scholarly journals Using Multivariate Regression and ANN Models to Predict Properties of Concrete Cured under Hot Weather: A Case of Rawalpindi Pakistan

2021 ◽  
Vol 13 (18) ◽  
pp. 10164
Author(s):  
Ahsen Maqsoom ◽  
Bilal Aslam ◽  
Muhammad Ehtisham Gul ◽  
Fahim Ullah ◽  
Abbas Z. Kouzani ◽  
...  
Keyword(s):  
Construction Material ◽  
Pulse Velocity ◽  
Ann Model ◽  
Quadratic Regression ◽  
Split Tensile Strength ◽  
Curing Conditions ◽  
Curing Methods ◽  
Ann Models ◽  
Hot Weather ◽  
The Individual

Concrete is an important construction material. Its characteristics depend on the environmental conditions, construction methods, and mix factors. Working with concrete is particularly tricky in a hot climate. This study predicts the properties of concrete in hot conditions using the case study of Rawalpindi, Pakistan. In this research, variable casting temperatures, design factors, and curing conditions are investigated for their effects on concrete characteristics. For this purpose, water–cement ratio (w/c), in-situ concrete temperature (T), and curing methods of the concrete are varied, and their effects on pulse velocity (PV), compressive strength (fc), depth of water penetration (WP), and split tensile strength (ft) were studied for up to 180 days. Quadratic regression and artificial neural network (ANN) models have been formulated to forecast the properties of concrete in the current study. The results show that T, curing period, and moist curing strongly influence fc, ft, and PV, while WP is adversely affected by T and moist curing. The ANN model shows better results compared to the quadratic regression model. Furthermore, a combined ANN model of fc, ft, and PV was also developed that displayed higher accuracy than the individual ANN models. These models can help construction site engineers select the appropriate concrete parameters when concreting under hot climates to produce durable and long-lasting concrete.

Hybrid bacteria mediated cemented sand: Microcharacterization, permeability, strength, shear wave velocity, stress-strain, and durability

2021 ◽  
pp. 105678952199119
Author(s):  
Meghna Sharma ◽  
Neelima Satyam ◽  
Krishna R Reddy
Keyword(s):  
Synergistic Effects ◽  
Ultrasonic Pulse Velocity ◽  
Ultrasonic Pulse ◽  
Pulse Velocity ◽  
Engineering Properties ◽  
Calcite Precipitation ◽  
Stress Strain ◽  
Split Tensile Strength ◽  
Cemented Sand ◽  
The Individual

Microbially induced calcite precipitation (MICP), a sustainable approach for sand biocementation, was investigated in previous studies based on metabolic activity of individual microorganisms. The individual bacteria, specifically Sporosarcina pasteurii (SP), Bacillus subtilis (BS), and Lysinibacillus sphaericus (LS), were found capable enough for sand biocementation. However, present study investigates synergistic effects of using bacterial-hybrids on cementation and consequent improvement in sand properties. The SP, BS, and LS strains were used in different combinations to create bacterial-hybrids and applied under simulated non-sterile field conditions. Initially, sand biotreatment was carried out in plastic tubes up to 14 days, using bacterial mixtures and 0.5 M cementation solution. Biocemented specimens were tested for calcite precipitation, XRD, FTIR, and SEM. The SP and LS combination (SPLS hybrid) showed maximum calcite precipitation, which is further used for biotreatment to create cylindrical sand samples for testing improved engineering properties. These samples were prepared using 0.5 M cementation solution in three pore volumes (1, 0.75, and 0.5 PV) and treatment cycles (12, 24, and 48 hrs TC) up to 18 days. Biocemented samples were tested for permeability (6th, 12th, and 18th days of biotreatment), unconfined compressive strength (UCS), split tensile strength (STS), ultrasonic pulse velocity (UPV), and consolidated undrained stress-strain response. Durability of biocementation was also investigated by determining reduction in strength and UPV subjected to freeze-thaw (FT) cycles (5, 10, 15, and 20). The results showed maximum UCS of 1902 kPa, STS of 356 kPa, UPV of 2408 m/s, and coefficient of permeability reduction up to 91%. The higher results were achieved with 11.11% calcite content in 1PV-12TC treated samples. The 1PV-12TC treated samples resulted in 4.2%, 8.3%, 17%, and 35% reduction of strength after 5, 10, 15, and 20 FT cycles, respectively. Overall, biocementation using hybrid bacteria is shown significant to improve sand's engineering properties, including potential to mitigate liquefaction.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

scholarly journals Kaolinite Claystone-Based Geopolymer Materials: Effect of Chemical Composition and Curing Conditions

