Skip to main content
SpringerLink
Log in

Extreme Learning Machine for Estimation of the Engineering Properties of Self-Compacting Mortar with High-Volume Mineral Admixtures

  • Research Paper
  • Published:
Iranian Journal of Science and Technology, Transactions of Civil Engineering Aims and scope Submit manuscript

Abstract

The utilization of supplementary cementitious materials obtained from industrial by-products or wastes is one of the most effective ways to minimize the costs as well as environmental impact associated with cement production. This work investigated the effects of the replacement of Portland cement (PC) with (25, 30, 35 and 40%) fly ash (FA) and (5, 10, 15, and 20%) silica fume (SF) by weight as binary and ternary blends on the compressive strength (fc) and flexural strength (fft) of self-compacting mortars (SCMs) at 28 and 91 curing days. Extreme learning machine (ELM), support vector regression (SVR), artificial neural network (ANN), and decision tree (DT) models were devised to predict these strengths of SCMs containing high-volume mineral admixture (HVMA). The selected input variables were the number of curing days, water-cementitious material (W/CM), PC, FA, SF, and sand contents, while the fc and fft were the output variables. ANOVA results show that the curing time was the most effective parameter for determining both strengths. The results also indicated that ELM achieved superior performance for the prediction of fc and fft of SCMs with HVMA compared to SVR, ANN, and DT due to having the highest coefficient of determination values of 0.9802 for both strengths.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Data availability

All data and materials support the published claims and comply with field standards.

Code availability

Software application or custom code support the published claims and comply with field standards.

References

  • Ahmad A, Farooq F, Niewiadomski P, Ostrowski K, Akbar A, Aslam F, Alyousef R (2021a) Prediction of compressive strength of fly ash based concrete using individual and ensemble algorithm. Materials 14(4):1–21

    Article  Google Scholar 

  • Ahmad A, Farooq F, Ostrowski KA, Śliwa-Wieczorek K, Czarnecki S (2021b) Application of novel machine learning techniques for predicting the surface chloride concentration in concrete containing waste material. Materials 14(9):2297

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  • Ahmad A, Ahmad W, Aslam F, Joyklad P (2022) Compressive strength prediction of fly ash-based geopolymer concrete via advanced machine learning techniques. Case Stud Constr Mater 16:e00840

    Google Scholar 

  • Alçin ÖF, Şengür A, Ince MC (2015) Forward-backward pursuit based sparse extreme learning machine. J Fac Eng Arch Gazi Univ 30(1):111–117

    Google Scholar 

  • Alcin OF, Sengur A, Ghofrani S, Ince MC (2014) GA-SELM: greedy algorithms for sparse extreme learning machine. Meas J Int Meas Confed 55:126–132

    Article  Google Scholar 

  • Al-Shamiri AK, Kim JH, Yuan TF, Yoon YS (2019) Modeling the compressive strength of high-strength concrete: an extreme learning approach. Constr Build Mater 208:204–219

    Article  Google Scholar 

  • Anjos MAS, Camões A, Campos P, Azeredo GA, Ferreira RLS (2020) Effect of high volume fly ash and metakaolin with and without hydrated lime on the properties of self-compacting concrete. J Build Eng 27:100985

    Article  Google Scholar 

  • Aprianti E, Shafigh P, Zawawi R, Abu Hassan ZF (2016) Introducing an effective curing method for mortar containing high volume cementitious materials. Constr Build Mater 107:365–377

    Article  CAS  Google Scholar 

  • Asteris PG, Mokos VG (2020) Concrete compressive strength using artificial neural networks. Neural Comput Appl 32(15):11807–11826

    Article  Google Scholar 

  • ASTM C39/C39M-20 (2020) Standard test method for compressive strength of cylindrical concrete specimens

  • ASTM-C09 (2000) Standard test method for flexural strength of concrete (Using simple beam with center-point loading)

  • Atis CD, Tanyildizi H, Karahan O (2009) Statistical analysis for strength properties of polypropylene-fibre- reinforced fly ash concrete. Mag Concr Res 61(7):557–566

    Article  Google Scholar 

  • Azimi-Pour M, Eskandari-Naddaf H, Pakzad A (2020) Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete. Constr Build Mater 230:117021

