Industrial buildings usually are designed to sustain several types of load systems, such as dead, live, and dynamic loads (especially the harmonic load produced by rotary motors). In general, these buildings require high-strength structural elements to carry the applied loads. Moreover, Reactive Powder Concrete (RPC) has been used for this purpose because of its excellent mechanical strength and endurance. Therefore, this study provides an experimental analysis of the structural behaviors of reinforced RPC beams under harmonic loads. The experimental program consisted of testing six simply supported RPC beams with lengths of 1500 mm, widths of 150 mm, and thicknesses of 200 mm under harmonic loading with varied frequencies between 1

It is suitable to use precast steel-concrete composite beams to quickly assemble a bridge or a building, particularly in isolated regions where cast-in-situ concrete is not a practical option. If steel-concrete composite beams are designed to allow demountability, they can also be extremely useful in the aftermath of natural disasters, such as earthquakes or flooding, to replace damaged infrastructure. Furthermore, rapid replacement of slabs is extremely beneficial in case of severe deterioration due to long-term stressors such as fatigue or corrosion. The only way to rapidly assemble and disassemble a steel-concrete composite structure is to use demountable shear connectors to connect/disconnect the steel beams to/from the concrete slab. I

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.

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.

An analytical method and a two-dimensional finite element model for treating the problem of laser heating and melting has been applied to aluminum 2519T87and stainless steel 304. The time needed to melt and vaporize and the effects of laser power density on the melt depth for two metals are also obtained. In addition, the depth profile and time evolution of the temperature before melting and after melting are given, in which a discontinuity in the temperature gradient is obviously observed due to the latent heat of fusion and the increment in thermal conductivity in solid phase. The analytical results that induced by  laser irradiation is in good agreement with numerical results.