Minerals ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 444 ◽  
Author(s):  
Pavlína Hájková
Keyword(s):  
Chemical Composition ◽  
Water Glass ◽  
Chemical Elements ◽  
Solidification Process ◽  
Ease Of Use ◽  
Raw Material ◽  
Curing Conditions ◽  
Low Viscosity ◽  
Lower Porosity ◽  
Curing Methods

This work describes the role of chemical composition and curing conditions in geopolymer strength, leachability of chemical elements and porosity. The study focuses on geopolymer material prepared from calcined kaolinite claystone, which is not studied frequently as a raw material for geopolymer production, although it has a high application potential as it is easily commercially available and allows preparation of geopolymers with low viscosity. The composition of geopolymers and their curing methods were selected considering their ease of use in the praxis. Therefore, the potassium water glass itself was used as alkali activator without any KOH or NaOH addition. Chemical composition was changed only by the density of water glass in the range of 1.2 to 1.6 g·cm−3. Geopolymers were cured at a temperature within the range of 5 °C–70 °C to speed up the solidification process as well as by microwave radiation. High compressive strengths were obtained for geopolymers with the highest densities of the water glass (1.5 and 1.6 g·cm−3) in dependence on various curing conditions. Higher strengths were achieved in the case of samples where the solidification was not accelerated. The samples cured at lower temperatures (5 °C) showed lower porosity compared to the other curing types. The lowest leachability of Si and alkalis was reached for the samples with water glass density 1.5 g·cm−3.

scholarly journals Study on Mix Proportion Optimization of Manufactured Sand RPC and Design Method of Steel Fiber Content under Different Curing Methods

Materials ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 1845 ◽  
Author(s):  
Chunling Zhong ◽  
Mo Liu ◽  
Yunlong Zhang ◽  
Jing Wang
Keyword(s):  
Steel Fiber ◽  
Design Method ◽  
Fiber Content ◽  
Orthogonal Experimental Design ◽  
Curing Conditions ◽  
Practical Applications ◽  
Manufactured Sand ◽  
Binder Ratio ◽  
Curing Methods ◽  
Reactive Powder

This study investigated four factors (water/binder ratio, silica fume, fly ash, and sand/binder ratio) using the orthogonal experimental design method to prepare the mix proportions of a manufactured sand reactive powder concrete (RPC) matrix to determine the optimal matrix mix proportions. On this basis, we assessed the compressive and splitting tensile strengths of different steel fiber contents under natural, standard, and compound curing conditions to develop an economical and reasonable RPC for various engineering requirements. A calculation method for the RPC strength of the steel fiber contents was evaluated. The results showed that the optimum steel fiber content for manufactured sand RPC is 4% under natural, standard, and compound curing conditions. Compared with standard curing, compound curing can improve the early strength of manufactured sand RPC but only has a small effect on the enhancement of late strength. Although the strength of natural curing is slightly lower than that of standard curing, it basically meets project requirements and is beneficial for practical applications. The calculation formula of 28-day compressive and splitting tensile strengths of manufactured sand RPC steel fiber at 0%–4% is proposed to meet the different engineering requirements and the flexible selection of steel fiber content.

