Forecasting compressive strength and electrical resistivity of graphite based nano-composites using novel artificial intelligence techniques

[1]  Nzar Shakr Piro,et al.  Evaluate and Predict the Resist Electric Current and Compressive Strength of Concrete Modified with GGBS and Steelmaking Slag Using Mathematical Models , 2022, Journal of Sustainable Metallurgy.

[2]  A. Mohammed,et al.  Proposing several model techniques including ANN and M5P-tree to predict the compressive strength of geopolymer concretes incorporated with nano-silica , 2022, Environmental Science and Pollution Research.

[3]  Yimiao Huang,et al.  Multi-objective design optimization for graphite-based nanomaterials reinforced cementitious composites: A data-driven method with machine learning and NSGA-Ⅱ , 2022, Construction and Building Materials.

[4]  R. Kurda,et al.  Electrical resistivity-Compressive strength predictions for normal strength concrete with waste steel slag as a coarse aggregate replacement using various analytical models , 2022, Construction and Building Materials.

[5]  Amir Mosavi,et al.  Statistical Methods for Modeling the Compressive Strength of Geopolymer Mortar , 2022, Materials.

[6]  P. G. Asteris,et al.  Interpreting the experimental results of compressive strength of hand-mixed cement-grouted sands using various mathematical approaches , 2021, Archives of Civil and Mechanical Engineering.

[7]  Nzar Shakr Piro,et al.  Multiple Analytical Models to Evaluate the Impact of Carbon Nanotubes on the Electrical Resistivity and Compressive Strength of the Cement Paste , 2021, Sustainability.

[8]  Danial Jahed Armaghani,et al.  The Effects of Rock Index Tests on Prediction of Tensile Strength of Granitic Samples: A Neuro-Fuzzy Intelligent System , 2021, Sustainability.

[9]  Z. Tao,et al.  Development of piezoresistive cement-based sensor using recycled waste glass cullets coated with carbon nanotubes , 2021 .

[10]  Mahdi Hasanipanah,et al.  Integrating the LSSVM and RBFNN models with three optimization algorithms to predict the soil liquefaction potential , 2021, Engineering with Computers.

[11]  Brett Nener,et al.  Mixture optimization for environmental, economical and mechanical objectives in silica fume concrete: A novel frame-work based on machine learning and a new meta-heuristic algorithm , 2021 .

[12]  Kaili Xu,et al.  Investigation on preparation and multifunctionality of reduced graphene oxide cement mortar , 2021 .

[13]  Theodore E. Matikas,et al.  Multifunctional Cement Mortars Enhanced with Graphene Nanoplatelets and Carbon Nanotubes , 2021, Sensors.

[14]  Guy Van den Broeck,et al.  On the Tractability of SHAP Explanations , 2020, AAAI.

[15]  Huanyu Li,et al.  Hybrid graphene oxide/carbon nanotubes reinforced cement paste: An investigation on hybrid ratio , 2020 .

[16]  Jinhao Xu,et al.  Study of mechanical properties and early-stage deformation properties of graphene-modified cement-based materials , 2020 .

[17]  D. Panesar,et al.  Nano reinforced cement paste composite with functionalized graphene and pristine graphene nanoplatelets , 2020 .

[18]  Muhammad Izhar Shah,et al.  Applications of Gene Expression Programming and Regression Techniques for Estimating Compressive Strength of Bagasse Ash based Concrete , 2020, Crystals.

[19]  Kaili Xu,et al.  Experimental Study on Mechanical and Functional Properties of Reduced Graphene Oxide/Cement Composites , 2020, Materials.

[20]  Guowei Ma,et al.  XGBoost algorithm-based prediction of concrete electrical resistivity for structural health monitoring , 2020 .

[21]  A. Ashour,et al.  Effect and mechanisms of nanomaterials on interface between aggregates and cement mortars , 2020 .

[22]  Linhua Jiang,et al.  Research on electrical conductivity of graphene/cement composites , 2020 .

[23]  D. Hui,et al.  A review on the properties, reinforcing effects, and commercialization of nanomaterials for cement-based materials , 2020 .

[24]  S. Memon,et al.  Effect of Graphene Oxide/Graphene Hybrid on Mechanical Properties of Cement Mortar and Mechanism Investigation , 2020, Nanomaterials.

