Advanced Probabilistic Neural Network for the Prediction of Concrete Strength

Probabilistic Neural Network(PNN) based on Alphastable distribution is proposed to improve former algorithm's poor fitting ability caused by independent identically distributed Gaussian assumption in intrusion detection's non-Gaussian data. Fast Fourier Transform Numerical Computation is used in the algorithm to establish its Alphastable distribution probability density function(PDF), and then the Samples Characteristic Function Algorithm is adopted to estimate the parameters, and finally Alpha-stable distribution PDF instead of Gaussian function is used as the activation function of PNN. In the simulation, intrusion detection dataset, KDD CUP 99, is used to validate the algorithm, which shows that compared with previous PNN and BPNN, the new algorithm has a stronger ability of fitting the non-Gaussian data and has improved the detection rate and false negative rate in intrusion detection greatly while the false alarm rate is stable.

Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28 th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS), and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature. The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c) is the most significant factor affecting the output of the model. The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

Neural Network (NN) system is an information-processing that has characteristics similar to the neural network in living beings. A model of Neural Network is used for classification is Probabilistic Neural Network (PNN). PNN structured by four layers, the input layer, layer pattern, the summation layer and output layer. One of classification problems that can be solved by PNN is a classification of Diabetes Mellitus’s status. Diabetes mellitus is a chronic disease that occurs either when the pancreas does not produce enough insulin or when the body cannot effectively use the insulin produced. To facilitate the classification of diabetes mellitus, it is used a software-based Graphical User Interface (GUI) of Matlab to build a software of PNN. GUI that is formed can do PNN classification and predict the status of one’s Diabetes Mellitus. PNN structure that is formed resulting the highest accuracy 0.9143548 on the training process and 0.919512 on the testing process obtained by the percentage of training data than testing data by 90%:10% with holdout accuracy evaluation method, and a smoothing value of 1. This classification resulting 23 patients were classified as negative diabetes and 18 patients were classified as positive diabetes. Keywords : Neural Network, Probabilistics Neural Network, diabetes mellitus, GUI, holdout, smoothing parameter.

High-performance concrete, besides aggregate, cement, and water, incorporates supplementary cementitious materials, such as fly ash and blast furnace slag, and chemical admixture, such as superplasticizer. Hence, it is a highly complex material and modeling its behavior represents a difficult task. This paper presents an evolutionary system for the prediction of high performance concrete strength. The proposed framework blends a recently developed version of genetic programming with a local search method. The resulting system enables us to build a model that produces an accurate estimation of the considered parameter. Experimental results show the suitability of the proposed system for the prediction of concrete strength. The proposed method produces a lower error with respect to the state-of-the art technique. The paper provides two contributions: from the point of view of the high performance concrete strength prediction, a system able to outperform existing state-of-the-art techniques is defined; from the machine learning perspective, this case study shows that including a local searcher in the geometric semantic genetic programming system can speed up the convergence of the search process.

