0000000000117142
AUTHOR
Ahmed Salih Mohammed
showing 3 related works from this author
Prediction of concrete materials compressive strength using surrogate models
2022
Using soft computing methods could be of great interest in predicting the compressive strength of Ultra-High-Performance Fibre Reinforced Concrete (UHPFRC). Therefore, this study developed four soft computing techniques. The models are the Linear- relationship (LR), pure quadratic, M5P-tree (M5P), and artificial neural network (ANN). The models were trained and developed using 306 datasets comprising 11 input parameters, including the curing temperature (T), the water-to-cement ratio (w/c), silica fume (SF), cement content (C), fiber content (Fb), water (W), sand content (S), superplasticizer (SP), fiber aspect ratio (AR) and curing time (t). Experimental results were used and compared to t…
Novel fuzzy-based optimization approaches for the prediction of ultimate axial load of circular concrete-filled steel tubes
2021
An accurate estimation of the axial compression capacity of the concrete-filled steel tubular (CFST) column is crucial for ensuring the safety of structures containing them and preventing related failures. In this article, two novel hybrid fuzzy systems (FS) were used to create a new framework for estimating the axial compression capacity of circular CCFST columns. In the hybrid models, differential evolution (DE) and firefly algorithm (FFA) techniques are employed in order to obtain the optimal membership functions of the base FS model. To train the models with the new hybrid techniques, i.e., FS-DE and FS-FFA, a substantial library of 410 experimental tests was compiled from openly availa…
Early prediction of COVID-19 outcome using artificial intelligence techniques and only five laboratory indices
2022
We aimed to develop a prediction model for intensive care unit (ICU) hospitalization of Coronavirus disease-19 (COVID-19) patients using artificial neural networks (ANN). We assessed 25 laboratory parameters at first from 248 consecutive adult COVID-19 patients for database creation, training, and development of ANN models. We developed a new alpha-index to assess association of each parameter with outcome. We used 166 records for training of computational simulations (training), 41 for documentation of computational simulations (validation), and 41 for reliability check of computational simulations (testing). The first five laboratory indices ranked by importance were Neutrophil-to-lymphoc…