    Article  Google Scholar 

  • Bagheri A, Zanganeh H, Alizadeh H, Shakerinia M, Marian MAS (2013) Comparing the performance of fine fly ash and silica fume in enhancing the properties of concretes containing fly ash. Constr Build Mater 47:1402–1408

    Article  Google Scholar 

  • Barcelo L, Kline J, Walenta G, Gartner E (2014) Cement and carbon emissions. Mater Struct Mater Constr 47(6):1055–1065

    Article  CAS  Google Scholar 

  • Ben Chaabene W, Flah M, Nehdi ML (2020) Machine learning prediction of mechanical properties of concrete: critical review. Constr Build Mater 260:119889

    Article  Google Scholar 

  • Benhelal E, Zahedi G, Shamsaei E, Bahadori A (2013) Global strategies and potentials to curb CO2 emissions in cement industry. J Clean Prod 51:142–161

    Article  Google Scholar 

  • Benli A, Turk K, Kina C (2018) Influence of silica fume and class F fly ash on mechanical and rheological properties and freeze-thaw durability of self-compacting mortars. J Cold Reg Eng 32(3):04018009

    Article  Google Scholar 

  • Bin HG (2003) Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Trans Neural Netw 14(2):274–281

    Article  Google Scholar 

  • Bin HG, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501

    Google Scholar 

  • Chajec A (2021) Granite powder vs. fly ash for the sustainable production of air-cured cementitious mortars. Materials 14(5):1–26

    Article  Google Scholar 

  • Chatterjee A, Das D (2013) Assessing flow response of self-compacting mortar by Taguchi method and ANOVA interaction. Mater Res 16(5):1084–1091

    Article  CAS  Google Scholar 

  • Cherkassky V, Mulier F (2006) Learning from data: concepts, theory, and methods, 2nd edn. Wiley, New Jersey

    Google Scholar 

  • Cotsovos DM, Pavlović MN (2008) Numerical investigation of concrete subjected to compressive impact loading. Part 2: parametric investigation of factors affecting behaviour at high loading rates. Comput Struct 86(1–2):164–180

    Article  Google Scholar 

  • Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods

  • Czarnecki S, Shariq M, Nikoo M, Sadowski Ł (2021) An intelligent model for the prediction of the compressive strength of cementitious composites with ground granulated blast furnace slag based on ultrasonic pulse velocity measurements. Meas J Int Meas Confed 172:108951

    Article  Google Scholar 

  • Das D, Chatterjee A (2014) Taguchi and ANOVA approach for optimization of flow characteristics of self-compacting concrete. Emerg Mater Res 3(1):37–45

    CAS  Google Scholar 

  • Diniz HAA, dos Anjos MAS, Rocha AKA, Ferreira RLS (2022) Effects of the use of agricultural ashes, metakaolin and hydrated-lime on the behavior of self-compacting concretes. Constr Build Mater 319:126087

    Article  CAS  Google Scholar 

  • Dou J, Yunus AP, Tien Bui D, Merghadi A, Sahana M, Zhu Z, Chen CW, Khosravi K, Yang Y, Pham BT (2019) Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. Sci Total Environ 662:332–346

    Article  CAS  PubMed  ADS  Google Scholar 

  • EFNARC (2002) Specification and guidelines for self-compacting concrete. Report from EFNARC

  • Elkiran G, Nourani V, Abba SI (2019) Multi-step ahead modelling of river water quality parameters using ensemble artificial intelligence-based approach. J Hydrol 577:123962

    Article  CAS  Google Scholar 

  • Falmata AM, Sulaiman A, Mohamed RN, Shettima AU (2020) Mechanical properties of self-compacting high-performance concrete with fly ash and silica fume. SN Appl Sci 2(33):1–11

    Google Scholar 

  • Farooq F, Akbar A, Khushnood RA, Muhammad WLB, Rehman SKU, Javed MF (2020) Experimental investigation of hybrid carbon nanotubes and graphite nanoplatelets on rheology, shrinkage, mechanical, and microstructure of SCCM. Materials 13(1):230

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  • Farooq F, Ahmed W, Akbar A, Aslam F, Alyousef R (2021) Predictive modeling for sustainable high-performance concrete from industrial wastes: a comparison and optimization of models using ensemble learners. J Clean Prod 292:126032

