scholarly journals Prediction Model of Compressive Strength Development in Concrete Containing Four Kinds of Gelled Materials with the Artificial Intelligence Method

2019 ◽  
Vol 9 (6) ◽  
pp. 1039 ◽  
Author(s):  
Guohua Liu ◽  
Jian Zheng
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Compressive Strength ◽  
Bp Neural Network ◽  
Strength Development ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Compressive Strength Development ◽  
Compressive Strength Of Concrete ◽  
Conventional Models

Green concrete has been widely used in recent years because its production compliments environmental conservation. The prediction of the compressive strength of concrete using non-destructive techniques is of interest to engineers worldwide. Such methods are easy to carry out because they require little or no sample preparation. Conventional models and artificial intelligence models are two main types of models to predict the compressive strength of concrete. Artificial intelligence models main include the artificial neural network (ANN) model, back propagation (BP) neural network model, fuzzy model etc. Since both conventional models and artificial intelligence models are flawed. This study proposes to build a concrete compressive strength development over time (CCSDOT) model by using conventional method combined with the artificial intelligence method. The CCSDOT model performed well in predicting and fitting the compressive strength development in green concrete containing cement, slag, fly ash, and limestone flour. It is concluded that the CCSDOT model is stable through the use of sensitivity analysis. To evaluate the precision of this model, the prediction results of the proposed model were compared to that of the model based on the BP neural network. The results verify that the recommended model enjoys better flexibility, capability, and accuracy in predicting the compressive strength development in concrete than the other models.

The Study of BP Neural Network in Predicting Concrete Strength

2011 ◽  
Vol 243-249 ◽  
pp. 6169-6173
Author(s):  
Zhao Ming
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Compressive Strength ◽  
Bp Neural Network ◽  
Construction Material ◽  
Concrete Strength ◽  
Main Criterion ◽  
The Past ◽  
Compressive Strength Of Concrete ◽  
Test Result

Concrete is a mainly and commonly good combined construction material, and is consisted of many well-defined components, so mechanical properties of concrete are very complex. the compressive strength of the concrete is a main criterion in producing concrete, but the test on it is complicated because test components of concrete must be kept in the special condition an tested after 28 days. To simplify the procedures and obtain a reasonable data, the paper presents a method using the system of BP neural network predicting the strength of concrete. the system is trained and tested by using many data of strength of concrete in the past ,the test result shows that the value of the strength of concrete predicted is approximate to the experimental value, and the method presented is very efficient and reasonable in predicting the compressive strength of concrete .

scholarly journals The Influence and Application of Slag, Fly Ash, and Limestone Flour on Compressive Strength of Concrete Based on the Concrete Compressive Strength Development over Time (CCSDOT) Model

2020 ◽  
Vol 10 (10) ◽  
pp. 3572
Author(s):  
Jian Zheng ◽  
Guohua Liu
Keyword(s):  
Compressive Strength ◽  
Fly Ash ◽  
Cementitious Materials ◽  
Supplementary Cementitious Materials ◽  
Strength Development ◽  
Mix Proportion ◽  
Practical Engineering ◽  
Compressive Strength Development ◽  
Compressive Strength Of Concrete ◽  
Selection Of

Concrete and cement have been widely used in past decades as a result of urbanization. More and more supplementary cementitious materials are adopted in concrete because its production complements environmental conservation. The influence of slag, fly ash, limestone, etc., on compressive strength of concrete is of interest to engineers worldwide. Many previous studies were specific to certain engineering or certain experiments that could not reveal the nature of the influence of the three supplementary cementitious materials on concrete’s compressive strength. The research concerning the influence of two or more kinds of supplementary cementitious materials on concrete’s compressive strength is still unclear. Moreover, there is a lack of clarity on the optimum proportion of one or more certain cementitious materials in practical engineering or experiments. To overcome these problems, this study adopts the concrete compressive strength development over time (CCSDOT) model, which generates an explicit formula to conduct quantitative research based on extensive data. The CCSDOT model performs well in fitting the compressive strength development of concrete containing cement, slag, fly ash, and limestone flour. The results reveal the nature of the influence of the three supplementary cementitious materials on concrete’s compressive strength through the parameter analysis in the model. Two application cases are analyzed concerning the selection of the three supplementary cementitious materials and design of concrete mix proportion for practical engineering. It is concluded that the CCSDOT model and the method in this study can possibly provide guidance on both the selection of supplementary cementitious materials and the design of optimal concrete mix proportion for practical engineering. Therefore, the study is highly essential and useful.

scholarly journals BRCA Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models

Genes ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1774
Author(s):  
Niyazi Senturk ◽  
Gulten Tuncel ◽  
Berkcan Dogan ◽  
Lamiya Aliyeva ◽  
Mehmet Sait Dundar ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Intelligence ◽  
Risk Factors ◽  
Risk Assessment ◽  
Cancer Patients ◽  
Breast Cancers ◽  
Breast Cancer Patients ◽  
Intelligence Models ◽  
Artificial Intelligence Models

Artificial intelligence provides modelling on machines by simulating the human brain using learning and decision-making abilities. Early diagnosis is highly effective in reducing mortality in cancer. This study aimed to combine cancer-associated risk factors including genetic variations and design an artificial intelligence system for risk assessment. Data from a total of 268 breast cancer patients have been analysed for 16 different risk factors including genetic variant classifications. In total, 61 BRCA1, 128 BRCA2 and 11 both BRCA1 and BRCA2 genes associated breast cancer patients’ data were used to train the system using Mamdani’s Fuzzy Inference Method and Feed-Forward Neural Network Method as the model softwares on MATLAB. Sixteen different tests were performed on twelve different subjects who had not been introduced to the system before. The rates for neural network were 99.9% for training success, 99.6% for validation success and 99.7% for test success. Despite neural network’s overall success was slightly higher than fuzzy logic accuracy, the results from developed systems were similar (99.9% and 95.5%, respectively). The developed models make predictions from a wider perspective using more risk factors including genetic variation data compared with similar studies in the literature. Overall, this artificial intelligence models present promising results for BRCA variations’ risk assessment in breast cancers as well as a unique tool for personalized medicine software.

scholarly journals Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Albert T. Young ◽  
Kristen Fernandez ◽  
Jacob Pfau ◽  
Rasika Reddy ◽  
Nhat Anh Cao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Stress Testing ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Skin Lesions ◽  
Stress Tests ◽  
Performance Metric ◽  
Intelligence Models ◽  
Artificial Intelligence Models

AbstractArtificial intelligence models match or exceed dermatologists in melanoma image classification. Less is known about their robustness against real-world variations, and clinicians may incorrectly assume that a model with an acceptable area under the receiver operating characteristic curve or related performance metric is ready for clinical use. Here, we systematically assessed the performance of dermatologist-level convolutional neural networks (CNNs) on real-world non-curated images by applying computational “stress tests”. Our goal was to create a proxy environment in which to comprehensively test the generalizability of off-the-shelf CNNs developed without training or evaluation protocols specific to individual clinics. We found inconsistent predictions on images captured repeatedly in the same setting or subjected to simple transformations (e.g., rotation). Such transformations resulted in false positive or negative predictions for 6.5–22% of skin lesions across test datasets. Our findings indicate that models meeting conventionally reported metrics need further validation with computational stress tests to assess clinic readiness.

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