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Concrete slump prediction modeling with a fine-tuned convolutional neural network: hybridizing sea lion and dragonfly algorithms

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Abstract

High-strength concrete (HSC) is defined as concrete that meets a special combination of uniformity and performance requirements, which cannot be attained routinely via traditional constituents and normal mixing, placing, and curing procedures. It is a complex material since modeling its behavior is a difficult task. This paper intends to show the feasible applicability of optimized convolutional neural networks (CNN) for predicting the slump in HSC. The following are the parameters that given as the input for the prediction of slump: cement (kg/m3), slag (kg/m3), fly ash (kg/m3), water (kg/m3), super-plasticizer (kg/m3), coarse aggregate (kg/m3), and fine aggregate (kg/m3). In order to make the prediction more accurate, the design of CNN is assisted with optimization logic by making some fine-tuned filter size of the convolutional layer. For this optimization purpose, this work presents a new “hybrid” algorithm that incorporates the concept of sea lion optimization algorithm (SLnO) and dragonfly algorithm (DA) and is named as Levy updated-sea lion optimization algorithm (LU-SLnO). Finally, the performance of the proposed work is compared and proved over the state-of-the-art models with respect to error measure and convergence analysis.

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Abbreviations

ANN:

Artificial neural network

BPC:

Bentonite plastic concrete

M5Tree:

M5 model tree

UEO:

Used engine oil

GA:

Genetic algorithm

RMC:

Ready mix concrete

BPNN:

Backpropagation neural network

GEP:

Gene expression programming

HPC:

High-performance concrete

PCE:

Polycarboxylate ether

RMSE:

Root mean square error

SF:

Silica fume

AASC:

Alkali-activated slag concretes

HSC:

High-strength concrete

SCC:

Self-compacting concrete

ELM:

Extreme learning machines

MARS:

Multivariate adaptive regression splines

MAPE:

Mean absolute percentage error

MAD:

Mean absolute deviation

References

  • Abdalla LB, Ghafor K, Mohammed A (2019) Testing and modeling the young age compressive strength for high workability concrete modified with PCE polymers. Results in Materials:1

  • Agrawal V, Sharma A (2010) Prediction of slump in concrete using artificial neural networks. International Journal of Civil and Environmental Engineering 4(9)

  • Agudelo, Isabel (2009) Supply chain management in the cement industry. PhD diss, Massachusetts Institute of Technology

    Google Scholar 

  • Amlashi AT, Abdollahi SM, Goodarzi S, Ghanizadeh AR (2019) Soft computing based formulations for slump, compressive strength, and elastic modulus of bentonite plastic concrete. J Clean Prod 230:1197–1216

    Article  Google Scholar 

  • Bayar G, Bilir T (2019) A novel study for the estimation of crack propagation in concrete using machine learning algorithms. Constr Build Mater 215:670–685

    Article  Google Scholar 

  • Beno MM, Valarmathi IR, Swamy SM, Rajakumar BR (2014) Threshold prediction for segmenting tumour from brain MRI scans. Int J Imaging Syst Technol 24(2):129–137

    Article  Google Scholar 

  • Bondar D, Ma Q, Soutsos M, Basheer M, Nanukuttan S (2018) Alkali activated slag concretes designed for a desired slump, strength and chloride diffusivity. Constr Build Mater 190:191–199

    Article  CAS  Google Scholar 

  • Boothalingam R (2018) Optimization using lion algorithm: a biological inspiration from lion’s social behavior. Evol Intel 11(1–2):31–52

    Article  Google Scholar 

  • Brammya, Deepa TA (2019) Job scheduling in cloud environment using lion algorithm. Journal of Networking and Communication Systems 2(1):1–14

    Google Scholar 

  • Chandwani V, Agrawal V, Nagar R (2015) Modeling slump of ready mix concrete using genetic algorithms assisted training of artificial neural networks. Expert Syst Appl 42(21):885–893

    Article  Google Scholar 

  • Chen L, Kou C-H, Ma S-W (2014) Prediction of slump flow of high-performance concrete via parallel hyper-cubic gene-expression programming. Eng Appl Artif Intell 34:66–74

