A.D. TQ, A.R. Masoodi, and A.H. Gandomi, Unveiling the potential of an evolutionary approach for accurate compressive strength prediction of engineered cementitious composites. Case Studies in Construction Materials. 19 (2023), e02172.

10.1016/j.cscm.2023.e02172
65

I. Ranjbar, V. Toufigh, and M. Boroushaki, A combination of deep learning and genetic algorithm for predicting the compressive strength of high‐performance concrete. Structural Concrete. 23(4) (2022), pp. 2405-2418.

10.1002/suco.202100199
66

G. Du, L. Bu, Q. Hou, J. Zhou, and B. Lu, Prediction of the compressive strength of high-performance self-compacting concrete by an ultrasonic-rebound method based on a GA-BP neural network. PLoS One. 16(5) (2021), e0250795.

10.1371/journal.pone.0250795
67

D. Tien Bui, M.A.M. Abdullahi, S. Ghareh, H. Moayedi, and H. Nguyen, Fine-tuning of neural computing using whale optimization algorithm for predicting compressive strength of concrete. Engineering with Computers. 37 (2021), pp. 701-712.

10.1007/s00366-019-00850-w
68

L. Sun, M. Koopialipoor, D. Jahed Armaghani, R. Tarinejad, and M. Tahir, Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples, Engineering with Computers. 37 (2021), pp. 1133-1145.

10.1007/s00366-019-00875-1
69

S.A. Moghaddas, M. Nekoei, E.M. Golafshani, A. Behnood, and M. Arashpour, Application of artificial bee colony programming techniques for predicting the compressive strength of recycled aggregate concrete. Applied Soft Computing. 130 (2022), 109641.

10.1016/j.asoc.2022.109641
70

H. Jahangir and D.R. Eidgahee, A new and robust hybrid artificial bee colony algorithm-ANN model for FRP-concrete bond strength evaluation. Composite Structures. 257 (2021), 113160.

10.1016/j.compstruct.2020.113160
71

M. Komasi and S.A. Hassanzadeh, Evaluation of compressive strength and rapid chloride permeability test of concretes containing metakaolin using Bayesian inference and GEP methods. Modares Civil Engineering journal. 21(1) (2021), pp. 203-217.

72

A.S. Hosseini, P. Hajikarimi, M. Gandomi, F.M. Nejad, and A.H. Gandomi, Genetic programming to formulate viscoelastic behavior of modified asphalt binder. Construction and Building Materials. 286 (2021), 122954.

10.1016/j.conbuildmat.2021.122954
73

H.A. Algaifi, A.S. Alqarni, R. Alyousef, S.A. Bakar, M.H.W. Ibrahim, S. Shahidan, M. Ibrahim, and B.A. Salami, Mathematical prediction of the compressive strength of bacterial concrete using gene expression programming. Ain Shams Engineering Journal. 12(4) (2021), pp. 3629-3639.

10.1016/j.asej.2021.04.008
74

H. Alabduljabbar, M. Khan, H.H. Awan, S.M. Eldin, R. Alyousef, and A.M. Mohamed, Predicting ultra-high-performance concrete compressive strength using gene expression programming method, Case Studies in Construction Materials. 18 (2023), e02074.

10.1016/j.cscm.2023.e02074
75

H.A. Shah, Q. Yuan, U. Akmal, S.A. Shah, A. Salmi, Y.A. Awad, L.A. Shah, Y. Iftikhar, M.H. Javed, and M.I. Khan, Application of machine learning techniques for predicting compressive, splitting tensile, and flexural strengths of concrete with metakaolin. Materials. 15(15), 5435.

10.3390/ma15155435
76

H. Sabetifar and M. Nematzadeh, An evolutionary approach for formulation of ultimate shear strength of steel fiber-reinforced concrete beams using gene expression programming. Structures. 34 (2021), pp. 4965-4976.

10.1016/j.istruc.2021.10.075
77

A.H. Gandomi, A.H. Alavi, T. Ting, and X.-S. Yang, Intelligent modeling and prediction of elastic modulus of concrete strength via gene expression programming, in Advances in Swarm Intelligence: 4th International Conference, ICSI 2013, Harbin, China, June 12-15, 2013, Proceedings, Part I 4, (2013). Springer, pp. 564-571.

10.1007/978-3-642-38703-6_66
78

A. Standard, C150: Standard Specification for Portland Cement, Annual Book of ASTM Standards, 2009.

79

M.S. Hameed, A. Sekar, and V. Saraswathy, Strength and permeability characteristics study of self-compacting concrete using crusher rock dust and marble sludge powder. Arabian Journal for Science and Engineering. 37 (2012), pp. 561-574.

10.1007/s13369-012-0201-x
80

I. Afshoon, M. Miri, and S.R. Mousavi, Comprehensive experimental and numerical modeling of strength parameters of eco-friendly steel fiber reinforced SCC containing coarse copper slag aggregates. Construction and Building Materials. 367 (2023), 130304.

10.1016/j.conbuildmat.2023.130304
81

British Standards Institution. Testing Concrete: Method for Determination of Compressive Strength Using Portions of Beams Broken in Flexure (equivalent Cube Method). British Standards Institution. 1983.

82

Z. Yuan, M.-Q. Niu, H. Ma, T. Gao, J. Zang, Y. Zhang, and L.-Q. Chen, Predicting mechanical behaviors of rubber materials with artificial neural networks. International Journal of Mechanical Sciences. 249 (2023), 108265.

10.1016/j.ijmecsci.2023.108265
83

Y.A. Al-Salloum, A.A. Shah, S.H. Alsayed, T.H. Almusallam, M. Al-Haddad, and H. Abbas, Prediction of compressive strength of concrete using neural networks, Computers and Concrete. An International Journal. 10(2) (2012), pp. 197-217.

10.12989/cac.2012.10.2.197
Information
  • Publisher :Sustainable Building Research Center (ERC) Innovative Durable Building and Infrastructure Research Center
  • Publisher(Ko) :건설구조물 내구성혁신 연구센터
  • Journal Title :International Journal of Sustainable Building Technology and Urban Development
  • Volume : 15
  • No :3
  • Pages :328-353
  • Received Date : 2024-04-16
  • Accepted Date : 2024-06-24
  • DOI :https://doi.org/10.22712/susb.20240024
Journal Informaiton International Journal of Sustainable Building Technology and Urban Development International Journal of Sustainable Building Technology and Urban Development
Online Submission
  • scopus
  • NRF
  • KOFST
  • KISTI Current Status
  • KISTI Cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
  • isc
Journal Informaiton Journal Informaiton - close