Compressive strength of natural hydraulic lime mortars using soft computing techniques

[1]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[2]  Antonia Moropoulou,et al.  Physico-chemical study of Cretan ancient mortars , 2003 .

[3]  Serhan Ozdemir,et al.  The use of GA-ANNs in the modelling of compressive strength of cement mortar , 2003 .

[4]  J. Lanas,et al.  MECHANICAL PROPERTIES OF NATURAL HYDRAULIC LIME-BASED MORTARS , 2004 .

[5]  Ramesh Sharda,et al.  Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks. , 2006, Accident; analysis and prevention.

[6]  Cenk Karakurt,et al.  Predicting the strength development of cements produced with different pozzolans by neural network and fuzzy logic , 2008 .

[7]  Okan Karahan,et al.  Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network , 2009, Adv. Eng. Softw..

[8]  Mustafa Saridemir,et al.  Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic , 2009, Adv. Eng. Softw..

[9]  Paulo Cachim,et al.  Hydraulic-lime based concrete: Strength development using a pozzolanic addition and different curing conditions , 2009 .

[10]  Okan Karahan,et al.  Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete , 2009, Adv. Eng. Softw..

[11]  Aminaton Marto,et al.  Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization , 2014, Arabian Journal of Geosciences.

[12]  C. Poon,et al.  Prediction of compressive strength of recycled aggregate concrete using artificial neural networks , 2013 .

[13]  K. Paine,et al.  Compressive strength development of binary and ternary lime–pozzolan mortars , 2013 .

[14]  B. Silva,et al.  Influence of natural hydraulic lime content on the properties of aerial lime-based mortars , 2014 .

[15]  P. G. Asteris,et al.  Modeling of masonry failure surface under biaxial compressive stress using Neural Networks , 2014 .

[16]  I. Karatasios,et al.  The effect of aggregate size and type of binder on microstructure and mechanical properties of NHL mortars , 2014 .

[17]  C. Varin,et al.  Statistical analysis of the physical properties and durability of water-repellent mortars made with limestone cement, natural hydraulic lime and pozzolana-lime , 2015 .

[18]  J. S. Pozo-Antonio.  Evolution of mechanical properties and drying shrinkage in lime-based and lime cement-based mortars with pure limestone aggregate , 2015 .

[19]  Dimitris G. Giovanis,et al.  Spectral representation-based neural network assisted stochastic structural mechanics , 2015 .

[20]  Chemical and physical characterisation of some NHL binders and the correlation with the mechanical properties of conservation mortars , 2015 .

[21]  G. Cultrone,et al.  Mineralogical, textural and physical-mechanical study of hydraulic lime mortars cured under different moisture conditions , 2015 .

[22]  Augusto Gomes,et al.  Natural hydraulic lime versus cement for blended lime mortars for restoration works , 2015 .

[23]  Bond-strength performance of hydraulic lime and natural cement mortared sandstone masonry , 2015 .

[24]  Mehdi Nikoo,et al.  Flood-routing modeling with neural network optimized by social-based algorithm , 2016, Natural Hazards.

[25]  Indra Prakash,et al.  Landslide Hazard Assessment Using Random SubSpace Fuzzy Rules Based Classifier Ensemble and Probability Analysis of Rainfall Data: A Case Study at Mu Cang Chai District, Yen Bai Province (Viet Nam) , 2017, Journal of the Indian Society of Remote Sensing.

[26]  Danial Jahed Armaghani,et al.  Prediction of the durability of limestone aggregates using computational techniques , 2016, Neural Computing and Applications.

[27]  V. Cnudde,et al.  Pore-related properties of natural hydraulic lime mortars: an experimental study , 2016 .

[28]  Panagiotis G. Asteris,et al.  Anisotropic masonry failure criterion using artificial neural networks , 2017, Neural Computing and Applications.

[29]  R. Ball,et al.  Chemical and physical characterisation of three NHL 2 binders and the relationship with the mortar properties , 2016 .

[30]  Panagiotis G. Asteris,et al.  Prediction of self-compacting concrete strength using artificial neural networks , 2016 .