Background Chondroitin sulphate proteoglycans (CSPGs) can cause the termination of ocular dominance plasticity in the visual cortex.Recently,protein tyrosine phosphatase σ (PTPσ) has been identified as a receptor that inhibits CSPGs.However,whether PTPσ and its downstream molecules participate in the reactivation of ocular dominance plasticity in adult visual cortex has not been studied.Objective The present study was to investigate the changes in the expression of the PTPσ,probabilistic neural networks (PNNs),and molecules downstream of PNN,such as N-cadherin/β-catenin,after the reactivation of adult visual cortical plasticity.Methods Fifty-four SPF Long Evans rats were grouped according to different postnatal week (PW) as the PW1 (6 rats),PW3 (6 rats),PW5 (6 rats),PW7 (24 rats),and PW9 (12 rats) groups,and the upper and lower eyelids were sutured in the 12 rats from the PW7 group for 14 days to establish the binocular plasticity reactivation models.Expression of PTPσ and PNNs in the rat visual cortex was detected using immunochemistry,and changes of PTPσ mRNA,N-cadherin mRNA and β-catenin mRNA expression in the rat visual cortex with plasticity reactivation were assessed by real-time fluorescence quantitative PCR (RT Q-PCR).The use of animals followed the Regulations for the Administration of Affairs Concerning Experimental Animals by State Science and Technology Commission.Results The expression level of PTPσ mRNA was significantly higher in the PW9 group than that of the binocular plasticity reactivation models and the PW7 group (t =1.965,3.526,P＜0.01).The staining of the rat visual cortex for PTPσwas localized to the cellular membrane,cytoplasm and axon.Cell densities of the PW9 group in the Ⅱ-Ⅲ layer,Ⅳ layer and Ⅴ-Ⅵ layer of the visual cortex were elevated in the PW9 rats compared with the PW7 rats (t =24.593,23.444,13.556,P＜0.01) and rats from the binocular plasticity reactivation model (t =44.111,43.000,16.556,P＜0.01).Cell densities for PNNs in the Ⅳ and Ⅴ-Ⅵ layers were significantly increased in the PW9 rats in comparison with the PW7 rats (t=1.926,P＜0.01 ;t=1.370,P＜0.05),but the cell density in the Ⅱ-Ⅱ layer has no statistical significance (t=0.889,P＞0.05).However,cell densities for PNNs in the Ⅱ-Ⅲ and Ⅳ layers in the binocular plasticity reactivation models were lower than those of the PW9 rats (t =2.556,4.585,P＜0.01).Compared with PW1 rats,the expression levels of the N-cadherin mRNA in the PW3,PW5,PW7,PW9 rats were lower (t =28.932,28.988,27.083,28.908,P＜0.01),but those in the PW7 rats were enhanced in comparison with the PW3 rats,PW5 rats and PW9 rats (t =1.848,1.904,1.825,P＜0.01).No significant difference was seen in the expression of the N-cadherin mRNA between the PW9 rats and rats from the binocular plasticity reactivation model (t =0.072,P＞0.05).A statistically significant increase was found in the β-catenin mRNA expression in the PW1 rats compared with the PW3,PW5,PW7 and PW9 rats (t =3.918,3.534,2.645,4.652,P＜ 0.0 1),as well as between rats from the binocular plasticity reactivation model and the PW9 rats (t =0.570,P＜0.01).Conclusions PTPr,PNNs and β-catenin are involved in the reactivation of ocular dominance plasticity in the adult visual cortex. Key words: Visual cortex; Protein tyrosine phosphatase σ; Plasticity; Binocular form-deprivation

Aiming at the complexity of University professor titles evaluation, and the problems of falling easily into part minimums and low velocity of convergence in BP neural network, PNN is put forward to forecast university professor titles evaluation. 46 teachers' promotion to associate professor in the Institution in 2012 are adopted as the sample, Comparing the probabilistic neural network (PNN) method with BP neural network one, The former gave the recognition rate of 97.3% and 88.9%, is better than the latter seeing from the experimental results.

The rise in paper waste from the paper processing sector is becoming the most important issue of disposal by filling large space and polluting the atmosphere, as it rises day to day. To decrease the paper waste as well as environmental emissions, it is recommended to use the paper waste in concrete as Hypo sludge. That contains the properties of cement such as SiO2 & Mg. Cement is the predominant material in concrete. In this research cement is replaced with fly ash as well as hypo sludge. By adding 5percent of fly ash and 0%, 3 %, 6%,9%,12% and 15% of hypo sludge, split tensile strength & compressive strength of concrete as well as Hypo sludge concrete efficiency are being tested. Split tensile strength & compressive strength of hypo sludge concrete are improved by the experiment up to 9% behind this % strength will decrease. In this journal the extension of research work is added that is flexural strength of concrete adding fly ash & hypo sludge in concrete and durability of concrete are studied. Performance of hypo sludge concrete, result and optimum percentage are mentioned in this paper.