    Article  Google Scholar 

  • Domone P (1998) The slump flow test for high-workability concrete. Cem Concr Res 28(2):177–182

    Article  CAS  Google Scholar 

  • Fang G, Ho WK, Tu W, Zhang M (2018) Workability and mechanical properties of alkali-activated fly ash-slag concrete cured at ambient temperature. Constr Build Mater 172:476–487

    Article  CAS  Google Scholar 

  • Feng D-C, Liu Z-T, Wang X-D, Chen Y, Jiang Z-M (2020) Machine learning-based compressive strength prediction for concrete: an adaptive boosting approach. Construction and Building Materials:230

  • Honglei C, Zuquan J, Tiejun Z, Benzhen W, Jian L (2020) Capillary suction induced water absorption and chloride transport in non-saturated concrete: the influence of humidity, mineral admixtures and sulfate ions. Construction and Building Materials:236

  • Jadhav AN, Gomathi N (2019) DIGWO: hybridization of dragonfly algorithm with improved grey wolf optimization algorithm for data clustering. Multimedia Research 2(3):1–11

    Google Scholar 

  • Jafari M, Chaleshtari MHB (2017) Using dragonfly algorithm for optimization of orthotropic infinite plates with a quasi-triangular cut-out. European Journal of Mechanics A/Solids 66:1–14

    Article  Google Scholar 

  • Kaufmann J (2020) Evaluation of the combination of desert sand and calcium sulfoaluminate cement for the production of concrete. Constr Build Mater 243:118281

    Article  CAS  Google Scholar 

  • Li C, Miao L, You Q, Hu S, Fang H (2018) Effects of viscosity modifying admixture (VMA) on workability and compressive strength of structural EPS concrete. Constr Build Mater 175:342–350

    Article  CAS  Google Scholar 

  • Lu C, Yang H, Mei G (2015) Relationship between slump flow and rheological properties of self compacting concrete with silica fume and its permeability. Constr Build Mater 75:157–162

    Article  Google Scholar 

  • Ma K, Feng J, Long G, Xie Y, Chen X (2017) Improved mix design method of self-compacting concrete based on coarse aggregate average diameter and slump flow. Constr Build Mater 143:566–573

    Article  CAS  Google Scholar 

  • Masadeh R, Mahafzah B, Sharieh A (2019) Sea lion optimization algorithm. International Journal of Advanced Computer Science and Applications 10:388–395

    Article  Google Scholar 

  • Meng W, Kumar A, Khayat KH (2019) Effect of silica fume and slump-retaining polycarboxylate-based dispersant on the development of properties of Portland cement paste. Cem Concr Compos 99:181–190

    Article  CAS  Google Scholar 

  • Nair AT, Muthuvel K (2019) Diabetic retinopathy recognition using enhanced crow search with levy flight algorithm. Multimedia Research 2(4):43–52

    Google Scholar 

  • Nguyen NT, Yu Y, Li J, Gowripalan N, Sirivivatnanon V (2019) Elastic modulus of ASR-affected concrete: an evaluation using artificial neural network. Comput Concr 24(6):541–553

    Google Scholar 

  • Nguyen TN, Yu Y, Li J, Sirivivatnanon V (2020) An optimised support vector machine model for elastic modulus prediction of concrete subject to alkali silica reaction. In: ACMSM25. Springer, Singapore, pp 899–909

    Chapter  Google Scholar 

  • Nilsen V, Pham LT, Hibbard M, Klager A, Morgan D (2019) Prediction of concrete coefficient of thermal expansion and other properties using machine learning. Constr Build Mater 220:587–595

    Article  Google Scholar 

  • O’Shea K, Nash R (2015) An introduction to convolutional neural networks. ArXive-prints

  • Prasad M, Huang C-J, Song X-B, Chen S-J, Qian C-H (2020) Concrete behavior in steel-concrete-steel panels subjected to biaxial tension compression. Journal of Constructional Steel Research:167

