Embedded Files
Prediction of Elastic Modulus of Recycled Aggregate Concrete
This Graphical User Interface (GUI), which is designed in MATLAB environment, would allow civil engineers to predict the elastic modulus of recycled aggregate concrete from its mixture proportions and components characteristics. The required input variables for this purpose include: (1) water-cement ratio (w/c), (2) coarse aggregate cement ratio (CA/C), (3) volume replacement of NA by recycled aggregate RA (r), (4) fine aggregate-total aggregate ratio (FA/TA), (5) saturated surface dry specific gravity (SGSSD), (6) water absorption (Wa) of the mixed (NA+RA) coarse aggregates, and (7) 28-day cube compressive strength (f'c) of the mixture.
The file is downloadable from HERE
After downloading the file, find the file named "main.m" and run it in MATLAB environment.
Developed by Golafshani and Behnood
Related Papers:
Mohammadi-Golafshani, E, Behnood, A., 2018. Automatic regression methods for formulation of elastic modulus of recycled aggregate concrete. Applied Soft Computing 64, 377-400. https://doi.org/10.1016/j.asoc.2017.12.030
Mohammadi-Golafshani, E, Behnood, A., 2018. Application of soft computing methods for predicting the elastic modulus of recycled aggregate concrete. Cleaner Production 176, 1163-1176. https://doi.org/10.1016/j.jclepro.2017.11.186
Behnood, A., Olek, J., Glinicki, M.A., 2015. Predicting modulus elasticity of recycled aggregate concrete using M5′ model tree algorithm. Construction and Building Materials 94, 137-147. http://dx.doi.org/10.1016/j.conbuildmat.2015.06.055
Optimal Mix Design of Silica Fume Concrete
This Graphical User Interface (GUI) is designed to determine the optimal mix design of silica fume concrete. It allows the users to input the unit prices and specific gravities of the mixture components in the "Concrete mix properties" area. Then, after setting the values of required compressive strength and air volume, the amounts of different components for the optimized mix design will be calculated.
The file is downloadable from HERE
After downloading the file, find the file named "ConcreteMixDesignGUI.m" and run it in MATLAB environement.
Developed by Golafshani and Behnood
Related Papers:
Behnood, A., Mohammadi-Golafshani, E., 2018 Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves. Journal of Cleaner Production 202, 54-64. https://doi.org/10.1016/j.jclepro.2018.08.065
Mohammadi-Golafshani, E., Behnood, A. 2019. Estimating the optimal mix design of silica fume concrete using biogeography-based programming. Cement and Concrete Composites 96, 95-105.
https://doi.org/10.1016/j.cemconcomp.2018.11.005
Dynamic modulus of asphalt mixture
This Graphical User Interface (GUI) is designed to determine the dynamic modulus of asphlt mixture. The designed GUI uses volumetric properties, aggregate characteristics, and test conditions for the estimation of the dynamic modulus of asphalt mixtures.
The file is downloadable from HERE
please find the file named "DynamicModulusAsphalt.m" and run it in Matlab environment.
Developed by Golafhsani and Behnood
Related Papers:
Mohammadi-Golafshani, E., Behnood, A., 2021. Karimi, M. M. Predicting the dynamic modulus of asphalt mixture using hybridized artificial neural network and grey wolf optimizer. International Journal of Pavement Engineering. https://doi.org/10.1080/10298436.2021.2005056
Daneshvar, D., Behnood, A., 2021. Estimating the dynamic modulus of asphalt concretes using the random forests classification method. International Journal of Pavement Engineering. https://doi.org/10.1080/10298436.2020.1741587
Daneshvar, D., Behnood, A., 2020. A machine learning study of the dynamic modulus of asphalt concretes: An application of M5P model tree algorithm, Construction and Building Materials, 262, 120544. https://doi.org/10.1016/j.conbuildmat.2020.120544
Behnood, A. Mohammadi Golafshani, E. 2021. Predicting the dynamic modulus of asphalt mixture using machine learning techniques: An application of multi biogeography-based programming, Construction and Building Materials, 266 (Part A) 120983. https://doi.org/10.1016/j.conbuildmat.2020.120983
Resilient Modulus of Non-Cohesive Subgrade Soils and Unbound Subbase Materials
This Graphical User Interface (GUI) is designed to estimate the resilient modulus of non-cohesive subgrade soils and unbound subbase materials using a hybrid support vector machine (SVM) and colliding body optimization (CBO) algorithm.
Related Paper:
Heidarabadizadeh, N. Ghanizadeh, A. R., Behnood, A., 2021. Prediction of the resilient modulus of non-cohesive subgrade soils and unbound subbase materials using a hybrid support vector machine method and colliding bodies optimization algorithm, Construction and Building Materials, 275, 122140. https://doi.org/10.1016/j.conbuildmat.2020.122140
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