    Article  Google Scholar 

  • Feng DC, Liu ZT, Wang XD, Chen Y, Chang JQ, Wei DF, Jiang ZM (2020) Machine learning-based compressive strength prediction for concrete: an adaptive boosting approach. Constr Build Mater 230:117000

    Article  Google Scholar 

  • Gao W, Karbasi M, Derakhsh AM, Jalili A (2019) Development of a novel soft-computing framework for the simulation aims: a case study. Eng Comput 35(1):315–322

    Article  Google Scholar 

  • Golafshani EM, Behnood A, Arashpour M (2020) Predicting the compressive strength of normal and high-performance concretes using ANN and ANFIS hybridized with grey wolf optimizer. Constr Build Mater 232:117266

    Article  Google Scholar 

  • Güçlüer K, Özbeyaz A, Göymen S, Günaydın O (2021) A comparative investigation using machine learning methods for concrete compressive strength estimation. Mater Today Commun 27:102278

    Article  Google Scholar 

  • Güneyisi E, Gesoğlu M (2008) Properties of self-compacting mortars with binary and ternary cementitious blends of fly ash and metakaolin. Mater Struct Mater Constr 41(9):1519–1531

    Article  Google Scholar 

  • Güneyisi E, Gesoglu M, Al-Goody A, Ipek S (2015) Fresh and rheological behavior of nano-silica and fly ash blended self-compacting concrete. Constr Build Mater 95:29–44

    Article  Google Scholar 

  • Gupta T, Patel KA, Siddique S, Sharma RK, Chaudhary S (2019) Prediction of mechanical properties of rubberised concrete exposed to elevated temperature using ANN. Meas J Int Meas Confed 147:106870

    Article  Google Scholar 

  • Gupta N, Siddique R (2020) Durability characteristics of self-compacting concrete made with copper slag. Constr Build Mater 247:118580

    Article  CAS  Google Scholar 

  • Jahangir H, Rezazadeh Eidgahee D (2021) A new and robust hybrid artificial bee colony algorithm – ANN model for FRP-concrete bond strength evaluation. Compos Struct 257:113160

    Article  Google Scholar 

  • Jalal M, Pouladkhan A, Harandi OF, Jafari D (2015) Comparative study on effects of Class F fly ash, nano silica and silica fume on properties of high performance self compacting concrete. Constr Build Mater 94:90–104

    Article  Google Scholar 

  • Jueyendah S, Lezgy-Nazargah M, Eskandari-Naddaf H, Emamian SA (2021) Predicting the mechanical properties of cement mortar using the support vector machine approach. Constr Build Mater 291:123396

    Article  CAS  Google Scholar 

  • Kajaste R, Hurme M (2016) Cement industry greenhouse gas emissions—management options and abatement cost. J Clean Prod 112:4041–4052

    Article  CAS  Google Scholar 

  • Kandiri A, Sartipi F, Kioumarsi M (2021) Predicting compressive strength of concrete containing recycled aggregate using modified ann with different optimization algorithms. Appl Sci (switzerland) 11(2):1–19

    Google Scholar 

  • Karahan O, Tanyildizi H, Atis CD (2008) An artificial neural network approach for prediction of long-term strength properties of steel fiber reinforced concrete containing fly ash. J Zhejiang Univ Sci A 9(11):1514–1523

    Article  Google Scholar 

  • Karbassi A, Mohebi B, Rezaee S, Lestuzzi P (2014) Damage prediction for regular reinforced concrete buildings using the decision tree algorithm. Comput Struct 130:46–56

    Article  Google Scholar 

  • Karimipour A, Bagherzadeh SA, Taghipour A, Abdollahi A, Safaei MR (2019) A novel nonlinear regression model of SVR as a substitute for ANN to predict conductivity of MWCNT-CuO/water hybrid nanofluid based on empirical data. Phys A 521:89–97

    Article  CAS  Google Scholar 

  • Kina C, Turk K, Atalay E, Donmez I, Tanyildizi H (2021) Comparison of extreme learning machine and deep learning model in the estimation of the fresh properties of hybrid fiber-reinforced SCC. Neural Comput Appl 33:11641–11659

    Article  Google Scholar 

  • Kina C, Turk K, Tanyildizi H (2022a) Estimation of strengths of hybrid FR-SCC by using deep-learning and support vector regression models. Struct Concr 23:3313–3330

