Application of Probabilistic Neural Networks for Prediction of Concrete Strength

Kim, Doo Kie; Lee, Jong-Jae; Lee, Jong Han; Chang, Seongkyu

doi:10.1061/(asce)0899-1561(2005)17:3(353)

Cited by 65 publications

(29 citation statements)

References 20 publications

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“…Lai and Serra 1997;Yeh 1998;Oh et al 1999;Ni and Wang 2000;Hong-Guang and Ji-Zong 2000;Lee 2003;Kim et al 2004;Chiang and Yang 2005) where a back propagation algorithm (BPNN) is used to train the network existing datasets. Kim et al (2005) have further enhanced the previously reported (Kim et al 2004) ANN using probabilistic neural network method to handle uncertainty and save computational time. Jain et al (2005) forwarded further insight into the implementation and discussions on the efficiency of neural network models for concrete mix.…”

Section: Fmentioning

confidence: 84%

Adaptive Network–Fuzzy Inferencing to Estimate Concrete Strength Using Mix Design

Tesfamariam

Najjaran

2007

J. Mater. Civ. Eng.

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Section: Fmentioning

confidence: 84%

Adaptive Network–Fuzzy Inferencing to Estimate Concrete Strength Using Mix Design

Tesfamariam

Najjaran

2007

J. Mater. Civ. Eng.

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“…In comparison, NNs use a mathematically flexible platform to construct the relationships for various applications (Lou et al 2001, Attalla and Hegazy 2003, Osman et al 2005. As an improvement to NN in modelling uncertainty within the requirements of probabilistic outcomes, the PNN, which was originally developed by Specht (1990), has been recently adopted for the prediction of concrete strength (Kim et al 2005) and in the reliability assessment of oil and gas pipelines (Sinha and Pandey 2002).…”

Section: Existing Deterioration Modelsmentioning

confidence: 99%

“…The purpose of equation (1) is to minimise the expected risk (Kim et al 2005) in classification, and the product of h i and f i (X) is a posterior probability from Bayesian theorem that allows the updating of existing knowledge h i with new information f i (X). The existing knowledge h i could be obtained from a previous sample or expert opinion and f i (X) is determined by applying an established mathematical foundation (Parzen 1962) to estimate the univariate pdf of a population from its sample, by taking an average sum of suitably chosen kernel (pdf) values for each observation in the sample.…”

Section: Pnn Classificationmentioning

confidence: 99%

“…In the output layer, there is one neuron to represent the classification result. The activation function is an arg max function, which outputs the category associated with the largest value between incoming signals (Kim et al 2005). It can be seen from here that the PNN configuration can allow us to express the non-linear combination effects of all input factors as a single function detailing the pipes' deteriorated condition.…”

Section: Pnn Classificationmentioning

confidence: 99%

“…However, the differences are that the training stage of a PNN is actually to keep the training vectors in the system by assigning their value into weights connecting the neurons in the input layer to corresponding neurons in the pattern layers. Also, a smoothing parameter can be determined by either using trial and error in testing vectors (Kim et al 2005) or applying a genetic algorithm to minimise the classification errors in the training vectors (Mao et al 2000).…”

Section: Pnn Classificationmentioning

confidence: 99%

See 2 more Smart Citations

Application of probabilistic neural networks in modelling structural deterioration of stormwater pipes

Tran

Perera

et al. 2006

Urban Water Journal

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In Australia, when stormwater systems were first introduced over 100 years ago, they were constructed independently of the sewer systems, and they are normally the responsibility of the third level of government, i.e., local government or city councils. Because of the increasing age of these stormwater systems and their worsening performance, there are serious concerns in a significant number of city councils regarding their deterioration. A study has been conducted on the structural deterioration of concrete pipes that make up the bulk of the stormwater pipe systems in these councils. In an attempt to look for a reliable deterioration model, a probabilistic neural network (PNN) model was developed using the data set supplied from participating councils. The PNN model was validated with snapshot-based sample data, which makes up the data set. The predictive performance of the PNN model was compared with a traditional parametric model using discriminant analysis on the same data set. Structural deterioration was hypothesised to be influenced by a set of explanatory factors, including pipe design and construction factors-such as pipe size, buried depth-and site factorssuch as soil type, moisture index, tree root intrusion, etc. The results show that the PNN model has a better predictive power and uses significantly more input variables (i.e., explanatory factors) than the discriminant model. More importantly, the key factors for prediction in the PNN model are difficult to interpret, suggesting that besides prediction accuracy, model interpretation is an important issue for further investigation.

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Concrete Classification Using Machine Learning Techniques

Natarajan¹,

Sundar²,

Berlin

et al. 2022

Structural Integrity

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Application of Probabilistic Neural Networks for Prediction of Concrete Strength

Cited by 65 publications

References 20 publications

Adaptive Network–Fuzzy Inferencing to Estimate Concrete Strength Using Mix Design

Adaptive Network–Fuzzy Inferencing to Estimate Concrete Strength Using Mix Design

Application of probabilistic neural networks in modelling structural deterioration of stormwater pipes

Concrete Classification Using Machine Learning Techniques

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