Abstract
In this article, the optimization of the specific urease activity (SUA) and the calcium carbonate (CaCO3) using microbially induced calcite precipitation (MICP) was compared to optimization using three algorithms based on machine learning: random forest regressor, artificial neural networks (ANNs), and multivariate linear regression. This study applied the techniques in two existing response surface method (RSM) experiments involving MICP technique. Random forest-based models and artificial neural network-based models were submitted through the optimization of hyperparameters via cross-validation technique and grid search, to select the best-optimized model. For this study, the random forest-based algorithm is aimed at having the best performance of 0.9381 and 0.9463 in comparison to the original r2 of 0.9021 and 0.8530, respectively. This study is aimed at exploring the capability of using machine learning-based models in small datasets for the purpose of optimization of experimental variables in MICP technique and the meaningfulness of the models by their specificities in the small experimental datasets applied to experimental designs. This study is aimed at exploring the capability of using machine learning-based models in small datasets for experimental variable optimization in MICP technique. The use of these techniques can create prerogatives to scale and mitigate costs in future experiments associated to the field.
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Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Also, the Python script is also available in the following Github repository: https://github.com/vlpacheco.
References
Abadi M, Barham P, Chen J et al (2016) Tensorflow: a system for large-scale machine learning. In: 12th symposium on operating systems design and implementation, pp 265–283. https://doi.org/10.48550/arXiv.1605.08695
Achal V, Pan X (2014) Influence of calcium sources on microbially induced calcium carbonate precipitation by Bacillus sp. CR2. Appl Biochem Biotechnol 173:307–317. https://doi.org/10.1007/s12010-014-0842-1
Achal V, Pan X, Fu Q, Zhang D (2012a) Biomineralization based remediation of As(III) contaminated soil by Sporosarcina ginsengisoli. J Hazard Mater 201–202:178–184. https://doi.org/10.1016/j.jhazmat.2011.11.067
Achal V, Pan X, Zhang D, Fu Q (2012b) Bioremediation of Pb-contaminated soil based on microbially induced calcite precipitation. J Microbiol Biotechnol 22:244–247. https://doi.org/10.4014/jmb.1108.08033
Adusumilli S, Bhatt D, Wang H, et al. (2013) A low-cost INS/GPS integration methodology based on random forest regression. Expert Syst Appl 40. https://doi.org/10.1016/j.eswa.2013.02.002
Afzal A, Aabid A, Khan A, et al. (2020) Response surface analysis, clustering, and random forest regression of pressure in suddenly expanded high-speed aerodynamic flows. Aerosp Sci Technol 107. https://doi.org/10.1016/j.ast.2020.106318
Akritas MG, van Keilegom I (2001) Non-parametric estimation of the residual distribution. Scand J Stat 28:549–567. https://doi.org/10.1111/1467-9469.00254
Al Qabany A, Soga K, Santamarina C (2012) Factors affecting efficiency of microbially induced calcite precipitation. J Geotech Geoenviron Eng 138:992–1001. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000666
Alexopoulos EC (2010) Introduction to multivariate regression analysis. Hippokratia 14:23–28
Ali TK, Esakki B (2020) CIRP Journal of Manufacturing Science and Technology. Study on compressive strength characteristics of selective inhibition sintered UHMWPE specimens based on ANN and RSM approach. CIRP J Manuf Sci Technol. https://doi.org/10.1016/j.cirpj.2020.05.016
Alves AAC, Chaparro Pinzon A, da Costa RM et al (2019) Multiple regression and machine learning based methods for carcass traits and saleable meat cuts prediction using non-invasive in vivo measurements in commercial lambs. Small Rumin Res 171:49–56. https://doi.org/10.1016/j.smallrumres.2018.12.008
Amarakoon GGNN, Kawasaki S (2018a) Factors affecting sand solidiflcation using MICP with Pararhodobacter sp. Mater Trans 59:72–81. https://doi.org/10.2320/matertrans.M-M2017849
Amarakoon GGNN, Kawasaki S (2018b) Factors affecting sand solidification using MICP with Pararhodobacter sp. Mater Trans 59:72–81. https://doi.org/10.2320/matertrans.M-M2017849