Partial discharge (PD) classification in power cable accessories and high voltage equipment in general is essential in evaluating the severity of the damage in the insulation. In this article, the PD classification was realised as a two-fold process. Firstly, measurements taken from a high-frequency current transformer (HFCT) sensor were represented as features by means of a transformation to the classifier and secondly, the probabilistic neural network (PNN) classifier itself was capable of effectively recognising features coming from different types of discharges. The feature that was used as a fingerprint for PD characterisation was extracted from the moments of the probability density function (PDF) of the wavelet coefficients at various scales, obtained through the wavelet packets transformation. The PNN classifier was used to classify the PDs and assess the suitability of this feature vector in PD classification. Four types of artificial PDs were created in a high voltage laboratory, namely corona discharge in air, floating discharge in oil, internal discharge in oil and surface discharge in air, at different applied voltages, and were used to train the PNN algorithm. The results obtained here (97.49, 91.9, 100 and 99.8% for the corona, the floating, the internal and the surface discharges, respectively) are very encouraging for the use of PNN in PD classification with this particular feature vector. This article suggests a feature extraction and classification algorithm for PD classification, which when combined together reduced the dimensionality of the feature space to a manageable dimension, and achieved very high levels of classification.

Hyperspectral identification of rice diseases and pests based on principal component analysis and probabilistic neural network.

In this paper, the environmental factors surrounding fruit trees are used as the explanatory variables, and the factors concerning the quality of the agricultural produce are used as the explained variables. The observed data are analyzed. The object of analysis is the Onshu orange (mandarin orange). The purposes of this paper are analysis of the effect of environmental factors on product quality, the target value settings for the controllable environmental factors in order to produce high-quality products, and evaluation of the cultivation environment for each item. A system for data analysis is proposed which extracts the useful information for the decision-making such as cultivation management, as well as harvestsshipping planning, from the observed data. The system uses a probabilistic neural network and learns the mapping from the describing variables consisting of observed data to the explained variables. By superposing the acquired knowledge, inverse modeling can be performed. It is shown that by using the proposed system, multivariate nonlinear inverse mapping can be realized instead of the linear inverse regression estimation used in conventional single regression analysis. © 2005 Wiley Periodicals, Inc. Syst Comp Jpn, 36(11): 102–110, 2005; Published online in Wiley InterScience (). DOI 10.1002sscj.10577

The evolution of concrete strength prediction methodologies has transitioned from empirical formulas based on experimental data to contemporary soft computing approaches. Initially, the concrete mix design was reliant on simple relationships between concrete mix proportions and compressive strength; later, the early techniques evolved to include statistical models incorporating material properties, curing conditions, and environmental variables. The advent of computational tools and artificial intelligence marked a paradigm shift, with accurate concrete strength prediction crucial for influencing structural integrity, safety, and cost-effectiveness in construction. The article explores empirical and analytical concrete strength prediction models before reviewing the application of soft computing approaches such as fuzzy logic, genetic algorithms, and neural networks. The integration of these models and hybrid approaches is discussed in this research study by highlighting their effectiveness in handling complex relationships within concrete mix parameters. A comparative analysis of various soft computing methods applied to structural and non-structural elements is carried out in this study to demonstrate their diverse applications and advantages in optimizing concrete mix designs, enhancing structural performance, and contributing to cost and time efficiency in construction processes.

The use of flow concrete is the main choice in construction because of its high workability that facilitates compaction. In this study, utilizing corn cob ash as a partial substitution material for cement because the content of corn cob ash makes the waste potentially used as a concrete mixing material. One of the most important silica content in concrete is silica (SiO2). Corn cob ash has a fairly high silica content of 59%. Silica plays an important role in concrete because it can increase the compressive strength of concrete. This study aims to determine the effect of corn cob ash with variations of 0%, 4%, 5%, 7%, 9% on the compressive strength of flow concrete with a proportion of superplasticizer 1.5% at the age of 7, 21, and 28 days. From the results of the study, it can be concluded that the maximum compressive strength of concrete is in the CCA mixture of 7% at the age of 28 days of concrete, which is 35.22 MPa. The compressive strength of concrete with a mixture of 0% to 7% CCA continues to increase in each life of concrete. Based on the results of this study, corn cob ash as a partial substitution of cement can affect the compressive strength value of concrete. Keywords—Flow Concrete, Corn Cob Ash, Compressive Strength of Concrete