[25]  Sardar Kashif Ur Rehman,et al.  Experimental Investigation of Hybrid Carbon Nanotubes and Graphite Nanoplatelets on Rheology, Shrinkage, Mechanical, and Microstructure of SCCM , 2020, Materials.

[26]  Muhammad Iqbal,et al.  Prediction of mechanical properties of green concrete incorporating waste foundry sand based on gene expression programming. , 2020, Journal of hazardous materials.

[27]  Farhad Aslani,et al.  A review on material design, performance, and practical application of electrically conductive cementitious composites , 2019 .

[28]  Wang Baomin,et al.  Effect and mechanism of graphene nanoplatelets on hydration reaction, mechanical properties and microstructure of cement composites , 2019 .

[29]  N. Arena,et al.  Graphene nanoplatelet reinforced concrete for self-sensing structures – A lifecycle assessment perspective , 2019 .

[30]  B. Pang,et al.  Mechanical property and toughening mechanism of water reducing agents modified graphene nanoplatelets reinforced cement composites , 2019, Construction and Building Materials.

[31]  Yancheng Li,et al.  A state-of-the-art on self-sensing concrete: Materials, fabrication and properties , 2019, Composites Part B: Engineering.

[32]  D. Ouyang,et al.  Effect of Graphene Oxide on Mechanical Properties of Cement Mortar and its Strengthening Mechanism , 2019, Materials.

[33]  Shi-lang Xu,et al.  Reinforcing Mechanism of Graphene and Graphene Oxide Sheets on Cement-Based Materials , 2019, Journal of Materials in Civil Engineering.

[34]  Chunping Gu,et al.  Study on dispersion, mechanical and microstructure properties of cement paste incorporating graphene sheets , 2019, Construction and Building Materials.

[35]  H. Eskandari-Naddaf,et al.  Effect of cement strength class on the prediction of compressive strength of cement mortar using GEP method , 2019, Construction and Building Materials.

[36]  C. Cai,et al.  Mechanical properties and microstructure of graphene oxide cement-based composites , 2019, Construction and Building Materials.

[37]  Baomin Wang,et al.  Effect of graphene nanoplatelets on the properties, pore structure and microstructure of cement composites , 2018, Materials Express.

[38]  Emad Benhelal,et al.  Graphene-based nanosheets for stronger and more durable concrete: A review , 2018, Construction and Building Materials.

[39]  N. Skipper,et al.  Understanding the behaviour of graphene oxide in Portland cement paste , 2018, Cement and Concrete Research.

[40]  A. El-Dieb,et al.  Multifunctional electrically conductive concrete using different fillers , 2018 .

[41]  Haibin Yang,et al.  Experimental study of the effects of graphene oxide on microstructure and properties of cement paste composite , 2017 .

[42]  W. Duan,et al.  Effects of graphene oxide agglomerates on workability, hydration, microstructure and compressive strength of cement paste , 2017 .

[43]  Amir Hossein Gandomi,et al.  New prediction models for concrete ultimate strength under true-triaxial stress states: An evolutionary approach , 2017, Adv. Eng. Softw..

[44]  Scott Lundberg,et al.  A Unified Approach to Interpreting Model Predictions , 2017, NIPS.

[45]  Seyed Saeed Mahini,et al.  Lightweight concrete design using gene expression programing , 2017 .

[46]  Jinping Ou,et al.  Nano graphite platelets-enabled piezoresistive cementitious composites for structural health monitoring , 2017 .

[47]  Surendra P. Shah,et al.  Effects of Graphene Oxide On Early-age Hydration And Electrical Resistivity Of Portland Cement Paste , 2017 .

[48]  Cheng Zhou,et al.  Enhanced mechanical properties of cement paste by hybrid graphene oxide/carbon nanotubes , 2017 .

[49]  A. Gandomi,et al.  New formulations for mechanical properties of recycled aggregate concrete using gene expression programming , 2017 .

[50]  T. Tong,et al.  Experimental investigation on mechanical and piezoresistive properties of cementitious materials containing graphene and graphene oxide nanoplatelets , 2016 .

[51]  Zhenlin Wu,et al.  Investigation of the Mechanical Properties and Microstructure of Graphene Nanoplatelet-Cement Composite , 2016, Nanomaterials.

[52]  Snigdha Sharma,et al.  Comparative effects of pristine and ball-milled graphene oxide on physico-chemical characteristics of cement mortar nanocomposites , 2016 .