Amiri A, Azima M, Bas Z (2018) Crack remediation in mortar via biomineralization: effects of chemical admixtures on biogenic calcium carbonate. 190:317–325https://doi.org/10.1016/j.conbuildmat.2018.09.083
Amodu OS, Odunlami MO, Akintola JT et al (2022) Artificial neural network and response surface methodology for optimization of corrosion inhibition of mild steel in 1 M HCl by Musa paradisiaca peel extract. Heliyon 8:e11955. https://doi.org/10.1016/j.heliyon.2022.e11955
Asuero AG, Sayago A, González AG (2006) The correlation coefficient: an overview. Crit Rev Anal Chem 36:41–59
Baddeley A, Turner R, Møller J, Hazelton M (2005) Residual analysis for spatial point processes. J R Stat Soc Series B Stat 67:617–666. http://bemlar.ism.ac.jp/zhuang/pubs/baddeley2005jrss.pdf
Baghbani A, Choudhury T, Costa S, Reiner J (2022) Application of artificial intelligence in geotechnical engineering: a state-of-the-art review. Earth Sci Rev 228:103991. https://doi.org/10.1016/j.earscirev.2022.103991
Bagheri AR, Ghaedi M, Hajati S, et al. (2015) Random forest model for the ultrasonic-assisted removal of chrysoidine G by copper sulfide nanoparticles loaded on activated carbon; response surface methodology approach. RSC Adv 5. https://doi.org/10.1039/C5RA08399K
Basri M, Rahman RNZRA, Ebrahimpour A et al (2007) Comparison of estimation capabilities of response surface methodology (RSM) with artificial neural network (ANN) in lipase-catalyzed synthesis of palm-based wax ester. BMC Biotechnol 7:1–14. https://doi.org/10.1186/1472-6750-7-53
Batarseh FA, Mohod R, Kumar A, Bui J (2020) The application of artificial intelligence in software engineering: a review challenging conventional wisdom. Data Democracy 179–232. https://doi.org/10.48550/arXiv.2108.01591
Behnke M, Guo S, Guo W “Grace” (2021) Comparison of early stopping neural network and random forest for in-situ quality prediction in laser based additive manufacturing. Procedia Manuf 53:656–663https://doi.org/10.1016/j.promfg.2021.06.065
Bello O, Holzmann J, Yaqoob T, Teodoriu C (2015) Application of artificial intelligence methods in drilling system design and operations: a review of the state of the art. J Artif Intell Soft Comput Res 5:121–139. https://doi.org/10.1515/jaiscr-2015-0024
Berrar D (2019) Cross-validation. Encyclopedia of Bioinformatics and Computational Biology 1:542–545. https://doi.org/10.1016/B978-0-12-809633-8.20349-X
Bibi S, Oualha M, Ashfaq MY et al (2018) Isolation, differentiation and biodiversity of ureolytic bacteria of Qatari soil and their potential in microbially induced calcite precipitation (MICP) for soil stabilization. RSC Adv 8:5854–5863. https://doi.org/10.1039/C7RA12758H
Borgognone MG, Bussi J, Hough G (2001) Principal component analysis in sensory analysis: covariance or correlation matrix? Food Qual Prefer 12. https://doi.org/10.1016/S0950-3293(01)00017-9
Botchkarev A (2019a) A new typology design of performance metrics to measure errors in machine learning regression algorithms. Interdiscip J Inf Knowl Manag 14:45–76. https://doi.org/10.28945/4184
Bourquin J, Schmidli H, van Hoogevest P, Leuenberger H (1998) Advantages of artificial neural networks (ANNs) as alternative modelling technique for data sets showing non-linear relatio, ships using data from a galenical study on a solid dosage form. Pharmaceutical Sciences 7:5–16
Breiman L (2001) Random forests. Mach. Learn. Random Forests 5–32. https://doi.org/10.1201/9780429469275-8
Burn R (2020) Optimizing approximate leave-one-out cross-validation to tune hyperparameters. https://doi.org/10.48550/arXiv.2011.10218
Cai J, Luo J, Wang S, Yang S (2018) Feature selection in machine learning: a new perspective. Neurocomputing 300:70–79. https://doi.org/10.1016/j.neucom.2017.11.077
Chen D, Hu F, Nian G, Yang T (2020) Deep residual learning for nonlinear regression. Entropy 22. https://doi.org/10.3390/e22020193
Chen L, Song Y, Fang H, et al. (2022) Systematic optimization of a novel, cost-effective fermentation medium of Sporosarcina pasteurii for microbially induced calcite precipitation (MICP). Constr Build Mater 348. https://doi.org/10.1016/j.conbuildmat.2022.128632