Application of artificial neural network to control the coagulant dosing in water treatment plant

2000 ◽  
Vol 42 (3-4) ◽  
pp. 403-408 ◽  
Author(s):  
R.-F. Yu ◽  
S.-F. Kang ◽  
S.-L. Liaw ◽  
M.-c. Chen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Water Treatment ◽  
Regression Model ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Ann Model ◽  
Coagulant Dosage ◽  
Ann Models

Coagulant dosing is one of the major operation costs in water treatment plant, and conventional control of this process for most plants is generally determined by the jar test. However, this method can only provide periodic information and is difficult to apply to automatic control. This paper presents the feasibility of applying artificial neural network (ANN) to automatically control the coagulant dosing in water treatment plant. Five on-line monitoring variables including turbidity (NTUin), pH (pHin) and conductivity (Conin) in raw water, effluent turbidity (NTUout) of settling tank, and alum dosage (Dos) were used to build the coagulant dosing prediction model. Three methods including regression model, time series model and ANN models were used to predict alum dosage. According to the result of this study, the regression model performed a poor prediction on coagulant dosage. Both time-series and ANN models performed precise prediction results of dosage. The ANN model with ahead coagulant dosage performed the best prediction of alum dosage with a R2 of 0.97 (RMS=0.016), very low average predicted error of 0.75 mg/L of alum were also found in the ANN model. Consequently, the application of ANN model to control the coagulant dosing is feasible in water treatment.

Measurement of strength and microstructural characteristics of epoxypolimers cured by thermal and microwave methods

2020 ◽  
pp. 35-41
Author(s):  
E. V. Matveev ◽  
A. V. Mamontov ◽  
A. I. Gajdar ◽  
B. A. Lapshinov ◽  
A. N. Vinogradov
Keyword(s):  
Microwave Field ◽  
Epoxy Polymer ◽  
Local Deformation ◽  
Test Method ◽  
Microstructural Characteristics ◽  
Curing Conditions ◽  
Secondary Cracks ◽  
Curing Methods ◽  
Cracks Propagation ◽  
The Relationship

In this work, we studied the strength parameters, fractographic patterns, and the microstructure of epoxy polymer samples cured both by thermal and microwave methods at various temperature, power, and time conditions. The dependence of strength on curing conditions is determined using the tensile test method. To achieve maximum strength for both curing methods optimum conditions were found. A comparative fractographic analysis of microwave and thermal cured samples fractures having similar strength characteristics was carried out by electron microscopy. It was found that microwave field curing leads to the globules size increase in the cured epoxy polymer and an increase in the number of nanopores in the material. Plastic samples local deformation is also more pronounced during fracture, which leads to a greater difference of the main and secondary cracks propagation velocities ratio. The relationship between the studied samples optical density in the wavelength range from 360 to 2500 nm and the parameters of both curing methods (microwave and thermal) was established.

Investigation of Critical Quality Assurance Parameter Variations for Concrete

1997 ◽  
Vol 1575 (1) ◽  
pp. 92-101
Author(s):  
Richard K. Smutzer ◽  
Sedat Gulen ◽  
Youlanda K. Belew ◽  
Virgil L. Anderson
Keyword(s):  
High Pressure ◽  
Quality Assurance ◽  
Flexural Strength ◽  
Concrete Strength ◽  
Pulse Velocity ◽  
Hardened Concrete ◽  
Army Corps ◽  
Split Tensile Strength ◽  
Air Content ◽  
Concrete Mix

The Indiana Department of Transportation is involved in preparing statistically sound specifications for strong and durable concrete used in quality assurance programs. Previous laboratory studies relating concrete strength to air content and concrete mix designs dealt with variation in compressive strength. This study searched for a statistically sound relationship between air content, concrete mix designs, and flexural strength. This study also developed a high-pressure method of hardened concrete air content determination. Sixty-four independent batches (combinations) of concrete were produced, each batch was subjected to a total of 24 tests—4 plastic and 20 hardened. The design factors were aggregate type and gradation, plastic air content, cement, and pozzolanic content and testing operator. After plastic testing, three flexural strength beams were cast from each batch of concrete. The experimental design response variables consisted of flexural, compressive, and split tensile strength along with pulse velocity. Analysis of variances, indicated that the optimum flexural strength could be obtained using as-received stone course aggregate and an air content of between 6 percent and 7.9 percent, with no fly ash. A high-pressure air meter, similar to the meter developed by the Army Corps of Engineers, was used. A strong statistical correlation of determination, r2 = 0.94, was obtained between plastic and the hardened concrete air content using this meter.

