Smartphone Prospects in Bridge Structural Health Monitoring, a Literature Review

Abstract

Bridges are critical components of transportation networks, and their conditions have effects on societal well-being, the economy, and the environment. Automation needs in inspections and maintenance have made structural health monitoring (SHM) systems a key research pillar to assess bridge safety/health. The last decade brought a boom in innovative bridge SHM applications with the rise in next-generation smart and mobile technologies. A key advancement within this direction is smartphones with their sensory usage as SHM devices. This focused review reports recent advances in bridge SHM backed by smartphone sensor technologies and provides case studies on bridge SHM applications. The review includes model-based and data-driven SHM prospects utilizing smartphones as the sensing and acquisition portal and conveys three distinct messages in terms of the technological domain and level of mobility: (i) vibration-based dynamic identification and damage-detection approaches; (ii) deformation and condition monitoring empowered by computer vision-based measurement capabilities; (iii) drive-by or pedestrianized bridge monitoring approaches, and miscellaneous SHM applications with unconventional/emerging technological features and new research domains. The review is intended to bring together bridge engineering, SHM, and sensor technology audiences with decade-long multidisciplinary experience observed within the smartphone-based SHM theme and presents exemplary cases referring to a variety of levels of mobility.

Keywords: bridge dynamics; computer vision; damage detection; dynamic identification; level of mobility (LoM); mobile sensing; modal analysis; sensor technologies; signal processing; smartphones; structural health monitoring; vision-based sensing.

Figure 1

A timeline of smartphone evolution from its advent to complex and large-scale SHM research (2007 to 2024), examples above refer to in [27,28,29,31,32,33,34,35,36,37,38,39,40,42], respectively.

Figure 2

Level of mobilities (LoMs) observed on a smartphone-engaged bridge monitoring ecosystem.

Figure 3

Spatial footprint comparison for LoMs.

Figure 4

Conceptual breakdown of smartphone components.

Figure 5

Bridge cable installation of smartphone sensor: smartphone vibration frequency comparisons with the reference data (a,b) and sensor configuration (c), Yu et al. (2015) [32].

Figure 6

Image frame with an ROI (left) and ROI with a target (right) [82].

Figure 7

A vehicle–bridge interaction model used for simulating the indirect response, Zhu and Malekjafarian (2019) [120].

Figure 8

CV-based system for measurement collection of the Wilford Suspension bridge.

Figure 9

A sketch of the Wilford Suspension bridge with the locations of targets (T$i$, where $i=1, 2, \dots , 9$).

Figure 10

Vertical displacement time-history for the selected activity (left) and power spectrum density plot (right).

Figure 11

The mode shape of the bridge at its first vertical frequency.

Figure 12

(a) Mudd–Schapiro Bridge, (b) accelerometer instrumentation, (c) sample smartphone measurement, (d) its frequency spectrum, and (e) and the comparison of crowdsourcing-identified modal frequency values with reference tests [34].

Figure 13

Smartphone-based SHM frameworks for (a) spatiotemporally uncontrolled [55] and (b) directionally distorted [56] and (c) and indirectly retrieved vibration data [57].

Figure 14

(a) Overall distribution of smartphone-based bridge monitoring sample studies according to their LoM context; (b–d) yearly publication trends among LoM1, LoM2, and LoM3, respectively.

Figure 15

A vision of collective and connected smartphone applications for bridge SHM.