[53]  M. Cao,et al.  Effect of graphene on mechanical properties of cement mortars , 2016 .

[54]  Teng Tong,et al.  Investigation of the effects of graphene and graphene oxide nanoplatelets on the micro- and macro-properties of cementitious materials , 2016 .

[55]  Z. Metaxa Exfoliated graphene nanoplatelet cement-based nanocomposites as piezoresistive sensors: influence of nanoreinforcement lateral size on monitoring capability , 2016 .

[56]  Hongjian Du,et al.  Enhancement of barrier properties of cement mortar with graphene nanoplatelet , 2015 .

[57]  Z. S. Metaxa,et al.  Polycarboxylate Based Superplasticizers as Dispersant Agents for Exfoliated Graphene Nanoplatelets Reinforcing Cement Based Materials , 2015 .

[58]  Amir Hossein Gandomi,et al.  Assessment of artificial neural network and genetic programming as predictive tools , 2015, Adv. Eng. Softw..

[59]  Jian Wang,et al.  Influence of graphene oxide additions on the microstructure and mechanical strength of cement , 2015 .

[60]  Dan Li,et al.  Mechanical properties and microstructure of a graphene oxide-cement composite , 2015 .

[61]  Dan Li,et al.  Reinforcing effects of graphene oxide on portland cement paste , 2015 .

[62]  O. Sengul Use of electrical resistivity as an indicator for durability , 2014 .

[63]  Sun Ting,et al.  Use of graphene oxide nanosheets to regulate the microstructure of hardened cement paste to increase its strength and toughness , 2014 .

[64]  Yujuan Ma,et al.  Effect of GO nanosheets on shapes of cement hydration crystals and their formation process , 2014 .

[65]  Seulgi Yu,et al.  Bio-based PCM/carbon nanomaterials composites with enhanced thermal conductivity , 2014 .

[66]  Yujuan Ma,et al.  Effect of graphene oxide nanosheets of microstructure and mechanical properties of cement composites , 2013 .

[67]  Ali Nazari,et al.  Modeling the compressive strength of geopolymeric binders by gene expression programming-GEP , 2013, Expert Syst. Appl..

[68]  S. S. Kumar,et al.  An experimental study on cracking evolution in concrete and cement mortar by the b-value analysis of acoustic emission technique , 2012 .

[69]  Amir Hossein Alavi,et al.  Novel Approach to Strength Modeling of Concrete under Triaxial Compression , 2012 .

[70]  Fatih Özcan,et al.  Gene expression programming based formulations for splitting tensile strength of concrete , 2012 .

[71]  A. Gandomi,et al.  Nonlinear Genetic-Based Models for Prediction of Flow Number of Asphalt Mixtures , 2011 .

[72]  Amir Hossein Alavi,et al.  Formulation of flow number of asphalt mixes using a hybrid computational method , 2011 .

[73]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[74]  Abdulkadir Cevik,et al.  Modeling mechanical performance of lightweight concrete containing silica fume exposed to high temperature using genetic programming , 2010 .

[75]  Mustafa Sarıdemir Genetic programming approach for prediction of compressive strength of concretes containing rice husk ash , 2010 .

[76]  Mehmet Gesoğlu,et al.  Empirical modeling of fresh and hardened properties of self-compacting concretes by genetic programming , 2008 .

[77]  Victor C. Li,et al.  Transport Properties of Engineered Cementitious Composites under Chloride Exposure , 2007 .

[78]  Jinping Ou,et al.  Electrode design, measuring method and data acquisition system of carbon fiber cement paste piezoresistive sensors , 2007 .

[79]  Doug Schmucker,et al.  Not As Bad As It Seems: Teaching Probability And Statistics In Civil Engineering , 2004 .

[80]  Simon Smith,et al.  Estimating key characteristics of the concrete delivery and placement process using linear regression analysis , 2003 .

[81]  A. Tropsha,et al.  Beware of q2! , 2002, Journal of molecular graphics & modelling.

[82]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[83]  Christian Fonteix,et al.  Multicriteria optimization using a genetic algorithm for determining a Pareto set , 1996, Int. J. Syst. Sci..

[84]  D. Chung Strain sensors based on the electrical resistance change accompanying the reversible pull-out of conducting short fibers in a less conducting matrix , 1995 .

[85]  John R. Koza,et al.  Genetic programming as a means for programming computers by natural selection , 1994 .

[86]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .