Bridge Health Monitoring via Displacement Reconstruction-Based NB-IoT Technology

Abstract

An aged bridge’s performance is affected by degradation and becomes one of the major concerns in maintenance. A preliminary, simple and workable procedure of bridge damage detection is required to minimize maintenance costs. In the past, frequency is one of the most common indicators to detect damage occurrence. Recent research found that using frequency as a health indicator still has room to improve. Alternatively, dynamic displacement is used as an indicator in the current study. These dynamic displacements are reconstructed based on measured acceleration records from micro electro mechanical system (MEMS) sensors. The Newmark-beta method with Windows is proposed to acquire the reconstructed displacements of considered bridges. To demonstrate the accuracy and applicability of the proposed approach, three different experiments are carried out; (i) A small scale bridge with the implementation of MEMS acceleration sensors; (ii) a numerical complex finite element method (FEM) bridge model; (iii) an actual bridge with the implementation of MEMS acceleration sensors and narrow bandwidth Internet of things (NB-IoT) technology. The first experiment shows that the proposed method can successfully identify the difference between damaged/undamaged bridges and determine damage location. The second experiment indicates that the proposed method is able to identify the difference between stiffened/unstiffened bridges. The last experiment shows the applicability of the proposed method on an actual bridge health monitoring project.

Novel method for identifying residual prestress force in simply supported concrete girder-bridges

“…By considering that a f 1,exp of 15.62 Hz was identified at 291 days of prestressing (Figure 4(b)), that is, when N 0x,aver = 515 kN, f 1,exp only increased by 0.5% despite the long-term losses of prestress force (N 0x,aver ) by almost 7%. This confirmed the fundamental frequency f 1,exp as an uncertain indicator of prestress losses (Thedy et al, 2020). Tables 3 and 4 compare the experimental values of the cross-sectional second moment of the area (I 1I,exp ) (equation ( 2) with f 1,exp ) with the reference ones (I 1I ) obtained from the solution proposed by Song, as explained in According to the solution proposed by Song.…”

“…Conversely, dynamic nondestructive methods are unsuitable because a change in prestress force does not significantly influence the PC girder-bridges' vibrational response (Bonopera et al, 2018a(Bonopera et al, , 2019a. This makes the fundamental frequency as an uncertain indicator of prestress losses (Thedy et al, 2020). However, dynamic methods require an accurate selection of the mode shape because different natural frequencies provide varying degrees of accuracy in prestress force estimations.…”

Design and Implementation of a Framework for Smart Home Automation Based on Cellular IoT, MQTT, and Serverless Functions

“…It provides extended coverage to delay-tolerant, low-cost, low-energy IoT applications, reaching good energy performance even in the worse-case, continuous transmission scenarios when compared to other communication protocols [17]. It represents a valuable choice for application fields that require massive deployments, good coverage and low data rates, including smart metering [33], smart grids [34], smart cities [35], healthcare [36][37][38], and industrial applications [39]. To the best of the authors' knowledge, it has still been seldom applied in home automation [40], mostly in home surveillance or metering applications [41,42], and rarely using MQTT.…”