The determination of ground granulated concrete compressive strength based machine learning models

The advancement of machine learning (ML) models has received remarkable attention by several science and engineering applications. Within the material engineering, ML models are usually utilized for building an expert system for supporting material design and attaining an optimal formulation material sustainability and maintenance. The current study is conducted on the based of the utilization of ML models for modeling compressive strength (Cs) of ground granulated blast furnace slag concrete (GGBFSC). Random Forest (RF) model is developed for this purpose. The predictive model is constructed based on multiple correlated properties for the concrete material including coarse aggregate (CA), curing temperature (T), GGBFSC to total binder ratio (GGBFSC/B), water to binder ratio (w/b), water content (W), fine aggregate (FA), superplasticizer (SP). A total of 268 experimental dataset are gather form the open-source previous published researches, are used to build the predictive model. For the verification purpose, a predominant ML model called support vector machine (SVM) is developed. The efficiency of the proposed predictive and the benchmark models is evaluated using statistical formulations and graphical presentation. Based on the attained prediction accuracy, RF model demonstrated an excellent performance for predicting the Cs using limited input parameters. Overall, the proposed methodology showed an exceptional predictive model that can be utilized for modeling compressive strength of GGBFSC

Reinforced concrete confinement coefficient estimation using soft computing models

Infrastructure vulnerability toward seismic lateral loading within high seismicity has received massive attention by structural engineers and designers. This is for the purpose to provide a reliable alternative material that strengthening the bending and shear of slabs, columns and reinforced concrete (RC). Despite the utilized approaches of strengthening the concrete structure based on fiber reinforced polymers (FRP) is considerably new technique, exploring more reliable and robust methodologies is the motive of scholars for better structural behaviour understanding. In the current research, two soft computing models called artificial neural network (ANN) and support vector regression (SVR) are applied to predict lateral confinement coefficient (Ks). The models are developed based on gathered dataset from open source researches for the lateral confinement coefficient of columns wrapped with carbon FRP (CFRP) and their corresponding parameters including column width, length and thickness (b, h and t mm), column radius (r mm), compressive strength of concrete (f_c^') and elastic modulus (EFRP). Results indicated the superiority of the ANN model for predicting Ks over the SVR model. The application of the soft computing showed an optimistic approach for the structural lateral confinement coefficient determination

Flexural behavior of one-way ferrocement slabs with fibrous cementitious matrices

Concrete compressive strength enhancement is considered as one of the popular topics in the field of civil engineering. It has received a massive attention by material and structural engineers over the past decades. The aim of this study is to investigate thin mortar matrix for the impacts of the combination of reinforcing steel meshes with discontinuous fibers, and to do this, one-way Ferrocement slabs were tested under bending with steel fibers and meshes, focusing more on the number of mesh layers (1, 2, & 3) as the studied parameter. The percentages of fiber content as volumetric ratio 0.25, 0.5 and 0.75 and type of fibers golden steel fibers and waste aluminum fibers from waste metallic cans. Results showed that at general the adding of fibers regardless of its type increased the ductility of tested slabs. In addition, results showed that steel fibers are more effective than aluminum fibers

Pd(PPh3)4 Catalyzed Synthesis of Indazole Derivatives as Potent Anticancer Drug

A series of 3-aryl indazoles and 1-methyl-3-aryl indazole derivatives are prepared with exceptional yields by coupling with several arylboronic acids and methylation by two dissimilar approaches. The as-prepared indazole derivatives (3a–3j) and their N-methyl derivatives (5a–5j) are evaluated for in vitro anticancer activity against two cancer cell lines, HCT-116 and MDA-MB-231. The results reveal that the indazole derivatives tested display mild to moderate anticancer activities against the cell lines tested