[31]  Liborio Cavaleri,et al.  Prediction of the Fundamental Period of Infilled RC Frame Structures Using Artificial Neural Networks , 2015, Comput. Intell. Neurosci..

[32]  Khabat Khosravi,et al.  Application and Comparison of Decision Tree-Based Machine Learning Methods in Landside Susceptibility Assessment at Pauri Garhwal Area, Uttarakhand, India , 2017, Environmental Processes.

[33]  Effect of Aggregate Type on Properties of Natural Hydraulic Lime-Based Mortars , 2017 .

[34]  Panagiotis G. Asteris,et al.  A methodological approach for the selection of compatible and performable restoration mortars in seismic hazard areas , 2017 .

[35]  Ji-Whan Ahn,et al.  Performance improvement of local Korean natural hydraulic lime-based mortar using inorganic by-products , 2017, Korean Journal of Chemical Engineering.

[36]  M. Amenta,et al.  The role of aggregate characteristics on the performance optimization of high hydraulicity restoration mortars , 2017 .

[37]  Danial Jahed Armaghani,et al.  Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition , 2017 .

[38]  Panagiotis G. Asteris,et al.  Feed-Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials , 2017, Sensors.

[39]  B. Pham,et al.  A comparative study of sequential minimal optimization-based support vector machines, vote feature intervals, and logistic regression in landslide susceptibility assessment using GIS , 2017, Environmental Earth Sciences.

[40]  Hamid Eskandari-Naddaf,et al.  ANN prediction of cement mortar compressive strength, influence of cement strength class , 2017 .

[41]  Mehdi Nikoo,et al.  Determination of Damage in Reinforced Concrete Frames with Shear Walls Using Self-Organizing Feature Map , 2017, Appl. Comput. Intell. Soft Comput..

[42]  Liborio Cavaleri,et al.  Modeling of surface roughness in electro-discharge machining using artificial neural networks , 2017 .

[43]  Panagiotis G. Asteris,et al.  Self-compacting concrete strength prediction using surrogate models , 2017, Neural Computing and Applications.

[44]  Binh Thai Pham,et al.  Machine Learning Methods of Kernel Logistic Regression and Classification and Regression Trees for Landslide Susceptibility Assessment at Part of Himalayan Area, India , 2018 .

[45]  Marijana Hadzima-Nyarko,et al.  Determining the Natural Frequency of Cantilever Beams Using ANN and Heuristic Search , 2018, Appl. Artif. Intell..

[46]  J. J. Ortega,et al.  The effects of dosage and production process on the mechanical and physical properties of natural hydraulic lime mortars , 2018 .

[47]  Maria Apostolopoulou,et al.  Study of the historical mortars of the Holy Aedicule as a basis for the design, application and assessment of repair mortars: A multispectral approach applied on the Holy Aedicule , 2018 .

[48]  Dongmin Wang,et al.  Comparative study on the properties of three hydraulic lime mortar systems: Natural hydraulic lime mortar, cement-aerial lime-based mortar and slag-aerial lime-based mortar , 2018, Construction and Building Materials.

[49]  Natural Hydraulic Lime Mortars: Influence of the Aggregates , 2018, Historic Mortars.

[50]  D. Bui,et al.  A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling: A case study in the Himalayan area, India , 2017, International Journal of Sediment Research.

[51]  Panagiotis G. Asteris,et al.  Surface treatment of tool steels against galling failure , 2018 .

[52]  Liborio Cavaleri,et al.  Krill herd algorithm-based neural network in structural seismic reliability evaluation , 2019 .

[53]  Panagiotis G. Asteris,et al.  Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures , 2019, Neural Computing and Applications.

[54]  Danial Jahed Armaghani,et al.  Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions , 2019, Soft Comput..

[55]  B. Pham,et al.  Evaluation and comparison of LogitBoost Ensemble, Fisher’s Linear Discriminant Analysis, logistic regression and support vector machines methods for landslide susceptibility mapping , 2019 .

[56]  Hui Chen,et al.  Assessing Dynamic Conditions of the Retaining Wall: Developing Two Hybrid Intelligent Models , 2019, Applied Sciences.