In this study, an experimental investigation had conducted for six high strength laced reinforced concrete one-way slabs to discover the behavior of laced structural members after being exposed to fire flame (high temperature). Self-compacted concrete (SCC) had used to achieve easy casting and high strength concrete. All the adopted specimens were identical in their compressive strength of ( , geometric layout 2000 750 150 mm and reinforcement specifics except those of lacing steel content, three ratios of laced steel reinforcement of (0.0021, 0.0040 and 0.0060) were adopted. Three specimens were fired with a steady state temperature of  for two hours duration and then after the specimens were cooled suddenly by spraying water. The

Fiber Reinforced Polymer (FRP) bars are anisotropic in nature and have high tensile strength in the fiber direction. The use of High-Strength Concrete (HSC) allows for better use of the high-strength properties of FRP bars. The mechanical properties of FRP bars can yield to large crack widths and deflections. As a result, the design of concrete elements reinforced with FRP materials is often governed by the Serviceability Limit States (SLS). This study investigates the short-term serviceability behavior of FRP RC I-beams. Eight RC I-beams reinforced with carbon-FRP (CFRP) and four steel RC I-beams, for comparison purposes, were tested under two-point loading.
Deformations on the concrete and crack widths and spacing are measured and

An extensive program of laboratory testing was conducted on ring footing rested on gypseous soil brought from the north of Iraq (Salah El-Deen governorate) with a gypsum content of 59%. There are limited researches available, and even fewer have been done experimentally to understand how to ring footings behave; almost all the previous works only concern the behavior of ring footing under vertical loads, Moreover, relatively few studies have examined the impact of eccentric load and inclined load on such footing. In this study, a series of tests, including dry and wet tests, were carried out using a steel container (600×600×600) mm, metal ring footing (100 mm outer diameter and 40 mm inner diameter) was placed in the m

Two new nonsymmetrical mesogenic homologous series of terminal substituent ether (series [Vn]) and carboxy (series [VIn]) incorporating azobenzene and 1,3,4-oxadiazole group were synthesized. Both series have been All compounds thus isolated were purified and characterized by elemental analysis, Fourier Transform Infrared Spectroscopy, 1H NMR, along with thermal analysis and texture observation using Differential Scanning Calorimetry (DSC) and Polarizing Optical Microscopy (POM), respectively. All compounds of the first series exhibited liquid crystalline properties. The homologues [V1]-[V3] display a nematic mesophase, the compounds [V4]-[V7] exhibit a dimorphism behavior, nematic (N) and smectic A (SmA) mesophases, the compounds [V8] and

This study was undertaken to diagnose routine settling problems within a third-party oil and gas companies’ Mono-Ethylene Glycol (MEG) regeneration system. Two primary issues were identified including; a) low particle size (<40 μm) resulting in poor settlement within high viscosity MEG solution and b) exposure to hydrocarbon condensate causing modification of particle surface properties through oil-wetting of the particle surface. Analysis of oil-wetted quartz and iron carbonate (FeCO₃) settlement behavior found a greater tendency to remain suspended in the solution and be removed in the rich MEG effluent stream or to strongly float and accumulate at the liquid-vapor interface in comparison to naturally water-wetted particles. As su

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod