Application of 3-algorithm ANN programming to predict the strength performance of hydrated-lime activated rice husk ash treated soil
Artificial neural network (ANN) method has been applied in the present work to predict the California bearing ratio (CBR), unconfined compressive strength (UCS), and resistance value (R) of expansive soil treated with recycled and activated composites of rice husk ash. Pavement foundations suffer fr...
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| Veröffentlicht in: | Multiscale and Multidisciplinary Modeling, Experiments and Design Jg. 4; H. 4; S. 259 - 274 |
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| Abstract | Artificial neural network (ANN) method has been applied in the present work to predict the California bearing ratio (CBR), unconfined compressive strength (UCS), and resistance value (R) of expansive soil treated with recycled and activated composites of rice husk ash. Pavement foundations suffer from poor design and construction, poor material handling and utilization and management lapses. The evolutions of soft computing techniques have produced various algorithms developed to overcome certain lapses in performance. Three of such algorithms from ANN are Levenberg–Muarquardt Backpropagation (LMBP), Bayesian Programming (BP), and Conjugate Gradient (CG) algorithms. In this work, the expansive soil classified as A-7-6 group soil was treated with hydrated-lime activated rice husk ash (HARHA) in varying proportions between 0.1 and 12% by weight of soil at the rate of 0.1% to produce 121 datasets. These were used to predict the behavior of the soil’s strength parameters (CBR, UCS and R) utilizing the evolutionary hybrid algorithms of ANN. The predictor parameters were HARHA, liquid limit (
w
L
), (plastic limit (
w
P
), plasticity index (
I
P
), optimum moisture content (
w
OMC
), clay activity (
A
C
), and (maximum dry density (
δ
max
). A multiple linear regression (MLR) was also conducted on the datasets in addition to ANN to serve as a check and linear validation mechanism. MLR and ANN methods agreed in terms of performance and fit at the end of computing and iteration. However, the response validation on the predicted models showed a good correlation above 0.9 and a great performance index. Comparatively, the LMBP algorithm yielded an accurate estimation of the results in lesser iterations than the Bayesian and the CG algorithms, while the Bayesian technique produced the best result with the required number of iterations to minimize the error. And finally, the LMBP algorithm outclassed the other two algorithms in terms of the predicted models’ accuracy. |
|---|---|
| AbstractList | Artificial neural network (ANN) method has been applied in the present work to predict the California bearing ratio (CBR), unconfined compressive strength (UCS), and resistance value (R) of expansive soil treated with recycled and activated composites of rice husk ash. Pavement foundations suffer from poor design and construction, poor material handling and utilization and management lapses. The evolutions of soft computing techniques have produced various algorithms developed to overcome certain lapses in performance. Three of such algorithms from ANN are Levenberg–Muarquardt Backpropagation (LMBP), Bayesian Programming (BP), and Conjugate Gradient (CG) algorithms. In this work, the expansive soil classified as A-7-6 group soil was treated with hydrated-lime activated rice husk ash (HARHA) in varying proportions between 0.1 and 12% by weight of soil at the rate of 0.1% to produce 121 datasets. These were used to predict the behavior of the soil’s strength parameters (CBR, UCS and R) utilizing the evolutionary hybrid algorithms of ANN. The predictor parameters were HARHA, liquid limit (
w
L
), (plastic limit (
w
P
), plasticity index (
I
P
), optimum moisture content (
w
OMC
), clay activity (
A
C
), and (maximum dry density (
δ
max
). A multiple linear regression (MLR) was also conducted on the datasets in addition to ANN to serve as a check and linear validation mechanism. MLR and ANN methods agreed in terms of performance and fit at the end of computing and iteration. However, the response validation on the predicted models showed a good correlation above 0.9 and a great performance index. Comparatively, the LMBP algorithm yielded an accurate estimation of the results in lesser iterations than the Bayesian and the CG algorithms, while the Bayesian technique produced the best result with the required number of iterations to minimize the error. And finally, the LMBP algorithm outclassed the other two algorithms in terms of the predicted models’ accuracy. |
