Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer

Recent trends in steel fibered high-strength concrete

Prediction of mechanical properties of green concrete incorporating waste foundry sand based on gene expression programming.

New formulations for mechanical properties of recycled aggregate concrete using gene expression programming

Assessment of artificial neural network and genetic programming as predictive tools

Behaviour of Slurry Infiltrated Fibre Concrete (SIFCON) under triaxial compression

In this study, a robust variant of genetic programming, namely gene expression programming (GEP) was utilized to build a prediction model for the strength of concrete under triaxial compression loading. The proposed model relates the concrete triaxial strength to mix design parameters. A comprehensive database used for building the model was established on the basis of the results of 330 tests on concrete specimens under triaxial compression. To verify the predictability of the GEP model, it was employed to estimate the concrete strength of the specimens that were not included in the modeling process. Further, the model was externally validated using several statistical criteria recommended by researchers. A sensitivity analysis was carried out to determine the contributions of the parameters affecting the concrete strength. The proposed model is effectively capable of evaluating the ultimate strength of concrete under triaxial compression loading. The derived model performs superior when compared with other empirical models found in the literature. The GEP-based design equation can readily be used for predesign purposes or may be used as a fast check on solutions developed by more in-depth deterministic analyses.

Novel Approach to Strength Modeling of Concrete under Triaxial Compression

New prediction models for concrete ultimate strength under true-triaxial stress states: An evolutionary approach

Real-Time Detection of Cracks on Concrete Bridge Decks Using Deep Learning in the Frequency Domain

Development and testing of a new type of bridge weigh-in-motion system (BWIM) based on the measurement of shear forces near the supports of the bridges is described. The proposed system is applicable to both determinate as well as indeterminate bridges. Fiber-optic Bragg grating rosette sensors were used in this approach. Formulations are based on the establishment of shear influence lines in terms of axle weights and spacings. The system further involves calibration for the estimation of a bridge parameter α, which is a function of the cross-sectional and material properties of the bridge. Three different bridges, one box-girder prestressed concrete bridge and two concrete-slab-on-steel-girder bridges with different span lengths, were instrumented for the evaluation of the proposed BWIM system. Field implementation involved a series of truck runs for calibration and evaluation of the BWIM system. Measured and actual truck-axle weights, spacings, and axle speeds as well as the gross vehicle weight...

Generalized Method and Monitoring Technique for Shear-Strain-Based Bridge Weigh-in-Motion

Saeed Babanajad

Papers

Behaviour of Slurry Infiltrated Fibre Concrete (SIFCON) under triaxial compression

Novel Approach to Strength Modeling of Concrete under Triaxial Compression

New prediction models for concrete ultimate strength under true-triaxial stress states: An evolutionary approach

Real-Time Detection of Cracks on Concrete Bridge Decks Using Deep Learning in the Frequency Domain

Generalized Method and Monitoring Technique for Shear-Strain-Based Bridge Weigh-in-Motion