Roller compacted concrete (RCC) is a concrete of no slump, no reinforcement, no finishing, and compacted using vibratory roller. When compared with conventional concrete, it contains less water content
when compared to traditional concrete. The RCC technique achieves significant time and cost savings during the construction of concrete. This study demonstrates the preparation of RCC slab of (38 ×38× 10) cm
samples by using roller compactor which is manufactured in local markets. The Hydrated lime additive is used to study the mechanical and physical properties of that RCC slab samples. This investigation is divided
into two main stages: The First stage consists of hammer compaction method with two gradation of aggregate, dense

Refractories are mineral and chemical-, based, materials with excellent heat resistance, making them ideal for use in the construction of ovens, furnace walls industries. According to this our research is concerned to study the effect of addition of (4% CaO) and (5% graphite) on the silica brick properties. Different amounts of CaO and Graphite were included in the white sand (raw ingredients) of silica bricks as a binder to prepare the composition then the composition were sintered using Different sintering temperatures ranging from (1000–1400)𝛐C under static air. Density, thermal conductivity, porosity, and water absorption Compression there was power tested after sintering. XRD analysis was used to identify raw materials’

To achieve sustainability, use waste materials to make concrete to use alternative components and reduce the production of Portland cement. Lime cement was used instead of Portland cement, and 15% of the cement's weight was replaced with silica fume. Also used were eco-friendly fibers (copper fiber) made from recycled electrical. This work examines the impact of utilizing sustainable copper fiber with different aspect ratios (l/d) on some mechanical properties of high-strength green concrete. A high-strength cement mixture with a compressive strength of 65 MPa in line with ACI 211.4R was required to complete the assignment. Copper fibers of 1% by volume of concrete were employed in mixes with four different aspect ratios

Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twentyfour samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.

A Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twentyfour samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.

A Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twenty four samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.