Chen Y, Gao Y, Ng CWW, Guo H (2021) Bio-improved hydraulic properties of sand treated by soybean urease induced carbonate precipitation and its application Part 1: water retention ability. Transportation Geotechnics 27. https://doi.org/10.1016/j.trgeo.2020.100489
Chollet F et al (2015) Keras. https://keras.io
Darst BF, Malecki KC, Engelman CD (2018) Using recursive feature elimination in random forest to account for correlated variables in high dimensional data. BMC Genet 19. https://doi.org/10.1186/s12863-018-0633-8
DeJong JT, Soga K, Kavazanjian E et al (2014) Biogeochemical processes and geotechnical applications: progress, opportunities and challenges. Bio-and Chemo-Mech Process Geotech Eng: Géotechnique Symposium in Print 2013:143–157
Desai KM, Survase SA, Saudagar PS et al (2008) Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: case study of fermentative production of scleroglucan. Biochem Eng J 41:266–273. https://doi.org/10.1016/j.bej.2008.05.009
Dobbin KK, Simon RM (2011) Optimally splitting cases for training and testing high dimensional classifiers. BMC Med Genomics 4. https://doi.org/10.1186/1755-8794-4-31
Dozat T (2015) Incorporating nesterov momentum into Adam. Technical Report. https://cs229.stanford.edu/proj2015/054_report.pdf
Duraisamy Y, Airey DW (2015) Performance of biocemented Sydney sand using ex situ mixing technique. DFI Journal 9:48–56. https://doi.org/10.1179/1937525515Y.0000000002
Fabbri GTP, Furlan AP (2016) In situ pavement layer properties using artificial neural networks. Electron J Geotech Eng 21:6433–6444
Fatiha B, Sameh B, Youcef S et al (2013) Comparison of artificial neural network (ANN) and response surface methodology (RSM) in optimization of the immobilization conditions for lipase from Candida rugosa on Amberjet® 4200-Cl. Prep Biochem Biotechnol 43:33–47. https://doi.org/10.1080/10826068.2012.693899
Fawagreh K, Gaber MM, Elyan E (2014) Random forests: from early developments to recent advancements. Syst Sci Control Eng 2:602–609. https://doi.org/10.1080/21642583.2014.956265
Feng K, Montoya BM (2016) Influence of confinement and cementation level on the behavior of microbial-induced calcite precipitated sands under monotonic drained loading. J Geotech Geoenviron Eng 142:04015057. https://doi.org/10.1061/(asce)gt.1943-5606.0001379
Feng K, Montoya BM, Evans TM (2017) Discrete element method simulations of bio-cemented sands. Comput Geotech 85:139–150. https://doi.org/10.1016/j.compgeo.2016.12.028
Feng Z, Li X (2023) Microbially induced calcite precipitation and synergistic mineralization cementation mechanism of Pisha sandstone components. Sci Total Environ 866:161348. https://doi.org/10.1016/j.scitotenv.2022.161348
Follmer C (2010) Ureases as a target for the treatment of gastric and urinary infections. J Clin Pathol 63:424–430. https://doi.org/10.1136/jcp.2009.072595
Foong LK, Moayedi H, Lyu Z (2020) Computational modification of neural systems using a novel stochastic search scheme, namely evaporation rate-based water cycle algorithm: an application in geotechnical issues. Eng Comput. https://doi.org/10.1007/s00366-020-01000-3
Gan R, Li B, Tang T et al (2022) Noise optimization of multi-stage orifice plates based on RBF neural network response surface and adaptive NSGA-II. Ann Nucl Energy 178:109372. https://doi.org/10.1016/j.anucene.2022.109372
Ganapathy S, Balasubramanian P, Vasanth B, Thulasiraman S (2020) Materials today: proceedings comparative investigation of artificial neural network (ANN) and response surface methodology (RSM) expectation in EDM parameters. Mater Today Proc. https://doi.org/10.1016/j.matpr.2020.05.499
Gao Y, Hang L, He J, Chu J (2019) Mechanical behaviour of biocemented sands at various treatment levels and relative densities. Acta Geotech 14:697–707. https://doi.org/10.1007/s11440-018-0729-3
Gat D, Ronen Z, Tsesarsky M (2017) Long-term sustainability of microbial-induced CaCO3 precipitation in aqueous media. Chemosphere 184:524–531. https://doi.org/10.1016/j.chemosphere.2017.06.015
Gat D, Tsesarsky M, Shamir D (2011) Ureolytic calcium carbonate precipitation in the presence of non-ureolytic competing bacteria. In: Geo-Frontiers 2011: Advances in Geotechnical Engineering, pp 3966–3974. https://doi.org/10.1061/41165(397)405