Optimal Model Parameters of Inverse Kinematics Solution of a 3R Robotic Manipulator Using ANN Models

2020 ◽  
pp. 814-832
Author(s):  
Nikolaos E. Karkalos ◽  
Angelos P. Markopoulos ◽  
Michael F. Dossis
Keyword(s):  
Network Architecture ◽  
Inverse Kinematics ◽  
Input Data ◽  
Robotic Manipulator ◽  
Industrial Applications ◽  
Model Parameters ◽  
Ann Model ◽  
Inverse Kinematics Problem ◽  
Ann Models ◽  
And Training

Solution of inverse kinematics equations of robotic manipulators constitutes usually a demanding problem, which is also required to be resolved in a time-efficient way to be appropriate for actual industrial applications. During the last few decades, soft computing models such as Artificial Neural Networks (ANN) models were employed for the inverse kinematics problem and are considered nowadays as a viable alternative method to other analytical and numerical methods. In the current paper, the solution of inverse kinematics equations of a planar 3R robotic manipulator using ANN models is presented, an investigation concerning optimum values of ANN model parameters, namely input data sample size, network architecture and training algorithm is conducted and conclusions concerning models performance in these cases are drawn.

scholarly journals Fast, Spectroscopy-Based Prediction of In Vitro Dissolution Profile of Extended Release Tablets Using Artificial Neural Networks

Pharmaceutics ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 400 ◽  
Author(s):  
Galata ◽  
Farkas ◽  
Könyves ◽  
Mészáros ◽  
Szabó ◽  
...  
Keyword(s):  
Raman Spectra ◽  
Extended Release ◽  
Near Infrared ◽  
Hydroxypropyl Methylcellulose ◽  
In Vitro Dissolution ◽  
Dissolution Profile ◽  
Ann Model ◽  
Compression Force ◽  
Ann Models ◽  
Artificial Neural

The pharmaceutical industry has never seen such a vast development in process analytical methods as in the last decade. The application of near-infrared (NIR) and Raman spectroscopy in monitoring production lines has also become widespread. This work aims to utilize the large amount of information collected by these methods by building an artificial neural network (ANN) model that can predict the dissolution profile of the scanned tablets. An extended release formulation containing drotaverine (DR) as a model drug was developed and tablets were produced with 37 different settings, with the variables being the DR content, the hydroxypropyl methylcellulose (HPMC) content and compression force. NIR and Raman spectra of the tablets were recorded in both the transmission and reflection method. The spectra were used to build a partial least squares prediction model for the DR and HPMC content. The ANN model used these predicted values, along with the measured compression force, as input data. It was found that models based on both NIR and Raman spectra were capable of predicting the dissolution profile of the test tablets within the acceptance limit of the f2 difference factor. The performance of these ANN models was compared to PLS models using the same data as input, and the prediction of the ANN models was found to be more accurate. The proposed method accomplishes the prediction of the dissolution profile of extended release tablets using either NIR or Raman spectra.

Development of Neural Network Models for a Crude Oil Distillation Column

2012 ◽  
Author(s):  
Khairiyah Mohd. Yusof ◽  
Fakhri Karray ◽  
Peter L. Douglas
Keyword(s):  
Neural Network ◽  
Steady State ◽  
Crude Oil ◽  
Distillation Column ◽  
Network Models ◽  
Ann Model ◽  
Neural Network Models ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Crude Oil Distillation

This paper discusses the development of artificial neural network (ANN) models for a crude oil distillation column. Since the model is developed for real time optimisation (RTO) applications they are steady state, multivariable models. Training and testing data used to develop the models were generated from a reconciled steady-state model simulated in a process simulator. The radial basis function networks (RBFN), a type of feedforward ANN model, were able to model the crude tower very well, with the root mean square error for the prediction of each variable less than 1%. Grouping related output variables in a network model was found to give better predictions than lumping all the variables in a single model; this also allowed the overall complex, multivariable model to be simplified into smaller models that are more manageable. In addition, the RBFN models were also able to satisfactorily perform range and dimensional extrapolation, which is necessary for models that are used in RTO.

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