The high demand of tensile strength in concrete is always a critical issue for engineers, as 10% of the compressive strength is not sufficient to withstand higher loadings. Lesser ductility and strain capacity is another major issue of normal concrete. In the queue of modern researches, this paper is an attempt to study Engineered Cementitious Composite (ECC) from research of Professor Victor Li, the University of Michigan. ECC is an ultra-ductile cementitious composite which is highly crack resistant, with a high tensile strain capacity over that of normal concrete. The composite replaces coarse aggregates and fine aggregates by sand and fly ash respectively. ECC is made up of OPC, sand (passing from 250 µm and retained on 150µm), Fly Ash (Class F) with addition of Polypropylene fiber on different percentages i.e. 0%, 0.25%, 0.5%, 0.75%, 1.0% were studied. Tensile Strength of ECC was measured by casting & testing cylinders of 4”x 8” in Universal Testing Machine (UTM). The experimental results revealed that 111.40% increment in tensile strength was found at 0.5% PP fiber at ECC 1:1:1 and an increment of 74.74% was observed at ECC 1:0.8:1.2 at 1% PP fiber. The study concludes that this composite could substitute the normal concrete where high tension is the ultimate requirement with higher strain capacity.

Induction motor plays a major role in industry. Despite of its strong structure, induction motors are often prone to faults. There are different types of faults that occurs in the induction motor such as bearing faults, winding faults, etc. Thus motors in major applications require continuous and effective monitoring. In this paper, a stand-alone and non-invasive condition monitoring system that can monitor the condition of 3-phase induction motor using motor current signatures with aid of deep learning (DL) approaches. The proposed system extracts the features using non-invasive current sensors it further samples the features using an analog to digital converter (ADC) and organizes the data acquired from ADC using Raspberry-pi microcomputer. The current data acquired from induction motor is used to train and test the DL models including Multilayer Perceptron (MLP), Long Short-term Memory (LSTM), and One-Dimensional Convolutional Neural Networks (1DCNN). The comparative analysis is demonstrated and the LSTM model as best fault classifier among all with accuracy up to 100%. Finally, the real-time testing of the device showed that the developed system can effectively monitor the conditions of motor using non-invasive current sensors.