Gat D, Tsesarsky M, Wahanon A, Ronen Z (2014) Ureolysis and MICP with model and native bacteria: implications for treatment strategies. Geotechnical Special Publication. American Society of Civil Engineers (ASCE), Atlanta, GA, pp 1713–1720
Ghosh A, Dey P (2021) Flood severity assessment of the coastal tract situated between Muriganga and Saptamukhi estuaries of Sundarban delta of India using frequency ratio (FR), fuzzy logic (FL), logistic regression (LR) and random forest (RF) models. Reg Stud Mar Sci 42:101624. https://doi.org/10.1016/j.rsma.2021.101624
Goldscheider N, Mádl-Szőnyi J, Erőss A, Schill E (2010) Review: thermal water resources in carbonate rock aquifers. Hydrogeol J 18:1303–1318. https://doi.org/10.1007/s10040-010-0611-3
Gomez MG, Anderson CM, Graddy CMR et al (2017) Large-scale comparison of bioaugmentation and biostimulation approaches for biocementation of sands. J Geotech Geoenviron Eng 143:1–13. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001640
Gomez MG, Graddy CMR, DeJong JT, Nelson DC (2019) Biogeochemical changes during bio-cementation mediated by stimulated and augmented ureolytic microorganisms. Sci Rep 9:1–15. https://doi.org/10.1038/s41598-019-47973-0
Gong G (1986) Cross-validation, the jackknife, and the bootstrap: excess error estimation in forward logistic regression. J Am Stat Assoc 81. https://doi.org/10.1080/01621459.1986.10478245
Gowthaman S, Mitsuyama S, Nakashima K et al (2019) Microbial induced slope surface stabilization using industrial-grade chemicals: a preliminary laboratory study. Int J Geomate 17:110–116. https://doi.org/10.21660/2019.60.8150
Han Z, Cheng X, Ma Q (2016) An experimental study on dynamic response for MICP strengthening liquefiable sands. Earthq Eng Eng Vib 15:673–679. https://doi.org/10.1007/s11803-016-0357-6
Hariharan R (2021) Random forest regression analysis on combined role of meteorological indicators in disease dissemination in an Indian city: a case study of New Delhi. Urban Clim 36:100780. https://doi.org/10.1016/j.uclim.2021.100780
Harrison JW, Lucius MA, Farrell JL et al (2021) Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using random forests regression. Sci Total Environ 763:143005. https://doi.org/10.1016/j.scitotenv.2020.143005
Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice-Hall Inc, New Jersey
He J, Chen X, Zhang Q, Achal V (2019) More effective immobilization of divalent lead than hexavalent chromium through carbonate mineralization by Staphylococcus epidermidis HJ2. Int Biodeterior Biodegradation 140:67–71. https://doi.org/10.1016/j.ibiod.2019.03.012
Hinton G, Srivastava N, Swersky K (2012) Overview of mini-batch gradient descent. Neural Networks for Machine Learning Lecture 6a. https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
Hu X-M, Liu J-D, Feng Y et al (2023) Application of urease-producing microbial community in seawater to dust suppression in desert. Environ Res 219:115121. https://doi.org/10.1016/j.envres.2022.115121
Huang S, Kuo C, Chen C et al (2016) RSM and ANN modeling-based optimization approach for the development of ultrasound-assisted liposome encapsulation of piceid. Ultrason Sonochem. https://doi.org/10.1016/j.ultsonch.2016.11.016
Huang SM, Kuo CH, Chen CA et al (2017) RSM and ANN modeling-based optimization approach for the development of ultrasound-assisted liposome encapsulation of piceid. Ultrason Sonochem 36:112–122. https://doi.org/10.1016/j.ultsonch.2016.11.016
Huang Y, Jin W, Yu Z, Li B (2020) Supervised feature selection through deep neural networks with pairwise connected structure. Knowl Based Syst 204:106202. https://doi.org/10.1016/j.knosys.2020.106202
Jiang NJ, Yoshioka H, Yamamoto K, Soga K (2016) Ureolytic activities of a urease-producing bacterium and purified urease enzyme in the anoxic condition: Implication for subseafloor sand production control by microbially induced carbonate precipitation (MICP). Ecol Eng 90:96–104. https://doi.org/10.1016/j.ecoleng.2016.01.073
Jung Y (2018) Multiple predicting K-fold cross-validation for model selection. J Nonparametr Stat 30. https://doi.org/10.1080/10485252.2017.1404598
Kahani M, Kalantary F, Soudi MR, et al. (2020a) Optimization of cost effective culture medium for Sporosarcina pasteurii as biocementing agent using response surface methodology: up cycling dairy waste and seawater. J Clean Prod 120022. https://doi.org/10.1016/j.jclepro.2020.120022
