Neural Networks for Evaluating CPT Calibration Chamber Test Data
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Summary
The neural network performance was found to be simpler and more effective than regression analysis for modeling the CPT test data andCorrelations between the cone measurements and the engineering properties of sand can be developed using the generalization capabilities of the neural network.
- Type
- article
- Published
- 1995-03-01
- Cited by
- 17
- References
- 6
- OpenAlex
- https://openalex.org/W2012689841
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:109347568
Keywords
Artificial neural network, Backpropagation, Generalization, Calibration, Computer science
References
- Knowledge-Based Modeling of Material Behavior with Neural Networks
- An introduction to computing with neural nets
- NEURAL NETWORKS TRAINED BY ANALYTICALLY SIMULATED DAMAGE STATES
- Seismic liquefaction potential assessed by neural networks
- Neural Networks in Civil Engineering. I: Principles and Understanding
- Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
- Interpretation of CPTs and CPTUs. Part 2: drained penetration of sands
- An introduction to computing with neural nets
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- Modeling slump of concrete with fly ash and superplasticizer
- Optimization of concrete mix proportioning using a flattened simplex–centroid mixture design and neural networks
- Simulation of concrete slump using neural networks
- Neural Networks in Civil Engineering: 1989–2000
- Reinforcement of clay soils using waste carpet fibres.
- O zastosowaniu sztucznych sieci neuronowych do zarządzania ryzykiem na budowie
- Prediction of Geotechnical Parameters Using Machine Learning Techniques
- Construction Scheduling, Cost Optimization and Management
- Fabricating a new Rheometer for Concrete
- Performance Evaluation of RBF Networks with Various Variables to Forecast the Properties of SCCs
- Hybrid Machine Learning Model for Predicting the Fatigue Life of Plain Concrete Under Cyclic Compression
- Forecasting the Classification of Soil Systems by Using Python Programming
- APPLICATION OF NEURAL NETWORKS FOR MODELING SHEAR STRENGTH OF REINFORCED CONCRETE BEAMS
- Artificial Intelligence Applied in the Concrete Durability Study
- Predicting post-wildfire debris flow onset using machine learning models on multi-parameter experimental data
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