Effective non-invasive runway monitoring system development using dual sensor devices

At airports the runways are always troubled by the presence of ice, water, cracks, foreign objects, etc. To avoid such problems the runway is supposed to be monitored regularly. To monitor a large number of technique are available such a runway inspection mobile vans. These techniques are largely human dependent and need interruptions in the runway’s operations for inspection. In this position paper, we suggest an alternative way to monitor the runway. This method is non-invasive in nature with the involvement of Light Detection and Ranging (LIDAR) sensors. In the methodology, we describe the schemes of labelling the data obtained from LIDAR using a MARWIS sensors fitted in a mobile van. We describe the entire system and the underlying technology involved to develop the system. The proposed system has the potential of developing an efficient runway monitoring system because the LIDAR technology has proved its efficiency in several terrestrial mapping and monitoring system.info:eu-repo/semantics/acceptedVersio

An effective identification of crop diseases using faster region based convolutional neural network and expert systems

The majority of research Study is moving towards cognitive computing, ubiquitous computing, internet of things (IoT) which focus on some of the real time applications like smart cities, smart agriculture, wearable smart devices. The objective of the research in this paper is to integrate the image processing strategies to the smart agriculture techniques to help the farmers to use the latest innovations of technology in order to resolve the issues of crops like infections or diseases to their crops which may be due to bugs or due to climatic conditions or may be due to soil consistency. As IoT is playing a crucial role in smart agriculture, the concept of infection recognition using object recognition the image processing strategy can help out the farmers greatly without making them to learn much about the technology and also helps them to sort out the issues with respect to crop. In this paper, an attempt of integrating kissan application with expert systems and image processing is made in order to help the farmers to have an immediate solution for the problem identified in a crop

Noncontact sensing systems and autonomous decision-making for early-age concrete

Early-age cracking and spalling in concrete pavements reduces slab capacity, joint load transfer, ride quality, and its long-term performance. These premature distresses lead to increased maintenance costs for sealing, patching, and grinding. Proper timing of sawcutting and curing are two construction activities that can minimize early-age distress development. In order to better time sawcutting and curing activities, an improved method to spatially monitor the setting time of concrete is required. Likewise, rapid evaluation of the joint quality after sawing is also necessary to provide feedback to adjust the timing. While previous methods for sawcutting and curing are experiential and subjective, this research aims to develop contactless sensing and computer vision techniques to significantly improve the timing of certain early-age concrete construction activity decisions through quantitative indicators. A non-contact, ultrasonic testing system (UTS) to monitor concrete set time has been developed by monitoring the evolution of leaky Rayleigh (LR-wave) wave signals over time and space (surface of the concrete). The non-contact UTS integrates a 50 kHz non-contact ultrasonic transmitter and an array of five microelectromechanical systems (MEMS) sensors as non-contact receivers. The UTS technique was first implemented in the laboratory at incident angles of 12^° for mortar mixtures in order to determine the final setting times. The UTS technique was also applied at different incident angles (12^° to 60^° ) on a mortar mixture to evaluate its influence of the angle on the UTS measurement. The final setting times for mortars were consistent with the ASTM C403 penetration resistance standard when an incident angle of 12^° was used. Additionally, this UTS was successfully field validated on three concrete pavement test sections in Illinois that had different casting times during the day. Final setting times in the field greatly varied (287 to 210 minutes) given the higher ambient temperatures and surrounding concrete mass. In order to improve decision-making on sawcut timing, the final set times measured by the UTS were linked with the earliest time to initiate sawcutting within an acceptable level of raveling. A computer vision-based (CV) process was developed that employed multiple joint images, 2D segmentation for joint raveling/spalling extraction, 3D point cloud reconstruction and meshing of the joint damage, and a 3D damage quantification analysis for assessing the joint damage. The proposed CV-based joint damage analysis quantified joint damage through two newly defined indices: (i) raveling damage index (RDI) for raveling and (ii) joint damage index (JDI) for spalling. The proposed CV-based method had an accuracy of 76% with an error of 10%. With this CV-based process, it was determined that RDI of 3% or less is an acceptable quality level for contraction joints in the field. A one-sided multi-sensor ultrasonic array device with a support vector machine algorithm was developed that detects the existence of a concealed, vertical crack beneath a notched contraction joint. This algorithm supports the field assessment of the effectiveness of sawcut timing, sawcut depth, and whether premature slab cracking was related to poor sawing procedures. The multi-sensor ultrasonic array device generated and received ultrasonic shear waves (S-wave) across the inspected joint. The acquired time domain signals were used to calculate normalized transmission energy (NTE) across the joint. The NTE algorithm defined the ratio of the energy of diffracted and reflected S-waves received behind the joint with respect to the energy of direct, diffracted, and reflected S-waves received in front of the joint. Laboratory results demonstrated that the NTE technique could successfully identify the existence or non-existence of a crack beneath the sawcut. Finally, the NTE technique coupled with a 2D decision boundary equation was field validated on 152 concrete pavement contraction joints from multiple projects with similar slab thicknesses and sawcut notch depths in Illinois and Iowa. Finally, the non-contact UTS was coupled with a 2D wavefield analysis to rapidly evaluate the effectiveness, spatially and with time, of curing methods through monitoring of the near-surface damage in hydrating paste at early-ages. The new technique monitored the energy of the LR-waves signal over time with the non-contact UTS and then, analyzed the frequency-wave number (f-k) domain to characterize the quantity of near-surface damage in the cement paste specimens. An ultrasonic surface damage index (USDI) was defined from the f-k wavefield domain based on the ratio of the non-propagating and forwarding LR-wave energy. The non-contact sensing and 2D wavefield analysis easily distinguished the differences in surface damage between the different curing methods (no curing surface, the plastic sheet cover cure, and the wax-based curing). Surfaces with low surface damage had negligible non-propagating wave energy, which was seen in the wax-based curing specimens and the unexposed bottom surfaces of all cast specimens

Proceedings of the 8th International Conference on Civil Engineering

This open access book is a collection of accepted papers from the 8th International Conference on Civil Engineering (ICCE2021). Researchers and engineers have discussed and presented around three major topics, i.e., construction and structural mechanics, building materials, and transportation and traffic. The content provide new ideas and practical experiences for both scientists and professionals

Region Based CNN for Foreign Object Debris Detection on Airfield Pavement

In this paper, a novel algorithm based on convolutional neural network (CNN) is proposed to detect foreign object debris (FOD) based on optical imaging sensors. It contains two modules, the improved region proposal network (RPN) and spatial transformer network (STN) based CNN classifier. In the improved RPN, some extra select rules are designed and deployed to generate high quality candidates with fewer numbers. Moreover, the efficiency of CNN detector is significantly improved by introducing STN layer. Compared to faster R-CNN and single shot multiBox detector (SSD), the proposed algorithm achieves better result for FOD detection on airfield pavement in the experiment