Kahani M, Kalantary F, Soudi MR, et al. (2020b) Optimization of cost effective culture medium for Sporosarcina pasteurii as biocementing agent using response surface methodology: up cycling dairy waste and seawater. J Clean Prod 253. https://doi.org/10.1016/j.jclepro.2020.120022
Kakelar MM, Ebrahimi S (2016) Up-scaling application of microbial carbonate precipitation: optimization of urease production using response surface methodology and injection modification. Int J Environ Sci Technol 13:2619–2628. https://doi.org/10.1007/s13762-016-1070-8
Kalil SJ, Maugeri F, Rodrigues MI (2000) Response surface analysis and simulation as a tool for bioprocess design and optimization. Process Biochem 35:539–550
Kasra AIA, Erkurt HA (2020) Biomineralization of different trace metals by using ureolytic bacteria isolated from soil. J Adv Res Dyn Control Syst 12:370–377. https://doi.org/10.5373/JARDCS/V12I3/20201203
Keramat-Jahromi M, Mohtasebi SS, Mousazadeh H et al (2021) Real-time moisture ratio study of drying date fruit chips based on on-line image attributes using kNN and random forest regression methods. Measurement (Lond) 172:108899. https://doi.org/10.1016/j.measurement.2020.108899
Keshtegar B, Nehdi ML, Trung NT, Kolahchi R (2021) Predicting load capacity of shear walls using SVR–RSM model[Formula presented]. Appl Soft Comput 112. https://doi.org/10.1016/j.asoc.2021.107739
Khodadadi Tirkolaei H, Bilsel H (2015) Statistical modeling of environmental factors on microbial urea hydrolysis process for biocement production. Advances in Materials Science and Engineering 2015. https://doi.org/10.1155/2015/340930
Kim B, Choi Y, Choi J et al (2020) Effect of surfactant on wetting due to fouling in membrane distillation membrane: application of response surface methodology (RSM) and artificial neural networks (ANN). Korean J Chem Eng 37:1–10. https://doi.org/10.1007/s11814-019-0420-x
Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. In: 3rd international conference for learning representations. https://doi.org/10.48550/arXiv.1412.6980
Kokkotis C, Moustakidis S, Papageorgiou E et al (2020) Machine learning in knee osteoarthritis: a review. Osteoarthr Cartil Open 2:100069. https://doi.org/10.1016/j.ocarto.2020.100069
Koul A, Becchio C, Cavallo A (2018) Cross-validation approaches for replicability in psychology. Front Psychol 9:1117. https://doi.org/10.3389/fpsyg.2018.01117
Krajewska B (2017) Urease-aided calcium carbonate mineralization for engineering applications: a review. J Adv Res. https://doi.org/10.1016/j.jare.2017.10.009
Krajewska B (2018) Urease-aided calcium carbonate mineralization for engineering applications: a review. J Adv Res 13:59–67. https://doi.org/10.1016/j.jare.2017.10.009
Kumar A, Song H-W, Mishra S, et al. (2023) Application of microbial-induced carbonate precipitation (MICP) techniques to remove heavy metal in the natural environment: a critical review. Chemosphere 137894. https://doi.org/10.1016/j.chemosphere.2023.137894
Lai Y, Yu J, Liu S, et al. (2021) Experimental study to improve the mechanical properties of iron tailings sand by using MICP at low pH. Constr Build Mater 273. https://doi.org/10.1016/j.conbuildmat.2020.121729
LaValle SM, Branicky MS, Lindemann SR (2004) On the relationship between classical grid search and probabilistic roadmaps. Int J Rob Res 23. https://doi.org/10.1177/0278364904045481
Lee K-Y, Kim K-H, Kang J-J et al (2017) Comparison and analysis of linear regression & artificial neural network. Int J Appl Eng 12:9820–9825. https://www.ripublication.com/ijaer17/ijaerv12n20_77.pdf
Li J, Ji L (2005) Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity (edinb) 95:221–227. https://doi.org/10.1038/sj.hdy.6800717
Lin W, Gao Y, Lin W et al (2023) Seawater-based bio-cementation of natural sea sand via microbially induced carbonate precipitation. Environ Technol Innov 29:103010. https://doi.org/10.1016/j.eti.2023.103010
Liu S, Wang X, Liu M, Zhu J (2017) Towards better analysis of machine learning models: a visual analytics perspective. Vis Inform 1:48–56. https://doi.org/10.1016/j.visinf.2017.01.006
Ma X, Qi M, Li Z et al (2019) Characterization of an efficient chloramphenicol-mineralizing bacterial consortium. Chemosphere 222:149–155. https://doi.org/10.1016/j.chemosphere.2019.01.131