    Article  Google Scholar 

  • Kina C, Turk K, Tanyildizi H (2022b) Deep learning and machine learning-based prediction of capillary water absorption of hybrid fiber reinforced self-compacting concrete. Struct Concr 23:3331–3358

    Article  Google Scholar 

  • Kişi Ö, Uncuoǧlu E (2005) Comparison of three back-propagation training algorithms for two case studies. Indian J Eng Mater Sci 12(5):434–442

    Google Scholar 

  • Lan Y, Soh YC, Bin HG (2010) Constructive hidden nodes selection of extreme learning machine for regression. Neurocomputing 73(16–18):3191–3199

    Article  Google Scholar 

  • Larissa LC, Marcos MA, Maria MV, de Souza SLN, de Farias EC (2020) Effect of high temperatures on self-compacting concrete with high levels of sugarcane bagasse ash and metakaolin. Constr Build Mater 248:118715

    Article  Google Scholar 

  • Latif SD (2021) Developing a boosted decision tree regression prediction model as a sustainable tool for compressive strength of environmentally friendly concrete. Environ Sci Pollut Res 28(46):65935–65944

    Article  CAS  Google Scholar 

  • Li X, Ma X, Zhang S, Zheng E (2013) Mechanical properties and microstructure of class C fly ash-based geopolymer paste and mortar. Materials 6(4):1485–1495

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  • Lv Y, Liu J, Yang T, Zeng D (2013) A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler. Energy 55:319–329

    Article  CAS  Google Scholar 

  • Ma H, Liu J, Zhang J, Huang J (2021) Estimating the compressive strength of cement-based materials with mining waste using support vector machine, decision tree, and random forest models. Adv Civ Eng 2021:1–10

    CAS  Google Scholar 

  • Madandoust R, Mousavi SY (2012) Fresh and hardened properties of self-compacting concrete containing metakaolin. Constr Build Mater 35:752–760

    Article  Google Scholar 

  • Meesaraganda LVP, Saha P, Tarafder N (2019) Artificial neural network for strength prediction of fibers’ self-compacting concrete. Adv Intell Syst Comput 816:15–24

    Article  Google Scholar 

  • Megat Johari MA, Brooks JJ, Kabir S, Rivard P (2011) Influence of supplementary cementitious materials on engineering properties of high strength concrete. Constr Build Mater 25(5):2639–2648

    Article  Google Scholar 

  • Mohammed MK, Al-Hadithi AI, Mohammed MH (2019) Production and optimization of eco-efficient self compacting concrete SCC with limestone and PET. Constr Build Mater 197:734–746

    Article  CAS  Google Scholar 

  • Moradi MJ, Khaleghi M, Salimi J, Farhangi V, Ramezanianpour AM (2021) Predicting the compressive strength of concrete containing metakaolin with different properties using ANN. Meas J Int Meas Confed 183:109790

    Article  Google Scholar 

  • Naderpour H, Rafiean AH, Fakharian P (2018) Compressive strength prediction of environmentally friendly concrete using artificial neural networks. J Build Eng 16:213–219

    Article  Google Scholar 

  • Ozbay E, Oztas A, Baykasoglu A, Ozbebek H (2009) Investigating mix proportions of high strength self compacting concrete by using Taguchi method. Constr Build Mater 23(2):694–702

    Article  Google Scholar 

  • Özcan F, Atiş CD, Karahan O, Uncuoǧlu E, Tanyildizi H (2009) Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete. Adv Eng Softw 40(9):856–863

    Article  Google Scholar 

  • Peng Y, Unluer C (2022) Analyzing the mechanical performance of fly ash-based geopolymer concrete with different machine learning techniques. Constr Build Mater 316:125785

    Article  CAS  Google Scholar 

  • Raharjo D, Subakti A (2013) Mixed concrete optimization using fly ash, silica fume and iron slag on the SCC’s compressive strength. Procedia Eng 54:827–839

    Article  CAS  Google Scholar 

  • Saha P, Debnath P, Thomas P (2020) Prediction of fresh and hardened properties of self-compacting concrete using support vector regression approach. Neural Comput Appl 32(12):7995–8010

    Article  Google Scholar 

  • Salami BA, Rahman SM, Oyehan TA, Maslehuddin M, Al Dulaijan SU (2020) Ensemble machine learning model for corrosion initiation time estimation of embedded steel reinforced self-compacting concrete. Meas J Int Meas Confed 165:108141

