In recent years, deep neural network models have shown to outperform many state of the art algorithms. The reason for this is, unsupervised pretraining with multi-layered deep neural networks have shown to learn better features, which further improves many supervised tasks. These models not only automate the feature extraction process but also provide with robust features for various machine learning tasks. But the unsupervised pretraining and feature extraction using multi-layered networks are restricted only to the input features and not to the output. The performance of many supervised learning algorithms (or models) depends on how well the output dependencies are handled by these algorithms [Dembczy´nski et al., 2012]. Adapting the standard neural networks to handle these output dependencies for any specific type of problem has been an active area of research [Zhang and Zhou, 2006, Ribeiro et al., 2012]. On the other hand, inference into multimodal data is considered as a difficult problem in machine learning and recently ‘deep multimodal neural networks’ have shown significant results [Ngiam et al., 2011, Srivastava and Salakhutdinov, 2012]. Several problems like classification with complete or missing modality data, generating the missing modality etc., are shown to perform very well with these models. In this work, we consider three nontrivial supervised learning tasks (i) multi-class classification (MCC), (ii) multi-label classification (MLC) and (iii) label ranking (LR), mentioned in the order of increasing complexity of the output. While multi-class classification deals with predicting one class for every instance, multi-label classification deals with predicting more than one classes for every instance and label ranking deals with assigning a rank to each label for every instance. All the work in this field is associated around formulating new error functions that can force network to identify the output dependencies. Aim of our work is to adapt neural network to implicitly handle the feature extraction (dependencies) for output in the network structure, removing the need of hand crafted error functions. We show that the multimodal deep architectures can be adapted for these type of problems (or data) by considering labels as one of the modalities. This also brings unsupervised pretraining to the output along with the input. We show that these models can not only outperform standard deep neural networks, but also outperform standard adaptations of neural networks for individual domains under various metrics over several data sets considered by us. We can observe that the performance of our models over other models improves even more as the complexity of the output/ problem increases.

Self-consolidating concrete (SCC) has wide use for placement in congested reinforced concrete structures in recent years. SCC represents one of the most outstanding advances in concrete technology during the last two decades. In the current work a great deal of cognizance pertaining to mechanical properties of SCC and comparison of fracture characteristics of notched and unnotched beams of plain concrete as well as using acoustic emission to understand the localization of crack patterns at different stages has been done. An artificial neural network (ANN) is proposed to predict the 28day compressive strength of a normal and high strength of SCC and HPC with high volume fly ash. The ANN is trained by the data available in literature on normal volume fly ash because data on SCC with high volume fly ash is not available in sufficient quantity. Fracture characteristics of notched and unnotched beams of plain self consolidating concrete using acoustic emission to understand the localization of crack patterns at different stages has been done. Considering this as a platform, further analysis has been done using moment tensor analysis as a new notion to evaluate fracture characteristics in terms of crack orientation, direction of crack propagation at nano and micro levels. Analysis of B-value (b-value based on energy) is also carried out, and this has introduced to a new idea of carrying out the analysis on the basis of energy which gives a clear picture of results when compared with the analysis carried out using amplitudes. Further a new concept is introduced to analyze crack smaller than micro (could be hepto cracks) in solid materials. Each crack formation corresponds to an AE event and is processed and analyzed for crack orientation, crack volume at hepto and micro levels using moment tensor analysis based on energy. Cracks which are tinier than microcracks (could be hepto), are formed in large numbers at very early stages of loading prior to peak load. The volume of hepto and micro cracks is difficult to measure physically, but could be characterized using AE data in moment tensor analysis based on energy. It is conjectured that the ratio of the volume of hepto to that of micro could reach a critical value which could be an indicator of onset of microcracks after the formation of hepto cracks.