Mani D, Kumar C (2014) Biotechnological advances in bioremediation of heavy metals contaminated ecosystems: an overview with special reference to phytoremediation. Int J Environ Sci Technol 11:843–872. https://doi.org/10.1007/s13762-013-0299-8
Mariano C, Mónica B (2021) A random forest-based algorithm for data-intensive spatial interpolation in crop yield mapping. Comput Electron Agric 184. https://doi.org/10.1016/j.compag.2021.106094
Martinez BC, DeJong JT (2009) Bio-mediated soil improvement: load transfer mechanisms at the micro- and macro-scales. In: Advances in ground improvement: research to practice in the United States and China, pp 242–251. https://doi.org/10.1061/41025(338)26
Moayedi H, Osouli A, Nguyen H, Rashid ASA (2019) A novel Harris hawks’ optimization and k-fold cross-validation predicting slope stability. Eng Comput. https://doi.org/10.1007/s00366-019-00828-8
Mondal PP, Galodha A, Verma VK et al (2023) Review on machine learning-based bioprocess optimization, monitoring, and control systems. Bioresour Technol 370:128523. https://doi.org/10.1016/j.biortech.2022.128523
Montoya BM, DeJong JT, Boulanger RW (2013) Dynamic response of liquefiable sand improved by microbial-induced calcite precipitation. Geotechnique 63:302–312. https://doi.org/10.1680/geot.SIP13.P.019
Mozumder RA, Laskar AI (2015) Prediction of unconfined compressive strength of geopolymer stabilized clayey soil using artificial neural network. Comput Geotech 69:291–300. https://doi.org/10.1016/j.compgeo.2015.05.021
Mujah D, Shahin MA, Cheng L (2017) State-of-the-art review of biocementation by microbially induced calcite precipitation (MICP) for soil stabilization. Geomicrobiol J 34:524–537. https://doi.org/10.1080/01490451.2016.1225866
Mwandira W, Nakashima K, Kawasaki S (2017) Bioremediation of lead-contaminated mine waste by Pararhodobacter sp. based on the microbially induced calcium carbonate precipitation technique and its effects on strength of coarse and fine grained sand. Ecol Eng 109:57–64. https://doi.org/10.1016/j.ecoleng.2017.09.011
Myers RH, Montgomery DC, Anderson-Cook CM (1995) Response surface methodology: process and product optimization using designed experiments, 4th edn. John Wiley & Sons LTD, New York
Nafisi A, Montoya BM, Evans TM (2020) Shear strength envelopes of biocemented sands with varying particle size and cementation level. J Geotech Geoenviron Eng 146:04020002. https://doi.org/10.1061/(asce)gt.1943-5606.0002201
Nafisi A, Safavizadeh S, Montoya BM (2019) Influence of microbe and enzyme-induced treatments on cemented sand shear response. J Geotech Geoenviron Eng 145:1–8. https://doi.org/10.1061/(ASCE)GT.1943-5606.0002111
Nakamura E (2005) Inflation forecasting using a neural network. Econ Lett 86:373–378. https://doi.org/10.1016/j.econlet.2004.09.003
Nassar MK, Bastani M, Shafei B, et al. (2018) Large-scale experiments in microbially-induced calcite precipitation (MICP): reactive transport model development and prediction. https://doi.org/10.1002/2017WR021488
Nesaragi N, Patidar S, Thangaraj V (2021) A correlation matrix-based tensor decomposition method for early prediction of sepsis from clinical data. Biocybern Biomed Eng 41:1013–1024. https://doi.org/10.1016/j.bbe.2021.06.009
Nweke CC, Pestana JM (2018) Modeling bio-cemented sands: a strength index for cemented sands. In: IFCEE 2018, pp 48–58. https://doi.org/10.1061/9780784481592.006
Okyay TO, Rodrigues DF (2014) Optimized carbonate micro-particle production by Sporosarcina pasteurii using response surface methodology. Ecol Eng 62:168–174. https://doi.org/10.1016/j.ecoleng.2013.10.024
Omoregie AI, Ngu LH, Ong DEL, Nissom PM (2019) Low-cost cultivation of Sporosarcina pasteurii strain in food-grade yeast extract medium for microbially induced carbonate precipitation (MICP) application. Biocatal Agric Biotechnol 17:247–255. https://doi.org/10.1016/j.bcab.2018.11.030
Patra PBRD, Kundu P et al (2023) Recent advances in machine learning applications in metabolic engineering. Biotechnol Adv 62:108069. https://doi.org/10.1016/j.biotechadv.2022.108069