    Article  Google Scholar 

  • Saridemir M (2009) Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial neural networks. Adv Eng Softw 40(5):350–355

    Article  Google Scholar 

  • Shahin MA, Jaksa MB, Maier HR (2009) Recent advances and future challenges for artificial neural systems in geotechnical engineering applications. Adv Artif Neural Syst 2009:1–9

    Article  Google Scholar 

  • Siddique R (2011) Properties of self-compacting concrete containing class F fly ash. Mater Des 32(3):1501–1507

    Article  CAS  Google Scholar 

  • Singh R, Goel S (2020) Experimental investigation on mechanical properties of binary and ternary blended pervious concrete. Front Struct Civ Eng 14(1):229–240

    Article  Google Scholar 

  • Su M, Zhong Q, Peng H, Li S (2021) Selected machine learning approaches for predicting the interfacial bond strength between FRPs and concrete. Constr Build Mater 270:121456

    Article  CAS  Google Scholar 

  • Sultana N, Zakir Hossain SM, Alam MS, Islam MS, Al AMA (2020) Soft computing approaches for comparative prediction of the mechanical properties of jute fiber reinforced concrete. Adv Eng Softw 149:102887

    Article  Google Scholar 

  • Tabatabaeian M, Khaloo A, Joshaghani A, Hajibandeh E (2017) Experimental investigation on effects of hybrid fibers on rheological, mechanical, and durability properties of high-strength SCC. Constr Build Mater 147:497–509

    Article  CAS  Google Scholar 

  • Taheri Amiri MJ, Ashrafian A, Haghighi FR, Javaheri Barforooshi M (2019) Prediction of the compressive strength of self-compacting concrete containing rice husk ash using data driven models. Modares Civ Eng J 19(1):196–206

    Google Scholar 

  • Tanyildizi H (2021) Predicting the geopolymerization process of fly ash-based geopolymer using deep long short-term memory and machine learning. Cement Concr Compos 123:104177

    Article  CAS  Google Scholar 

  • Tanyildizi H, Şahin M (2017) Taguchi optimization approach for the polypropylene fiber reinforced concrete strengthening with polymer after high temperature. Struct Multidiscip Optim 55(2):529–534

    Article  Google Scholar 

  • Tanyildizi H, Şengür A, Akbulut Y, Şahin M (2020) Deep learning model for estimating the mechanical properties of concrete containing silica fume exposed to high temperatures. Front Struct Civ Eng 14:1316–1330

    Article  Google Scholar 

  • Tanyildizi H, Marani A, Türk K, Nehdi ML (2022) Hybrid deep learning model for concrete incorporating microencapsulated phase change materials. Constr Build Mater 319:126146

    Article  CAS  Google Scholar 

  • Tanyildizi H, Coşkun A, Somunkiran I (2008) An experimental investigation of bond and compressive strength of concrete with mineral admixtures at high temperatures. Arab J Sci Eng 33(2B):443–449

    CAS  Google Scholar 

  • Tanyildizi H (2018) Long-term performance of the healed mortar with polymer containing phosphazene after exposed to sulfate attack. Constr Build Mater 167:473–481

    Article  CAS  Google Scholar 

  • Tanyildizi H (2018) Prediction of the strength properties of carbon fiber-reinforced lightweight concrete exposed to the high temperature using artificial neural network and support vector machine. Adv Civ Eng 2018:1–10

    Article  Google Scholar 

  • Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106(D7):7183–7192

    Article  ADS  Google Scholar 

  • Tikalsky PJ, Carrasquillo RL (1992) Influence of fly ash on the sulfate resistance of concrete. ACI Mater J 89(1):69–75

    CAS  Google Scholar 

  • Turk K (2012) Viscosity and hardened properties of self-compacting mortars with binary and ternary cementitious blends of fly ash and silica fume. Constr Build Mater 37:326–334

    Article  Google Scholar 

  • Turk K, Kina C (2018) Freeze-thaw resistance and sorptivity of self-compacting mortar with ternary blends. Comput Concr 21(2):149–156

    Google Scholar 

  • Turk K, Karatas M, Gonen T (2013) Effect of Fly Ash and Silica Fume on compressive strength, sorptivity and carbonation of SCC. KSCE J Civ Eng 17(1):202–209