| Author | Onuoha, Ifeanyichukwu C. Jalal, Fazal E. Onyelowe, Kennedy C. Iqbal, Mudassir Onyia, Michael E. |
| Author_xml | – sequence: 1 givenname: Kennedy C. orcidid: 0000-0001-5218-820X surname: Onyelowe fullname: Onyelowe, Kennedy C. email: kennedychibuzor@kiu.ac.ug, konyelowe@mouau.edu.ng, konyelowe@gmail.com organization: Department of Civil Engineering, Michael Okpara University of Agriculture, Department of Civil and Mechanical Engineering, Kampala International University – sequence: 2 givenname: Mudassir surname: Iqbal fullname: Iqbal, Mudassir organization: Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, School of Naval Architecture, Ocean and Civil Engineering, Shangai Jiao Tong University – sequence: 3 givenname: Fazal E. surname: Jalal fullname: Jalal, Fazal E. organization: Department of Civil Engineering, Shanghai Jiao Tong University – sequence: 4 givenname: Michael E. surname: Onyia fullname: Onyia, Michael E. organization: Department of Civil Engineering, Faculty of Engineering, University of Nigeria – sequence: 5 givenname: Ifeanyichukwu C. surname: Onuoha fullname: Onuoha, Ifeanyichukwu C. organization: Department of Environmental Technology, Federal University of Technology |
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| Cites_doi | 10.1016/j.ijprt.2018.04.003 10.3354/cr030079 10.1007/s00521-015-1943-7 10.1002/cyto.a.20896 10.1016/j.advengsoft.2017.03.011 10.1080/14697688.2019.1633014 10.1093/Ijlct/Ctz028 10.1016/j.atmosenv.2008.10.005 10.2991/iccsae-15.2016.43 10.2478/s11600-008-0073-3 10.1016/j.protcy.2013.12.157 10.4271/2012-01-0223 10.1109/TASL.2008.919072 10.14382/epitoanyag-jsbcm.2020.35 10.2174/1874836802014010237 10.33922/j.ujet_v6i1_9 10.14382/epitoanyag-jsbcm.2020.36 10.1016/j.eti.2018.04.005 10.1016/j.proeng.2017.05.286 10.3390/app10134565 10.1007/978-3-642-00296-0_5 10.1016/j.jhazmat.2019.121322 |
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| Keywords | Bayesian algorithm Back-propagation algorithm Artificial neural network (ANN) Soft computing Sustainable construction materials Artificial intelligence Machine learning in geotechnics Levenberg–muarquardt algorithm Conjugate gradient algorithm |
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| References | KisiOUncuogluEComparison of three back-propagation training algorithms for two case studiesIndian J Eng Materials Sci2005122005434442 Onyelowe KC, Alaneme GU, Onyia ME, Bui Van D, Diomonyeka MU, Nnadi E, Ogbonna C, Odum LO, Aju DE, Abel C, Udousoro IM, Onukwugha E (2021) Comparative modeling of strength properties of hydrated-lime activated rice-husk-ash (HARHA) modified soft soil for pavement construction purposes by artificial neural network (ANN) and fuzzy logic (FL). Jurnal Kejuruteraan 33(2) Alaneme GU, Onyelowe KC, Onyia ME, Bui Van D, Mbadike EM, Ezugwu CN, Dimonyeka MU, Attah IC, Ogbonna C, Abel C, Ikpa CC, Udousoro IM (2020) Modeling volume change properties of hydrated-lime activated rice husk ash (HARHA) modified soft soil for construction purposes by artificial neural network (ANN). Umudike J Eng Technol (UJET) 6(1):1–12. https://doi.org/https://doi.org/10.33922/j.ujet_v6i1_9 Benesty J et al. (2009) Pearson correlation coefficient, in Noise reduction in speech proceeding, 2009, Springer, p. 1–4 WillmottCJMatsuuraKAdvantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performanceClim Res2005301798210.3354/cr030079 AdlerJParmryd J (2010) Quantifying colocalization by correlation: pearson correlation coeeficient is superior to the Mander, s overlap coefficientCytometry A201077873374210.1002/cyto.a.20896 KingstonGBMaierHRLambertMFA Bayesian approach to artificial neural network model selectionCentre Appl Model Water Eng School Civ Environ Eng Univ Adelaide Bull20166201618531859 Sariev E, Germano G (2019). Bayesian regularized artificial neural networks for the estimation of the probability of default. Quantitative Finance, 20: 2, 311–328, doi: https://doi.org/10.1080/14697688.2019.1633014 BS 1377 - 2, 3, 1990. 