Payen FT, Sykes A, Aitkenhead M et al (2021) Predicting the abatement rates of soil organic carbon sequestration management in Western European vineyards using random forest regression. Clean Environ Syst 2:100024. https://doi.org/10.1016/j.cesys.2021.100024
Pérez-Rave JI, Correa-Morales JC, González-Echavarría F (2019) A machine learning approach to big data regression analysis of real estate prices for inferential and predictive purposes. J Prop Res 36:59–96. https://doi.org/10.1080/09599916.2019.1587489
Pilkington JL, Preston C, Gomes RL (2014) Comparison of response surface methodology (RSM) and artificial neural networks (ANN) towards efficient extraction of artemisinin from Artemisia annua. Ind Crops Prod 58:15–24. https://doi.org/10.1016/j.indcrop.2014.03.016
Puri M, Solanki A, Padawer T et al (2016) Introduction to artificial neural network (ANN) as a predictive tool for drug design, discovery, delivery, and disposition: basic concepts and modeling. Elsevier Inc, Basic Concepts and Modeling
Rácz A, Bajusz D, Héberger K (2021) Effect of dataset size and train/test split ratios in qsar/qspr multiclass classification. Molecules 26. https://doi.org/10.3390/molecules26041111
Rajasekar A, Wilkinson S, Moy CKS (2021) MICP as a potential sustainable technique to treat or entrap contaminants in the natural environment: a review. Environ Sci Ecotechnol 6:100096. https://doi.org/10.1016/j.ese.2021.100096
Richman R, Wüthrich MV (2020) Nagging predictors. Risks 8:1–26. https://doi.org/10.3390/risks8030083
Rodríguez JD, Pérez A, Lozano JA (2010) Sensitivity analysis of k-fold cross validation in prediction error estimation. IEEE Trans Pattern Anal Mach Intell 32:569–575. https://doi.org/10.1109/TPAMI.2009.187
Sarker IH (2021) Machine learning: algorithms, real-world applications and research directions. SN Comput Sci 2:160. https://doi.org/10.1007/s42979-021-00592-x
Scheck L, Weissmann M, Mayer B (2018) Efficient methods to account for cloud-top inclination and cloud overlap in synthetic visible satellite images. J Atmos Ocean Technol 35:665–685. https://doi.org/10.1175/JTECH-D-17-0057.1
Sha W, Edwards KL (2007) The use of artificial neural networks in materials science based research. Mater Des 28:1747–1752
Sharma A, Ramkrishnan R, R, (2016) Study on effect of microbial induced calcite precipitates on strength of fine grained soils. Perspect Sci (Neth) 8:198–202. https://doi.org/10.1016/j.pisc.2016.03.017
Sharma M, Satyam N (2021) Strength and durability of biocemented sands: wetting-drying cycles, ageing effects, and liquefaction resistance. Geoderma 402:115359. https://doi.org/10.1016/j.geoderma.2021.115359
Shekar BH, Dagnew G (2019) Grid search-based hyperparameter tuning and classification of microarray cancer data. In: 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP). https://doi.org/10.1109/ICACCP.2019.8882943
Shozib IA, Ahmad A, Rahaman MSA et al (2021) Modelling and optimization of microhardness of electroless Ni-P-TiO2composite coating based on machine learning approaches and RSM. J Market Res 12:1010–1025. https://doi.org/10.1016/j.jmrt.2021.03.063
Singh N, Singh J, Singh K (2018) Small at size, big at impact: microorganisms for sustainable development. In: Microbial bioprospecting for sustainable development, pp 3–28. https://doi.org/10.1007/978-981-13-0053-0_1
Singhal A, Kumari N, Ghosh P, et al. (2022) Optimizing cellulase production from Aspergillus flavus using response surface methodology and machine learning models. Environ Technol Innov 27. https://doi.org/10.1016/j.eti.2022.102805
Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2:568–576. https://doi.org/10.1109/72.97934
Sun M, Cao J, Cao J et al (2022) Discrete element modeling of shear wave propagation in carbonate precipitate–cemented particles. Acta Geotech 17:2633–2649. https://doi.org/10.1007/s11440-022-01456-1
Tseng MM, Wang Y, Jiao RJ (2017) Mass customization. CIRP encyclopedia of production engineering. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 1–8
van Paassen LA, Ghose R, van der Linden TJM et al (2010) Quantifying biomediated ground improvement by ureolysis: large-scale biogrout experiment. J Geotech Geoenviron Eng 136:1721–1728. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000382
van Rossum G, Drake FL (2009) Python 3 reference manual. CreateSpace, Scotts Valley, CA, p 242
