scholarly journals Using an Artificial Neural Network to Validate and Predict the Physical Properties of Self-Compacting Concrete

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
K. Thirumalai Raja ◽  
N. Jayanthi ◽  
Jule Leta Tesfaye ◽  
N. Nagaprasad ◽  
R. Krishnaraj ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Concrete Strength ◽  
Setting Time ◽  
Second Phase ◽  
Self Compacting Concrete ◽  
Initial Setting Time ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

SCC (self-compacting concrete) is a high-flowing concrete that blasts into structures. Many academics have been interested in using an artificial neural network (ANN) to forecast concrete strength in recent years. As a result, the goal of this study is to confirm the various possibilities of using an artificial neural network (ANN) to detect the features of SCC when Portland Pozzolana Cement (PPC) is partially substituted with biowaste such as Bagasse Ash (BA) and Rice Husk Ash (RHA) (RHA). Specialist systems based on the fully connected cascade (FCC) architecture in artificial neural networks (ANN) are used to estimate the compressive toughness of SCC. The research results are confirmed with the forecasting results of ANN utilizing 73 trial datasets of differentiation focus proposals of cement, BA, and RHA containing parameters such as initial setting time (IST), final setting time (FST), and standard consistency. Experiments to determine compressive strength for a wider range of mixed prepositions will result in higher project expenses and delays. So, an expert system ANN is used to find the standard consistency, setting time, and compressive strength for the intermediate mix propositions according to IS 10262:2009. The experimental results of compressive strength for 28 days are considered, in which 70% was used to train the ANN and 30% was utilized for testing the accuracy of the predicted compressive strength for the intermediate mix proposition. Using all of the datasets, the number of hidden layers used for compressive strength prediction for intermediate mix proposal is determined in the first step. The compressive strength for the intermediate mix preposition was identified in the second phase of the research, using the number of hidden layers determined in the first phase. The results were validated using the correlation coefficient (R) and root mean square error (RMSE) obtained from ANN, resulting in an acceptance range of 97 percent to 99 percent.

scholarly journals Learned Prediction of Compressive Strength of GGBFS Concrete Using Hybrid Artificial Neural Network Models

Materials ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 3708 ◽  
Author(s):  
In-Ji Han ◽  
Tian-Feng Yuan ◽  
Jin-Young Lee ◽  
Young-Soo Yoon ◽  
Joong-Hoon Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Experimental Studies ◽  
Back Propagation ◽  
Network Models ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Artificial Neural Network Models

A new hybrid intelligent model was developed for estimating the compressive strength (CS) of ground granulated blast furnace slag (GGBFS) concrete, and the synergistic benefits of the hybrid algorithm as compared with a single algorithm were verified. While using the collected 269 data from previous experimental studies, artificial neural network (ANN) models with three different learning algorithms namely back-propagation (BP), particle swarm optimization (PSO), and new hybrid PSO-BP algorithms, were constructed and the performance of the models was evaluated with regard to the prediction accuracy, efficiency, and stability through a threefold procedure. It was found that the PSO-BP neural network model was superior to the simple ANNs that were trained by a single algorithm and it is suitable for predicting the CS of GGBFS concrete.

scholarly journals Speech recognition system for Turkish language with hybrid method

2017 ◽  
Vol 7 (1) ◽  
pp. 48-57
Author(s):  
Cigdem Bakir
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Speech Recognition ◽  
Hybrid Model ◽  
Recognition System ◽  
Second Phase ◽  
Speech Recognition System ◽  
Feature Vectors ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Currently, technological developments are accompanied by a number of associated problems. Security takes the first place amongst such problems. In particular, biometric systems such as authentication constitute a significant fraction of the security problem. This is because sound recordings having connection with various crimes are required to be analysed for forensic purposes. Authentication systems necessitate transmission, design and classification of biometric data in a secure manner. The aim of this study is to actualise an automatic voice and speech recognition system using wavelet transform, taking Turkish sound forms and properties into consideration. Approximately 3740 Turkish voice samples of words and clauses of differing lengths were collected from 25 males and 25 females. The features of these voice samples were obtained using Mel-frequency cepstral coefficients (MFCCs), Mel-frequency discrete wavelet coefficients (MFDWCs) and linear prediction cepstral coefficient (LPCC). Feature vectors of the voice samples obtained were trained with k-means, artificial neural network (ANN) and hybrid model. The hybrid model was formed by combining with k-means clustering and ANN. In the first phase of this model, k-means performed subsets obtained with voice feature vectors. In the second phase, a set of training and tests were formed from these sub-clusters. Thus, for being trained more suitable data by clustering increased the accuracy. In the test phase, the owner of a given voice sample was identified by taking the trained voice samples into consideration. The results and performance of the algorithms used for classification are also demonstrated in a comparative manner. Keywords: Speech recognition, hybrid model, k-means, artificial neural network (ANN).