  • Rousseau MAD, Laftchiev E, Kasprzyk JR, Rajagopalan B, Srubar WV (2019) A comparison of machine learning methods for predicting the compressive strength of field-placed concrete. Construction and Building Materials:228

  • Shafiq N, Choo CS, Isa MH (2018) Effects of used engine oil on slump, compressive strength and oxygen permeability of normal and blended cement concrete. Constr Build Mater 187:178–184

    Article  CAS  Google Scholar 

  • Sokhansefat G, Ley MT, Cook MD, Alturki R, Moradian M (2019) Investigation of concrete workability through characterization of aggregate gradation in hardened concrete using X-ray computed tomography. Cem Concr Compos 98:150–161

    Article  CAS  Google Scholar 

  • Taghipour, Atour, Frayret J-M (2013) Coordination of operations planning in supply chains: a review. International Journal of Business Performance and Supply Chain Modelling 5(3):272–307

    Article  Google Scholar 

  • Tay YWD, Qian Y, Tan MJ (2019) Printability region for 3D concrete printing using slump and slump flow test. Engineering, Composites Part B, p 174

    Google Scholar 

  • Vieira LBP, Figueiredo AD (2020) Implementation of the use of hydration stabilizer admixtures at a ready-mix concrete plant. Case Studies in Construction Materials 12

  • Vosooghidizaji M, Taghipour A, Canel-Depitre B (2020) Supply chain coordination under information asymmetry: a review. Int J Prod Res 58(6):1805–1834

    Article  Google Scholar 

  • Yeh I (2008) Modeling slump of concrete with fly ash and superplasticizer. Comput Concr 5(6):559–572

    Article  Google Scholar 

  • Yeh, I-Cheng (2006) Exploring concrete slump model using artificial neural networks. J Comput Civ Eng 20(3):217–221

    Article  Google Scholar 

  • Yeh, I-Cheng (2007) Modeling slump flow of concrete using second-order regressions and artificial neural networks. Cem Concr Compos 29(6):474–480

    Article  CAS  Google Scholar 

  • Yeh, I-Cheng (2008) Prediction of workability of concrete using design of experiments for mixtures. Comput Concr 5(1):1–20

    Article  Google Scholar 

  • Yeh, I-Cheng (2009) Simulation of concrete slump using neural networks. Constr Mater 162(1):11–18

    Article  Google Scholar 

  • Yu Y, Li W, Li J, Nguyen TN (2018) A novel optimised self-learning method for compressive strength prediction of high performance concrete. Constr Build Mater 184:229–247

    Article  Google Scholar 

  • Yu Y, Zhang C, Gu X, Cui Y (2019) Expansion prediction of alkali aggregate reactivity-affected concrete structures using a hybrid soft computing method. Neural Comput & Applic 31(12):8641–8660

    Article  Google Scholar 

  • Yuan T-F, Lee J-Y, Yoon Y-S (2020) Enhancing the tensile capacity of no-slump high-strength high-ductility concrete. Cem Concr Compos 106:103458

    Article  CAS  Google Scholar 

  • Zheng L, Cheng H, Huo L, Song G (2019) Monitor concrete moisture level using percussion and machine learning. Constr Build Mater 229:117077

    Article  Google Scholar 

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Availability of data and materials

The dataset used for this paper is downloaded from the link: https://archive.ics.uci.edu/ml/datasets/Concrete+Slump+Test.

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  1. Department of Civil Engineering, Bennett University, Greater Noida, Uttar Pradesh, 201310, India

    Kumar Shaswat

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  1. Kumar Shaswat
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Kumar Shaswat contributed to the design and implementation of the research, to the analysis of the results, and to the writing of the manuscript.

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Correspondence to Kumar Shaswat.

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Shaswat, K. Concrete slump prediction modeling with a fine-tuned convolutional neural network: hybridizing sea lion and dragonfly algorithms. Environ Sci Pollut Res 29, 43758–43769 (2022). https://doi.org/10.1007/s11356-020-12244-3

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