van Wijngaarden WK, van Paassen LA, Vermolen FJ et al (2016) Simulation of front instabilities in density-driven flow, using a reactive transport model for biogrout combined with a randomly distributed permeability field. Transp Porous Media 112:333–359. https://doi.org/10.1007/s11242-016-0649-3
van Wijngaarden WK, Vermolen FJ, van Meurs GAM, Vuik C (2013) A mathematical model for biogrout: bacterial placement and soil reinforcement. Comput Geosci 17:463–478. https://doi.org/10.1007/s10596-012-9316-0
Wallace MI, Ng KC (2016) Development and application of underground space use in Hong Kong. Tunn Undergr Space Technol 55:257–279. https://doi.org/10.1016/j.tust.2015.11.024
Wang K, Chu J, Wu S, He J (2021) Stress–strain behaviour of bio-desaturated sand under undrained monotonic and cyclic loading. Géotechnique 71:521–533. https://doi.org/10.1680/jgeot.19.P.080
Wang X, Liu A, Kara S (2022) Machine learning for engineering design toward smart customization: a systematic review. J Manuf Syst 65:391–405
Whiffin VS, van Paassen LA, Harkes MP (2007) Microbial carbonate precipitation as a soil improvement technique. Geomicrobiol J 24:417–423. https://doi.org/10.1080/01490450701436505
Wiesmeier M, Barthold F, Blank B, Kögel-Knabner I (2011) Digital mapping of soil organic matter stocks using random forest modeling in a semi-arid steppe ecosystem. Plant Soil 340. https://doi.org/10.1007/s11104-010-0425-z
Wong TT (2015) Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognit 48:2839–2846. https://doi.org/10.1016/j.patcog.2015.03.009
Wu H, Wu W, Liang W et al (2022) 3D DEM modeling of biocemented sand with fines as cementing agents. Int J Numer Anal Methods Geomech. https://doi.org/10.1002/nag.3466
Xiao Y, He X, Zaman M, et al. (2022a) Review of strength improvements of biocemented soils. International Journal of Geomechanics 22. https://doi.org/10.1061/(asce)gm.1943-5622.0002565
Xiao Y, He X, Zaman M, et al. (2022b) Review of strength improvements of biocemented soils. International Journal of Geomechanics 22. https://doi.org/10.1061/(asce)gm.1943-5622.0002565
Xue L, Liu Y, Xiong Y et al (2021) A data-driven shale gas production forecasting method based on the multi-objective random forest regression. J Pet Sci Eng 196:107801. https://doi.org/10.1016/j.petrol.2020.107801
Yadav S, Shukla S (2016) Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification. In: PIEEE 6th International Conference on Advanced Computing (IACC), pp 78–83. https://doi.org/10.1109/IACC.2016.25
Yasuhara H, Neupane D, Hayashi K, Okamura M (2012) Experiments and predictions of physical properties of sand cemented by enzymatically-induced carbonate precipitation. Soils Found 52:539–549. https://doi.org/10.1016/j.sandf.2012.05.011
Zamani A, Montoya BM (2016) Permeability reduction due to microbial induced calcite precipitation in sand. In: Geo-Chicago 2016 94–103. https://doi.org/10.1061/9780784480120.011
Zhang C, Jiang J, Ma J et al (2015) Evaluating soil reinforcement by plant roots using artificial neural networks. Soil Use Manag 31:408–416. https://doi.org/10.1111/sum.12196
Zhu JH, Zaman MM, Anderson SA (1998) Modelling of shearing behaviour of a residual soil with recurrent neural network. Int J Numer Anal Methods Geomech 22:671–687. https://doi.org/10.1002/(SICI)1096-9853(199808)22:8%3c671::AID-NAG939%3e3.0.CO;2-Y
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This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES)-Finance Code 001 and by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq–Grants #312756/2017–8 and #314643/2020–6).
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VLP, LB, FDR, and AT designed the content and logic of this experimental optimization via artificial neural networks. VLP and LB finished the first-hand manuscript; also, FDR and AT revised this manuscript. All authors read and approved the final manuscript.
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Pacheco, V.L., Bragagnolo, L., Dalla Rosa, F. et al. Optimization of biocementation responses by artificial neural network and random forest in comparison to response surface methodology. Environ Sci Pollut Res 30, 61863–61887 (2023). https://doi.org/10.1007/s11356-023-26362-1
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DOI: https://doi.org/10.1007/s11356-023-26362-1