    Article  Google Scholar 

  • Turk K, Kina C, Bagdiken M (2017) Use of binary and ternary cementitious blends of F-Class fly-ash and limestone powder to mitigate alkali-silica reaction risk. Constr Build Mater 151:422–427

    Article  CAS  Google Scholar 

  • Turk K, Kina C, Oztekin E (2020) Effect of macro and micro fiber volume on the flexural performance of hybrid fiber reinforced SCC. Adv Concr Constr 10(3):257–269

    Google Scholar 

  • Turk K, Bassurucu M, Bitkin RE (2021) Workability, strength and flexural toughness properties of hybrid steel fiber reinforced SCC with high-volume fiber. Constr Build Mater 266:120944

    Article  Google Scholar 

  • Turk K, Oztekin E, Kina C (2022) Self-compacting concrete with blended short and long fibres: experimental investigation on the role of fibre blend proportion. Eur J Environ Civ Eng 26(3):905–918

    Article  Google Scholar 

  • Türk K, Kına C (2017) Çimento esasli kompozitlerde karma lif kullanimı. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 23(6):671–678

    Google Scholar 

  • Uysal M, Tanyildizi H (2012) Estimation of compressive strength of self compacting concrete containing polypropylene fiber and mineral additives exposed to high temperature using artificial neural network. Constr Build Mater 27(1):404–414

    Article  Google Scholar 

  • Uysal M, Yilmaz K, Ipek M (2012) The effect of mineral admixtures on mechanical properties, chloride ion permeability and impermeability of self-compacting concrete. Constr Build Mater 27(1):263–270

    Article  Google Scholar 

  • Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10(5):988–999

    Article  CAS  PubMed  Google Scholar 

  • Wongkeo W, Thongsanitgarn P, Ngamjarurojana A, Chaipanich A (2014) Compressive strength and chloride resistance of self-compacting concrete containing high level fly ash and silica fume. Mater Des 64:261–269

    Article  CAS  Google Scholar 

  • Wu W, Wang R, Zhu C, Meng Q (2018) The effect of fly ash and silica fume on mechanical properties and durability of coral aggregate concrete. Constr Build Mater 185:69–78

    Article  CAS  Google Scholar 

  • Xiao H, Duan Z, Zhou Y, Zhang N, Shan Y, Lin X, Liu G (2019) CO2 emission patterns in shrinking and growing cities: a case study of Northeast China and the Yangtze River Delta. Appl Energy 251:113384

    Article  Google Scholar 

  • Yaseen ZM, Deo RC, Hilal A, Abd AM, Bueno LC, Salcedo-Sanz S, Nehdi ML (2018) Predicting compressive strength of lightweight foamed concrete using extreme learning machine model. Adv Eng Softw 115:112–125

    Article  Google Scholar 

  • Yuvaraj P, Ramachandra Murthy A, Iyer NR, Sekar SK, Samui P (2013) Support vector regression based models to predict fracture characteristics of high strength and ultra high strength concrete beams. Eng Fract Mech 98(1):29–43

    Article  Google Scholar 

  • Zhang D, Tsai JJP (2003) Machine learning and software engineering. Softw Qual J 11(2):87–119

    Article  Google Scholar 

  • Zhou T, Wang F, Yang Z (2017) Comparative analysis of ANN and SVM models combined with wavelet preprocess for groundwater depth prediction. Water (switzerland) 9(10):781

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Engineering Faculty, Inonu University, Malatya, Türkiye

    Kazim Turk

  2. Department of Civil Engineering, Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal University, Malatya, Türkiye

    Ceren Kina

  3. Department of Civil Engineering, Technology Faculty, Firat University, Elazig, Türkiye

    Harun Tanyildizi

Authors
  1. Kazim Turk
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ceren Kina
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Harun Tanyildizi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by KT, CK and HT. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ceren Kina.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Turk, K., Kina, C. & Tanyildizi, H. Extreme Learning Machine for Estimation of the Engineering Properties of Self-Compacting Mortar with High-Volume Mineral Admixtures. Iran J Sci Technol Trans Civ Eng 48, 41–60 (2024). https://doi.org/10.1007/s40996-023-01153-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40996-023-01153-3

Keywords

Search

Navigation