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Evaluating Concrete Strength Under Various Curing Conditions Using Artificial Neural Networks
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Abstract<p>This study examines the impact of different curing methods on the compressive strength of concrete. It investigates techniques such as air curing, periodic water spraying, full water submersion, and polyethylene encasement. Artificial neural network models were employed to evaluate the compressive strength under each curing condition. A model for calculating compressive strength that considers surrounding conditions was created using an artificial neural network. The current study’s figures were generated using this model. The research thoroughly examined the impact of curing environments and concrete mix components on strength properties, taking into account factors such as temperature, the inclusion of additives such as fly ash and silica fume, adjustments in water-to-cement ratio, selection of aggregates, and the integration of various admixtures. One important discovery is that models that predict compressive strength based on 28-day water immersion do not accurately represent the actual strength because of the substantial impact of local curing conditions. Furthermore, concrete that was cured in polyethylene bags exhibited noticeable differences in moisture retention and temperature properties when compared to alternative methods. Understanding and evaluating curing conditions is crucial for accurate strength predictions. The study also found that compressive strength decreases with temperatures above 30°C and below 15°C.</p>
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Publication Date
Fri Jun 30 2006
Journal Name
Tikrit Journal Of Engineering Sciences
Curvature Ductility of Reinforced Concrete Column Sections Under Different strain Rates
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This paper presents theoretical parametric study of the curvature ductility capacity for reinforced concrete column sections. The study considers the behavior of concrete and reinforcing steel under different strain rates. A computer program has been written to compute the curvature ductility taking into account the spalling in concrete cover. Strain rate sensitive constitutive models of steel and concrete were used for predicting the moment-curvature relationship of reinforced concrete columns at different rate of straining. The study parameters are the yield strength of main reinforcement, yield strength of transverse reinforcement, compressive strength of concrete, spacing of ties and the axial load. The results indicated that hi

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Publication Date
Thu Jun 25 2026
Journal Name
Engineering And Technology
Structural Performance of Reinforced Concrete Beams under Impact Loading: A Review
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This study presented a summarized review investigation about the influence of the impact force exerted on reinforced concrete (RC) beams. Different aspects were considered and highlighted to achieve the main goal of this study. First of all, measuring the impact force generated by a drop in mass was discussed, focusing on how various factors influence the magnitude of the force. Then, after the dynamic equilibrium and shear dominance in the Impact test were deeply and widely discussed. Moreover, the Impact and high-rate loading effect on the reinforced concrete beams was widely investigated. The influence of steel fiber on the dynamic response of reinforced concrete beams affected by impact loading was also included in this study. The findi

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Publication Date
Tue Jan 15 2002
Journal Name
Abhath Al- Yarmouk [basic Sciences And Engineering]
Computer Program for Predicting Ultimate Strength of Structural Concrete Sections of General Shape
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Publication Date
Tue Jan 19 2021
Journal Name
Sn Applied Sciences
Post-fire serviceability and residual strength of composite post-tensioned concrete T-beams
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Abstract<p>In this study, the response of ten composite post-tensioned concrete beams topped by a reinforced concrete deck with adequate reinforcing shear connectors is investigated. Depending on the concrete compressive strength of the deck slab (20, 30, and 40 MPa), beams are grouped into three categories. Seven of these beams are exposed to a fire attack of 700 and 800 °C temperature simultaneously with or without the presence of a uniformly distributed sustained static loading. After cooling back to ambient temperature, these composite beams are loaded up to failure, using a force control module, by monotonic static loading in a four-point-bending setup with two symmetrical concentrated loads applied in </p> ... Show More
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Publication Date
Tue Jan 19 2021
Journal Name
Sn Applied Sciences
Post-fire serviceability and residual strength of composite post-tensioned concrete T-beams
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Abstract<p>In this study, the response of ten composite post-tensioned concrete beams topped by a reinforced concrete deck with adequate reinforcing shear connectors is investigated. Depending on the concrete compressive strength of the deck slab (20, 30, and 40 MPa), beams are grouped into three categories. Seven of these beams are exposed to a fire attack of 700 and 800 °C temperature simultaneously with or without the presence of a uniformly distributed sustained static loading. After cooling back to ambient temperature, these composite beams are loaded up to failure, using a force control module, by monotonic static loading in a four-point-bending setup with two symmetrical concentrated loads applied in </p> ... Show More
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Publication Date
Wed Jul 01 2020
Journal Name
Journal Of Engineering
Evaluating the Uses of Concrete Demolishing Waste in improving the Geotechnical Properties of Expansive Soil
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Expansive soil is one of the most serious problems that face engineers during the execution of any infrastructure projects. Soil stabilization using chemical admixture is one of the most traditional and widespread methods of soil improvement. Nevertheless, soil improvement on site is one of the most economical solutions for many engineering applications. Using construction and demolishing waste in soil stabilization is still under research., The aim of this study is to identify the effect of using concrete demolishing waste (CDW) in soil stabilization. Serious tests were conducted to investigate the changes in the geotechnical properties of the natural soil stabilized with CDW. From the results, it is concluded that the

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Publication Date
Sun Nov 01 2015
Journal Name
Journal Of Engineering
A Spike Neural Controller for Traffic Load Parameter with Priority-Based Rate in Wireless Multimedia Sensor Networks
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Wireless Multimedia Sensor Networks (WMSNs) are a type of sensor network that contains sensor nodes equipped with cameras, microphones; therefore the WMSNS are able to   produce multimedia data such as video and audio streams, still images, and scalar data from the surrounding environment. Most multimedia applications typically produce huge volumes of data, this leads to congestion. To address this challenge, This paper proposes Modify Spike Neural Network control for Traffic Load Parameter with Exponential Weight of Priority Based Rate Control algorithm (MSNTLP with EWBPRC). The Modify Spike Neural Network controller (MSNC) can calculate the appropriate traffi

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Publication Date
Mon Jan 01 2024
Journal Name
Ieee Access
Transfer Learning and Hybrid Deep Convolutional Neural Networks Models for Autism Spectrum Disorder Classification From EEG Signals
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Publication Date
Mon Jun 19 2023
Journal Name
Journal Of Engineering
Data Classification using Quantum Neural Network
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In this paper, integrated quantum neural network (QNN), which is a class of feedforward

neural networks (FFNN’s), is performed through emerging quantum computing (QC) with artificial neural network(ANN) classifier. It is used in data classification technique, and here iris flower data is used as a classification signals. For this purpose independent component analysis (ICA) is used as a feature extraction technique after normalization of these signals, the architecture of (QNN’s) has inherently built in fuzzy, hidden units of these networks (QNN’s) to develop quantized representations of sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that

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Publication Date
Sun Jun 06 2010
Journal Name
Baghdad Science Journal
Using Neural Network with Speaker Applications
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In Automatic Speech Recognition (ASR) the non-linear data projection provided by a one hidden layer Multilayer Perceptron (MLP), trained to recognize phonemes, and has previous experiments to provide feature enhancement substantially increased ASR performance, especially in noise. Previous attempts to apply an analogous approach to speaker identification have not succeeded in improving performance, except by combining MLP processed features with other features. We present test results for the TIMIT database which show that the advantage of MLP preprocessing for open set speaker identification increases with the number of speakers used to train the MLP and that improved identification is obtained as this number increases beyond sixty.

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