scholarly journals PREDIKSI KUAT TEKAN PERVIOUS PAVING DENGAN CAMPURAN ABU SEKAM DENGAN MENGGUNAKAN PEMODELAN ARTIFICIAL NEURAL NETWORK (ANN)

2019 ◽  
Vol 1 (1) ◽  
pp. 46-52
Author(s):  
Erna Suryani ◽  
Wahyu Naris Wari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Rice Husk ◽  
Empirical Equation ◽  
Rice Husk Ash ◽  
Asphalt Pavement ◽  
Construction Material ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Pervious Paving (Paving Berpori) adalah material konstruksi yang terbuat dari semen, air, agregat dan bahan campuran lainnya. Paving berpori dapat diapilkasikan pada trotoar, area bermain dan jalan perumahan. Dengan menggunakan paving berpori air akan langsung meresap, sehingga akan mencegah adanya genangan air pada lapis permukaan paving. Metode penelitian yang digunakan adalah menggunakan AAPA (Australian Asphalt Pavement Association) dimana dilakukan sistem Trial Eror. Campuran yang digunakan adalah 1:4, dengan menggunakan gradasi terbuka. Kuat tekan yang di rencanakan yaitu 18,00 MPa, masuk dalam kategori mutu B untuk tempat parkir mobil, pejalan kaki dan taman kota. Campuran paving menggunakan abu sekam padi sebagai reduksi semen dengan persentase 0%, 10%, 20% dan 30%. Pelaksanaan pekerjaan dimulai dari pengambilan bahan baku, pengujian material, perencanaan komposisi dan pembuatan benda uji dengan ukuran P = 21 cm, L = 11,5 cm dan T = 6 cm. Uji kuat tekan dilakukan untuk mengetahui pengaruh abu sekam sebagai bahan reduksi semen pada Paving Berpori. Nilai kuat tekan yang didapatkan akan menjadi input pada program Matlab untuk mendapatkan pemodelan Persamaan Empiris dengan ARTIFICIAL NEURAL NETWORK (ANN) sehingga didapatkan nilai kuat tekan dari berbagai komposisi penambahan bahan abu sekam. Dari hasil penelitian didapatkan persentase tertinggi dicapai pada tambahan abu sekam 30%.Kata kunci : Abu sekam, Artificial Neural Network (ANN), Pervious Paving, Kuat tekan, Persamaan EmpirisPervious Paving is a construction material made from cement, water, aggregate and other materials. Pervious paving can be applied to right on sidewalks, play ground and residential roads. By using Pervious Paving, the water will absorb quickly, so it will prevent the puddles on the surface layer. AAPA (Australian Asphalt Pavement Association) is the reserach methode which we used with Trial and Eror. The mixture of ingredients is 1: 4 with the open gradation. The compressive strength designed is 18 MPa, which is in category B for parking car, pedestrian and city park. Paving mixture consisted of rice husk ash as cement reduction with a percentage of 0%, 10%, 20% and 30%. The work starting from the taking of raw materials, material testing, composition planning and the making of specimens with sizes P = 21 cm, L = 11.5 cm and T = 6 cm. The compressive strength test was conducted to determine the effect of husk ash addition. The compressive strength will be input to the Matlab program to obtain the Empirical Equation modelling with ARTIFICIAL NEURAL NETWORK (ANN). Based on the results of the study, the highest percentage was achieved in the mixture with an addition of 30% rice husk ash.Keywords: Rice husk ash, Artificial Neural Network (ANN), Pervious Paving, Compressive strength, Empirical Equation

Predicting the Workability of Self-Compacting Concrete Using Artificial Neural Network

2010 ◽  
Vol 168-170 ◽  
pp. 1730-1734
Author(s):  
Fang Xian Li ◽  
Qi Jun Yu ◽  
Jiang Xiong Wei ◽  
Jian Xin Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ann Model ◽  
Self Compacting Concrete ◽  
Data Set ◽  
Slump Flow ◽  
Super Plasticizer ◽  
Input Parameters ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

An artificial neural network (ANN) is presented to predict the workability of self compacting concrete (SCC) containing slump, slump flow and V-test. A data set of a laboratory work, in which a total of 23 concretes were produced, was utilized in the ANNs study. ANN model is constructed, trained and tested using these data. The data used in the ANN model are arranged in a format of six input parameters that cover the cement, fly ash, blast furnace slag, super plasticizer, sand ratio and water/binder, three output parameters which are slump, slump flow and V-test of SCC. ANN-1, ANN-2 and ANN-3 models which containing 15 ,11 and 5 neurons in the hidden layers, respectively are found to predict workability of concrete well within the ranges of the input parameters considered. The three models are tested by comparing to the results to actual measured data. The results showed that ANN-2 is the best suitable for predicting the workability of SCC using concrete ingredients as input parameters.

Sign in / Sign up

Export Citation Format

Share Document