Self-compacting concrete strength prediction using surrogate models

Asteris, Panagiotis G.; Kolovos, Konstantinos G.

doi:10.1007/s00521-017-3007-7

Cited by 202 publications

(80 citation statements)

References 48 publications

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“…In this work, the input and output parameters have been normalized in the range [0,1] and [−1,1], respectively. Moreover, in this work a recently proposed transformation technique called Central has been applied [51], in which the origin of the training data is shifted to the centre of the data with the following formula:

z_{i} = x_{i} - \frac{\max (x) + \min (x)}{2}

where

x (x_{1}, x_{2}, \dots, x_{n})

are the original data and

z_{i}

is the i-th transformed data.…”

Section: Resultsmentioning

confidence: 99%

Feed-Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials

Asteris

Roussis

Douvika

2017

Sensors

146

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This work presents a soft-sensor approach for estimating critical mechanical properties of sandcrete materials. Feed-forward (FF) artificial neural network (ANN) models are employed for building soft-sensors able to predict the 28-day compressive strength and the modulus of elasticity of sandcrete materials. To this end, a new normalization technique for the pre-processing of data is proposed. The comparison of the derived results with the available experimental data demonstrates the capability of FF ANNs to predict with pinpoint accuracy the mechanical properties of sandcrete materials. Furthermore, the proposed normalization technique has been proven effective and robust compared to other normalization techniques available in the literature.

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z_{i} = x_{i} - \frac{\max (x) + \min (x)}{2}

where

x (x_{1}, x_{2}, \dots, x_{n})

are the original data and

z_{i}

is the i-th transformed data.…”

Section: Resultsmentioning

confidence: 99%

Feed-Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials

Asteris

Roussis

Douvika

2017

Sensors

146

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“…where P p is accuracy and P exp are the expected agreements. RMSE is often used to evaluate the differences between the predicted and target values [57][58][59][60][61][62], it can be calculated using the following equation:…”

Section: Validation Criteriamentioning

confidence: 99%

“…where m is the number of samples, e i and e i are the output and the target values of the i-th samples, respectively. RMSE has the same error metric units as the data, and smaller RMSE values indicate better performance of a model [60,[63][64][65][66][67][68]. AUC is defined as the area under the ROC curve which is constructed using two statistical values: "Sensitivity" and "100-specificity" [52,69,70].…”

Section: Validation Criteriamentioning

confidence: 99%

GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam

Nguyen

Tran

et al. 2019

Sustainability

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Landslides affect properties and the lives of a large number of people in many hilly parts of Vietnam and in the world. Damages caused by landslides can be reduced by understanding distribution, nature, mechanisms and causes of landslides with the help of model studies for better planning and risk management of the area. Development of landslide susceptibility maps is one of the main steps in landslide management. In this study, the main objective is to develop GIS based hybrid computational intelligence models to generate landslide susceptibility maps of the Da Lat province, which is one of the landslide prone regions of Vietnam. Novel hybrid models of alternating decision trees (ADT) with various ensemble methods, namely bagging, dagging, MultiBoostAB, and RealAdaBoost, were developed namely B-ADT, D-ADT, MBAB-ADT, RAB-ADT, respectively. Data of 72 past landslide events was used in conjunction with 11 landslide conditioning factors (curvature, distance from geological boundaries, elevation, land use, Normalized Difference Vegetation Index (NDVI), relief amplitude, stream density, slope, lithology, weathering crust and soil) in the development and validation of the models. Area under the receiver operating characteristic (ROC) curve (AUC), and several statistical measures were applied to validate these models. Results indicated that performance of all the models was good (AUC value greater than 0.8) but B-ADT model performed the best (AUC= 0.856). Landslide susceptibility maps generated using the proposed models would be helpful to decision makers in the risk management for land use planning and infrastructure development.

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“…There are researches which focus on algorithm development like the study of decomposition techniques for multilayer perceptron training and surrogate models [37][38][39]. A fast and efficient method for training categorical radial basis function network is also studied [40].…”

Section: Artificial Neural Network In Structural Engineering and Matementioning

confidence: 99%

Investigation on the Sensitivity of Ultrasonic Test Applied to Reinforced Concrete Beams Using Neural Network

2018

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An experiment on reinforced concrete beams using four-point bending test during an ultrasonic test was conducted. Three beam specimens were considered for each water/cement ratio (WC) of 40% and 60%, with three reinforcement schedules named design A (comprising two top bars and two bottom bars), design B (with two bottom bars), and design C (with one bottom bar). The concrete beam had a size of 100 mm × 100 mm × 400 mm in length with a plain reinforcement bar of 9 mm in diameter. An ultrasonic test with pitch-catch configuration was conducted at each loading with the transducers oriented in direct transmission across the beams' length with recordings of 68 datasets per beam specimen. Recordings of ultrasonic test results and strains at the top and bottom surfaces subjected to multiple step loads in the experiment were done. After the collection of the data, feed-forward backpropagation artificial neural network (ANN) was used to investigate the sensitivity of the ultrasonic parameters to the mechanical load applied. Five input parameters were examined, as follows: neutral axis (NA), fundamental harmonic amplitude (A1), second harmonic amplitude (A2), third harmonic amplitude (A3), and peak-to-peak amplitude (PPA), while the output parameter was the percentage of ultimate load. Optimum models were chosen after training, validating, and testing 60 ANN models. The optimum model was chosen on the basis of the highest Pearson's Correlation Coefficient (R) and soundness, confirming that it exhibited good behavior in agreement with theories. A classification of sensitivity was performed using simulations based on the developed optimum models. It was found that A2 and NA were sensitive to all WC and reinforcements used in the ANN simulation. In addition, the range of sensitivity of A2 and NA was inversely and directly proportional to the reinforcing bars, respectively. This study can be used as a guide in the selection of ultrasonic parameters to assess damage in concrete with low or high WC and varying reinforcement content.

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Self-compacting concrete strength prediction using surrogate models

Cited by 202 publications

References 48 publications

Feed-Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials

Feed-Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials

GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam

Investigation on the Sensitivity of Ultrasonic Test Applied to Reinforced Concrete Beams Using Neural Network

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