Ultrasonic non-destructive evaluation is an essential component of con- dition assessment of concrete. Conventionally, the use of ultrasonics is limited to the study of velocity of sound through concrete. The velocity is qualitatively linked to the condition of the concrete in service. However, sophistication in signal pro- cessing has led to the development of better ultrasonic techniques that can give some quantitative estimates of the damage. This paper describes the use of the transmitted ultrasonic energy to estimate the level of damage in concrete under uniaxial compression. The experimental set up was designed to collect ultrasonic signals between transducers at opposite ends of a concrete cube that was loaded in compression. A specially designed software program allowed real time analy- sis of the signal - including calculation of transmitted energy, obtaining FFT, etc. The methodology adopted in the study was able to differentiate the behaviour of ordinary and high strength concrete in compression.

Flyash waste is a result of coal combustion which can pollute the environment. The research aims to determine (1) how the effect of the variation of the percentage of Flyash as a partial substitute for cement on the weight of the concrete load, (2) how the variation in the percentage of Flyash as a partial cement substitute for the compressive strength of concrete, and (3) how does curing variation effect (treatment) on the value of compressive strength of concrete with Flyash as a substitute for partial cement. The research was conducted in the Laboratory of Building Materials, Structures, and Construction, Faculty of Architectural Engineering, University of Hasanuddin, Gowa Regency, South Sulawesi Province. The method used in this study was an experiment by making concrete from Flyash class F waste from PT. Bosowa Energi Jeneponto Regency, South Sulawesi Province. Concrete samples were made in a total of 120 pieces for two types of curing, namely dry curing and water curing, four sample variations namely 0%, 30%, 50%, and 70% Flyash, weight testing and compressive strength of the samples carried out each of the three age of 3, 7, 14, 21 and 28 days respectively. The results showed that the average weight of the lowest concrete samples was in the variation of 30% Flyash for dry curing, which was 3.45 kg, and water curing was 3.61 kg, while the highest was in the 70% variation of Flyash for dry curing, which is 4.47 kg, and water curing is 4.61 kg. Based on variations of 0%, 30%, 50%, and 70% the compressive strength of concrete declined, namely 24.84 MPa, 22.16 MPa, 11.97 MPa, and 4.20 MPa, for dry curing, and 25.73 MPa, 22.93 MPa, 12.87 MPa, and 4.97 MPa for water curing. The effect of curing variations on the compressive strength of concrete is also increasing as the curing period increases, for dry curing 3, 7, 14, 21, and 28 days, respectively 12.73 MPa, 13.33 MPa, 13.63 MPa, 15,07 MPa, and 22.16 MPa, for water curing, respectively 13.50 MPa, 14.09 MPa, 14.56 MPa, 15.84 MPa, and 22, 93 MPa.

Fly ash or pulverized fuel ash is one of the mineral admixtures in concrete, widely used throughout the world. However, very little attention has been paid to the material in Pakistan, though large quantities of it are being wasted in the thermal power stations . The aim of this research was to study the cementitious properties of fly ash sample collectedfrom coal fire station Lakhra District Hyderabad Sindh. The chemical analysis of fly ash revealed that various constituents are falling in the favourable limits recommended by the American Concrete Association. The effect of cement substitution by fly ash in concrete was studied both in compression and flexure . The level of substitution , a typical primary school with mass construction all over Pakistan was considered . The research has revealed that the compressive strength of concrete has been decreased by merely 15% with half of the cement replaced by fly ash whereas there is an overall saving of 12% in the cost of construction . The behavior of reinforced fly ash added concrete also reflected very slight reduction in the flexural strength of concrete . The results necessitate further research about the various aspects of fly ash available at different thermal Power Stations .