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Methods of Soil Description, British Standard Institute, London FerentinouMFakirMAn ANN approach for the prediction of uniaxial compressive strength, of some sedimentary and Igneous Rocks in Eastern KwaZulu-NatalSymp Int Soc Rock Mech Proc Eng201719120171117112510.1016/j.proeng.2017.05.286 BabanajadSKGandomiAHAlaviAHNew prediction models for concrete ultimate strength under true-triaxial stress states: An evolutionary approachAdv Eng Softw20172017110556810.1016/j.advengsoft.2017.03.011 Onyelowe KC, Onyia ME, Onukwugha ER, Nnadi OC, Onuoha IC, Jalal FE (2020) Polynomial relationship of compaction properties of silicate-based RHA modified expansive soil for pavement subgrade purposes Epitőanyag—J Silicate Based Composite Materials 72(6):223–228. https://doi.org/https://doi.org/10.14382/epitoanyag-jsbcm.2020.36 Van BuiDOnyeloweKCVan NguyenMCapillary rise, suction (absorption) and the strength development of HBM treated with QD base GeopolymerInt J Pavement Res Technol [in press]201810.1016/j.ijprt.2018.04.003 SaldañaMPérez-ReyJGIJeldresMToroNApplying statistical analysis and machine learning for modeling the UCS from P-Wave velocity, density and porosity on dry travertineAppl Sci202010456510.3390/app10134565 VanBDuc and Onyelowe, K.C. 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| References_xml | – reference: Quan S, Sun P, Wu G, Hu J (2015) One bayesian network construction algorithm based on dimensionality reduction. In: 5th international conference on computer sciences and automation engineering (ICCSAE 2015), Atlantis Publishers, p. 222–229 – reference: BenestyJChenJHuangYOn the importance of the Pearson correlation coefficient in noise reductionIEEE Trans Audio Speech Language Proc200816475776510.1109/TASL.2008.919072 – reference: WillmottCJMatsuuraKAdvantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performanceClim Res2005301798210.3354/cr030079 – reference: KingstonGBMaierHRLambertMFA Bayesian approach to artificial neural network model selectionCentre Appl Model Water Eng School Civ Environ Eng Univ Adelaide Bull20166201618531859 – reference: BS 1377 - 2, 3, 1990. Methods of Testing Soils for Civil Engineering Purposes, British Standard Institute, London – reference: ErzinYTurkozDUse of neural networks for the prediction of the CBR value of some Aegean sandsNeural Comput Applic2016271415142610.1007/s00521-015-1943-7 – reference: HosseiniMNaeiniSARMDehghaniAAZeraatpishehMModeling of soil mechanical resistance using intelligentmethodsJ Soil Sci Plant Nutr2018184939951 – reference: AdlerJParmryd J (2010) Quantifying colocalization by correlation: pearson correlation coeeficient is superior to the Mander, s overlap coefficientCytometry A201077873374210.1002/cyto.a.20896 – reference: SaldañaMPérez-ReyJGIJeldresMToroNApplying statistical analysis and machine learning for modeling the UCS from P-Wave velocity, density and porosity on dry travertineAppl Sci202010456510.3390/app10134565 – reference: Benesty J et al. (2009) Pearson correlation coefficient, in Noise reduction in speech proceeding, 2009, Springer, p. 1–4 – reference: OnyeloweKCVan BuiDUbachukwuORecycling and reuse of solid wastes; a hub for ecofriendly, ecoefficient and sustainable soil, concrete, wastewater and pavement reengineeringInt J Low-Carbon Technol201914344045110.1093/Ijlct/Ctz028 – reference: BabanajadSKGandomiAHAlaviAHNew prediction models for concrete ultimate strength under true-triaxial stress states: An evolutionary approachAdv Eng Softw20172017110556810.1016/j.advengsoft.2017.03.011 – reference: Van BuiDOnyeloweKCVan NguyenMCapillary rise, suction (absorption) and the strength development of HBM treated with QD base GeopolymerInt J Pavement Res Technol [in press]201810.1016/j.ijprt.2018.04.003 – reference: Sariev E, Germano G (2019). Bayesian regularized artificial neural networks for the estimation of the probability of default. Quantitative Finance, 20: 2, 311–328, doi: https://doi.org/10.1080/14697688.2019.1633014 – reference: Alaneme GU, Onyelowe KC, Onyia ME, Bui Van D, Mbadike EM, Ezugwu CN, Dimonyeka MU, Attah IC, Ogbonna C, Abel C, Ikpa CC, Udousoro IM (2020) Modeling volume change properties of hydrated-lime activated rice husk ash (HARHA) modified soft soil for construction purposes by artificial neural network (ANN). Umudike J Eng Technol (UJET) 6(1):1–12. https://doi.org/https://doi.org/10.33922/j.ujet_v6i1_9 – reference: Onyelowe KC, Onyia M, Onukwugha ER, Bui Van D, Obimba-Wogu J, Ikpa C (2020) Mechanical properties of fly ash modified asphalt treated with crushed waste glasses as fillers for sustainable pavements. 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| Title | Application of 3-algorithm ANN programming to predict the strength performance of hydrated-lime activated rice husk ash treated soil |
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