Stiffness degradation of concrete due to alkali-silica reaction: A computational homogenization approach

Alkali-silica reaction (ASR) is one of the most harmful distress mechanisms affecting concrete infrastructure worldwide. ASR is a chemical reaction that generates a secondary product, which induces expansive pressure within the reacting aggregate material and adjacent cement paste upon moisture uptake, leading to cracking, loss of material integrity, and functionality of the affected structure. In this work, a computational homogenization approach is proposed to model the impact of ASR-induced cracking on concrete stiffness as a function of its development. A representative volume element (RVE) of the material at the mesoscale is developed, which enables the input of the cracking pattern and extent observed from a series of experimental testing. The model is appraised on concrete mixtures presenting different mechanical properties and incorporating reactive coarse aggregates. The results have been compared with experimental results reported in the literature. The case studies considered for the analysis show that stiffness reduction of ASR-affected concrete presenting distinct damage degrees can be captured using the proposed mesoscale model as the predictions of the proposed methodology fall in between the upper and lower bounds of the experimental results

Predicting elastic modulus degradation of alkali silica reaction affected concrete using soft computing techniques: A comparative study

Alkali silica reaction (ASR) is a harmful distress mechanism which results in expansion and reduction of mechanical properties of concrete. The latter may cause loss of serviceability and load carrying capacity of affected concrete structures. Influences of ASR on concrete are known to be complex in nature, for which the traditional empirical and curve-fitting approaches are insufficient to provide adequate models to capture such complexity. Recent advancement in soft computing (SC) offers a new tool for tackling the complexity of ASR affected concrete. Most of previous experimental studies agreed that as a result of ASR, the elastic modulus suffers a significant reduction compared with other properties such as compressive and tensile strength of the affected concrete. In this study, an investigation has been conducted, utilising different SC models to quantify ASR-induced elastic modulus degradation of unrestrained concrete. Five SC techniques, namely support vector machine (SVM), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), M5P model and genetic expression programming (GEP), are investigated comparatively in this research. The models, on basis of SC techniques, are developed and tested using a comprehensive dataset collected from existing publications. In order to demonstrate the superiorities of SC techniques, the proposed approaches are compared to several empirical models developed using same dataset. The comparative results show that the developed SC models outperform empirical models in a wide range of evaluation indices, which indicates promising applications of the proposed approach

Correlating alkali-silica reaction (ASR) induced expansion from short-term laboratory testings to long-term field performance: A semi-empirical model

Correlating short-term expansion of concrete specimens in the laboratory and long-term expansion of concrete in the field is crucial to evaluate the reliability of laboratory test methods and essential for the prognosis of alkali-silica reaction (ASR) in concrete infrastructures. In this study, a novel semi-empirical approach is proposed for forecasting ASR-induced expansion of unrestrained concrete in the field using laboratory measurements data. In addition to the use of short-term laboratory expansion data, the model accounts for the effects of alkali leaching, alkali contribution from aggregates, and environmental conditions (i.e., temperature and relative humidity). A comprehensive database from the literature was gathered for the development and calibration of the proposed model. Finally, the model was used for various concrete blocks incorporating different reactive aggregates and exposed to three outdoor conditions in Canada and the USA. Model outcomes show that it is highly promising for forecasting the induced expansion of concrete in the field from the accelerated laboratory tests data. Analysing the modelling results also highlights the importance of alkali leaching and environmental conditions on the correlation between laboratory and field performance

An Optimised Support Vector Machine Model for Elastic Modulus Prediction of Concrete Subject to Alkali Silica Reaction

© 2020, Springer Nature Singapore Pte Ltd. Alkali-silica reaction (ASR) can induce the damage and loss in serviceability of concrete structures. Many studies have been conducted to investigate the influence of ASR on the degradation of mechanical properties of the concrete. Their results show that compared with other mechanical properties, the modulus of elasticity is the most affected by ASR, where the reduction is up to roughly 70% compared to its properties without expansion. In this study, to effectively assess the reduction of the modulus of elasticity caused by ASR, a novel predictive model is proposed based on support vector machine (SVM), in which the mix proportion of concrete, exposure environment and corresponding expansion are employed as the inputs and the output is the modulus of elasticity degradation. To improve the generalization capacity of the proposed predictive model, three different optimization algorithms are adopted to select optimal model parameters. Finally, the experimental data from the existing literatures are used to test the performance of the proposed method with satisfactory results