An Effective Crack Identification in Civil Infrastructure with IoT and Improved Convolutional Neural Network

Crack identification of buildings using the Internet of things (IoT) is done by continuously monitoring the building structures that provide an early indication of cracks in buildings. The established IoT system constantly gathers structural information using sensors and stores it on a cloud server. This paper presented an innovative machine learning crack identification methodology for detecting cracks using the sensor data. Initially, the collected sensor data is pre-processed by the cloud server using the data fusion process for further processing. Subsequently, effective damage sensitive features such as mode structure (MS) features such as damage signature, streamlined damage signature index, modal assurance criterion (MAC) and coordinate MAC, improved natural frequency (INF) features, and mode structure curvature (MSC) features with curvature damage factor are extracted from the pre-processed data to differentiate cracks easily. After features are extracted, the feature score-based random projection (FSRP) technique is utilized for dimensionality reduction. Finally, hybridization of improved convolutional neural network with modified whale optimization (ICNN-MWO) detects the cracks in the civil structure utilizing the selected features. These effective classification results might alert the user when a high severity or damage is likely to occur. The implementation platform used in this work is PYTHON. The experimental outcomes of the presented technique proved that the presented work is significantly better in terms of various effective performance measures like accuracy (99.93%), mean squared error (3%), precision (99.91%), recall (99.90%), and F-measure (99.9%). The experimental results of the presented methodology provide improved performance than the existing crack identification techniques.