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   3D meso-scale modelling of foamed concrete based on X-ray Computed Tomography; ; et al in Construction and Building Materials (2018), 188 Foamed concrete has been widely used in infrastructure construction and poses new challenges to design and modelling. This paper investigates the behaviour of foamed concrete with the help of X-ray ... [more ▼] Foamed concrete has been widely used in infrastructure construction and poses new challenges to design and modelling. This paper investigates the behaviour of foamed concrete with the help of X-ray Computed Tomography (XCT), which is capable of characterising the microstructure of foamed concrete. An in situ compressive test-XCT scan is carried out to observe the failure mechanism of foamed concrete by XCT when subjected to compression load. A meso-scale simulation based on XCT images is conducted to simulate the behaviour of foamed concrete. An algorithm that directly reconstructs the meso-scale model from XCT images is developed. The experimental and numerical results show that foamed concrete exhibits a significant change in mechanical behaviour; it is less brittle compared to the response of dense samples. However, the reduction in the level of brittleness is accompanied by a significant decrease in compressive strength. Failure development inside samples is successfully captured by the XCT scan and the meso-scale modelling. The topology of foamed structures, in particular the chain of interconnected pores, influences the failure mechanism of foamed concrete. The combination of XCT scan and meso-scale modelling provides a comprehensive framework to understand the mechanical behaviour of foamed concrete. [less ▲] Detailed reference viewed: 161 (2 UL)Deep neural network with high-order neuron for the prediction of foamed concrete strength; ; et al in Computer-Aided Civil and Infrastructure Engineering (2018) The article presents a deep neural network model for the prediction of the compressive strength of foamed concrete. A new, high-order neuron was developed for the deep neural network model to improve the ... [more ▼] The article presents a deep neural network model for the prediction of the compressive strength of foamed concrete. A new, high-order neuron was developed for the deep neural network model to improve the performance of the model. Moreover, the cross-entropy cost function and rectified linear unit activation function were employed to enhance the performance of the model. The present model was then applied to predict the compressive strength of foamed concrete through a given data set, and the obtained results were compared with other machine learning methods including conventional artificial neural network (C-ANN) and second-order artificial neural network (SO-ANN). To further validate the proposed model, a new data set from the laboratory and a given data set of high-performance concrete were used to obtain a higher degree of confidence in the prediction. It is shown that the proposed model obtained a better prediction, compared to other methods. In contrast to C-ANN and SO-ANN, the proposed model can genuinely improve its performance when training a deep neural network model with multiple hidden layers. A sensitivity analysis was conducted to investigate the effects of the input variables on the compressive strength. The results indicated that the compressive strength of foamed concrete is greatly affected by density, followed by the water-to-cement and sand-to-cement ratios. By providing a reliable prediction tool, the proposed model can aid researchers and engineers in mixture design optimization of foamed concrete. [less ▲] Detailed reference viewed: 201 (12 UL) |
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