A review of vibration-based damage detection in civil structures : from traditional methods to Machine Learning and Deep Learning applications

Emerging artificial intelligence methods in structural engineering

https://par.nsf.gov/servlets/purl/10100512

Computational design optimization of concrete mixtures: A review

Artificial neural network based modelling approach for strength prediction of concrete incorporating agricultural and construction wastes

Probability and statistics in Civil Engineering: by G.N. Smith, Nichols Publishing Company, New York, NY, 1986, 244 pp.

Compressive strength and splitting tensile strength are both important parameters that are utilized for characterization concrete mechanical properties. This paper aims to show a possible applicability of support vector machine (SVM) to predict the splitting tensile strength of concrete from compressive strength of concrete, a SVM model was built, trained, and tested using the available experimental data gathered from the literature. All of the results predicted by the SVM model are compared with results obtained from experimental data, and we found that the predicted splitting tensile strength of concrete is in good agreement with the experimental data. The splitting tensile strength results predicted by SVM are also compared to those obtained by using empirical results of the building codes and various models. These comparisons show that SVM has strong potential as a feasible tool for predicting splitting tensile strength from compressive strength.

https://downloads.hindawi.com/journals/amse/2013/597257.pdf

Prediction of Splitting Tensile Strength from Cylinder Compressive Strength of Concrete by Support Vector Machine

Gene expression programming (GEP) models, a robust variant of genetic programming, are developed in this study to correlate resilient modulus with routine properties of subgrade soils and state of stress for pavement design applications. A database used for building the model was developed that contained grain size distribution, Atterberg limits, standard Proctor, unconfined compression, and resilient modulus results for 97 soils from 16 different counties in Oklahoma. Of these, 63 soils (development data set) are used in training, and the remaining 34 soils (evaluation data set) from two different counties are used in evaluation of the models developed. Two different correlations were developed using different combinations of the influencing parameters. The proposed constitutive models relate the resilient modulus of routine subgrade soils to moisture content w, dry density γd, plasticity index (PI), percent passing a No. 200 sieve (P200), unconfined compressive strength Uc, deviatoric stress σd,...

Guang-hui Shen

Papers

Prediction of Splitting Tensile Strength from Cylinder Compressive Strength of Concrete by Support Vector Machine

Novel Approach to Resilient Modulus Using Routine Subgrade Soil Properties