Advanced machine learning prediction of the unconfined compressive strength of geopolymer cement reconstituted granular sand for road and liner construction applications
暂无分享,去创建一个
[1] K. Onyelowe,et al. The influence of fines on the hydro-mechanical behavior of sand for sustainable compacted liner and sub-base construction applications , 2023, Asian Journal of Civil Engineering.
[2] A. Kaveh,et al. Efficient training of two ANNs using four meta-heuristic algorithms for predicting the FRP strength , 2023, Structures.
[3] L. Bernardo,et al. Random Forest Algorithm for the Strength Prediction of Geopolymer Stabilized Clayey Soil , 2023, Sustainability.
[4] Denise-Penelope N. Kontoni,et al. Simulation of self-compacting concrete (SCC) passing ability using the L-box model for sustainable buildings , 2022, IOP Conference Series: Earth and Environmental Science.
[5] Denise-Penelope N. Kontoni,et al. Flow simulation of self-consolidating concrete through V-funnel for sustainable buildings , 2022, IOP Conference Series: Earth and Environmental Science.
[6] P. Samui,et al. Evaluation of the Compressive Strength of CFRP-Wrapped Circular Concrete Columns Using Artificial Intelligence Techniques , 2022, Designs.
[7] V. Tran,et al. Novel hybrid machine learning models including support vector machine with meta-heuristic algorithms in predicting unconfined compressive strength of organic soils stabilised with cement and lime , 2022, International Journal of Pavement Engineering.
[8] Ahmed M. Ebid,et al. Global warming potential-based life cycle assessment and optimization of the compressive strength of fly ash-silica fume concrete; environmental impact consideration , 2022, Frontiers in Built Environment.
[9] Yanqi Wu,et al. Splitting tensile strength prediction of sustainable high-performance concrete using machine learning techniques , 2022, Environmental Science and Pollution Research.
[10] Wengang Zhang,et al. Prediction of wall deflection induced by braced excavation in spatially variable soils via convolutional neural network , 2022, Gondwana Research.
[11] Yanqi Wu,et al. Hybrid machine learning model and Shapley additive explanations for compressive strength of sustainable concrete , 2022, Construction and Building Materials.
[12] Xiaochuan Xie,et al. Auto machine learning-based modelling and prediction of excavation-induced tunnel displacement , 2022, Journal of Rock Mechanics and Geotechnical Engineering.
[13] Honggen Chen,et al. Compressive Strength Prediction of High-Strength Concrete Using Long Short-Term Memory and Machine Learning Algorithms , 2022, Buildings.
[14] A. Iravanian,et al. Stress–strain behavior of modified expansive clay soil: experimental measurements and prediction models , 2022, Environmental Earth Sciences.
[15] Fazal E. Jalal,et al. An Investigation on the Behaviour of Geosynthetic Reinforced Quarry Waste Bases (QWB) Under Vertical loading , 2022, Environmental Science and Pollution Research.
[16] A. Mohammed,et al. The role of nanomaterials in geopolymer concrete composites: A state-of-the-art review , 2022, Journal of Building Engineering.
[17] H. Sharif. Fresh and Mechanical Characteristics of Eco-efficient Geopolymer Concrete Incorporating Nano-silica: An Overview , 2021, Kurdistan Journal of Applied Research.
[18] H. Do,et al. Prediction of California Bearing Ratio (CBR) of Stabilized Expansive Soils with Agricultural and Industrial Waste Using Light Gradient Boosting Machine , 2021, Journal of Science and Transport Technology.
[19] A. Mosavi,et al. Survey of Mechanical Properties of Geopolymer Concrete: A Comprehensive Review and Data Analysis , 2021, Materials.
[20] A. Kaveh,et al. Machine learning regression approaches for predicting the ultimate buckling load of variable-stiffness composite cylinders , 2021, Acta Mechanica.
[21] Abdulgazi Gedik,et al. A review on the evaluation of the potential utilization of construction and demolition waste in hot mix asphalt pavements , 2020 .
[22] M. Abdi,et al. Prediction of Enhanced Soil–Anchored Geogrid Interactions in Direct Shear Mode Using Gene Expression Programming , 2020, Geotechnical and Geological Engineering.
[23] S. Sahoo,et al. Rainfall-Induced Slope Failures and Use of Bamboo as a Remedial Measure: A Review , 2020 .
[24] M. Shahin,et al. Strength Characteristics of Clay Stabilized with Fly-ash Based Geopolymer Incorporating Granulated Slag , 2019, Proceedings of the 4th World Congress on Civil, Structural, and Environmental Engineering.
[25] Wan Amizah Wan Jusoh,et al. A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications , 2019, Neural Computing and Applications.
[26] Kejin Wang,et al. A review on properties of fresh and hardened geopolymer mortar , 2018, Composites Part B: Engineering.
[27] M. Kaur,et al. Synthesis of fly ash based geopolymer mortar considering different concentrations and combinations of alkaline activator solution , 2018 .
[28] Mohd Zamin Jumaat,et al. Incorporation of nano-materials in cement composite and geopolymer based paste and mortar – A review , 2017 .
[29] Aminul Islam Laskar,et al. Prediction of unconfined compressive strength of geopolymer stabilized clayey soil using Artificial Neural Network , 2015 .
[30] Peiwen Li,et al. Experimental study of geopolymer mortar with incorporated PCM , 2015 .
[31] E. Allouche,et al. Factors affecting the suitability of fly ash as source material for geopolymers , 2010 .
[32] Jay G. Sanjayan,et al. An investigation of the mechanisms for strength gain or loss of geopolymer mortar after exposure to elevated temperature , 2009 .
[33] Ali Kaveh,et al. Optimal Design of Transmission Towers Using Genetic Algorithm and Neural Networks , 2008 .
[34] Ali Kaveh,et al. Comparative Study of Backpropagation and Improved Counterpropagation Neural Nets in Structural Analysis and Optimization , 1998 .
[35] Danial Jahed Armaghani,et al. An efficient optimal neural network based on gravitational search algorithm in predicting the deformation of geogrid-reinforced soil structures , 2021, Transportation Geotechnics.
[36] Pengcheng Jiao,et al. New prediction models for unconfined compressive strength of geopolymer stabilized soil using multi-gen genetic programming , 2018 .