Wireless Sensor Networks Composed of Standard Microcomputers and Smartphones for Applications in Structural Health Monitoring

Abstract

Wireless sensor networks have attracted great attention for applications in structural health monitoring due to their ease of use, flexibility of deployment, and cost-effectiveness. This paper presents a software framework for WiFi-based wireless sensor networks composed of low-cost mass market single-board computers. A number of specific system-level software components were developed to enable robust data acquisition, data processing, sensor network communication, and timing with a focus on structural health monitoring (SHM) applications. The framework was validated on Raspberry Pi computers, and its performance was studied in detail. The paper presents several characteristics of the measurement quality such as sampling accuracy and time synchronization and discusses the specific limitations of the system. The implementation includes a complementary smartphone application that is utilized for data acquisition, visualization, and analysis. A prototypical implementation further demonstrates the feasibility of integrating smartphones as data acquisition nodes into the network, utilizing their internal sensors. The measurement system was employed in several monitoring campaigns, three of which are documented in detail. The suitability of the system is evaluated based on comparisons of target quantities with reference measurements. The results indicate that the presented system can robustly achieve a measurement performance commensurate with that required in many typical SHM tasks such as modal identification. As such, it represents a cost-effective alternative to more traditional monitoring solutions.

Datenmodelle für digitale Zwillinge von Brücken und bildbasierte Zustandsaufnahmen

“…Die Unterstützung von Inspektionen durch moderne Technologien, wie unbemannte Flugsysteme (engl. : Unmanned Aircraft Systems, UAS) [3] oder Sensor-Netzwerke [4], erzeugt große Datenmengen an Bildern, Orthofotos, 3-D-Punktwolken und -Oberflächennetzen bzw. an gemessenen Zeitreihen, welche in konventionellen Ablagemethoden, 2-D-Plänen oder Datenmodellen nur schwer oder gar nicht verarbeitbar sind und somit ihr Potenzial in der Bauwerkserhaltung nicht ausgeschöpft wird.…”

Health monitoring of bridges

“…Morgenthal et al (2018) proposed to employ offthe-shelf electronic components such as Raspberry Pi and MPU-6050 accelerometer, which costs V5, to build a low-cost WSN. Accurate cable dynamic properties and forces as well as mode shapes of horizontal structures can be calculated from accelerations collected with the Raspberry Pi-based monitoring system (Morgenthal et al, 2018(Morgenthal et al, , 2019a. A single node in the system costs below V50.…”

Piezoceramic-Based Damage Monitoring of Concrete Structure for Underwater Blasting

“…In recent years, with the increasing demands for structural-damage detection [17,18], structural health monitoring (SHM) has rapidly advanced [19,20], and piezoelectric transducers are widely used in structural health and damage detection [21,22]. They have low cost, broadband-frequency response [23][24][25], energy-harvesting capacity [26,27] and the ability to function as both actuators and sensors [28,29].…”