id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
azojete-48	Uche, O.A.U. ; Abdulwahab, M.T. ; Suleiman, A. ; Ismail, Y. 	Prediction Modeling of 28-Day Concrete Compressive Strength using Artificial Neutral Network	2019	10	.pdf	application/pdf	3687	160	52	The input variables for the proposed neural network consisted of water cement ratio (w/c), cement content, fine aggregate content, coarse aggregate content and 7th day compressive strength, while the output variable was the 28thday compressive strength. 2.2. Table 2: Results of Compressive Strength at Various Concrete Mix Constituent Concrete Mix Constituent (kg/m3) Compressive Strength (N/mm2) W/C ratio Cement Coarse Aggregate Fine Aggregate 7th Day 28th Day 0.4 360.4 1441.7 720.8 24.9 38.0 0.5 348.8 1395.2 697.6 20.1 30.5 0.6 337.6 1350.6 675.2 16.0 23.7 0.65 332.2 1329.0 664.5 14.3 22.1 Figure 4: Compressive Strength at Various Water-Cement Ratio Figure 4 shows the relationship between compressive strength (N/mm2) at 7th and 28th day with water-cement ratio, this shows an inverse relationship, where the compressive strength decreased with increase in water-cement ratio, hence the compressive strength increased with increase in cement content and decreased with increase in water content.	cache/azojete-48.pdf	txt/azojete-48.txt