DOI: 10.5061/dryad.15dv41p49
title: Commodifying infrastructure spatial dynamics with crowdsourced smartphone data

description: Structural information deficits about our aging bridges have led to
 several avoidable catastrophes in recent years. Data-driven methods for
 bridge vibration monitoring enable frequent, accurate structural
 assessments; however, the high costs of large-scale deployments of these
 systems make important condition information a luxury for bridge owners.
 Smartphone-based monitoring is inexpensive yet has produced structural
 information, i.e., modal frequencies, in crowdsensing applications.
 However, current methods cannot extract spatial vibration characteristics,
 which are needed for damage identification. Here we present the most
 extensive real-world study on bridge monitoring with crowdsourced
 smartphone-vehicle trips and simulate damage detection capabilities. Our
 method analyzes over 500 trips across four bridges with main spans ranging
 from 30 to 1300 meters in length, representing about one-quarter of US
 bridges, and extracts absolute value mode shapes, a damage-sensitive
 feature. We demonstrate a bridge health monitoring platform compatible
 with ride-sourcing data streams that check conditions daily. The result is
 the potential to commodify data-driven structural assessments globally.
description: This data set was collected from various sources: the research
 team, ANAS employees, and Uber drivers. The method for data collection and
 data processing for each dataset can be found in the related
 works. 
description: # Commodifying infrastructure spatial dynamics with crowdsourced
 smartphone data
 [https://doi.org/10.5061/dryad.15dv41p49](https://doi.org/10.5061/dryad.15dv41p49) The dataset consists of acceleration and position measurements taken using smartphones as vehicles drove across various bridges. Current projects using this data revolve around mobile sensing and system identification. The accelerations were measured using the smartphone's onboard triaxial accelerometer. However, the position measurements are a process version of the GPS measurements. The position measurements are 1D distance from a bridge pier, measured in meters, rather than the raw 2D GPS measurements from the phone. Within this repository, there are multiple case studies, and between cases, the method of data collection varied. The methods for data collection can be found in the related work.  A Jupyter Notebook accompanies the datasets to reproduce the Golden Gate Bridge absolute mode shape results in the related work. ## Description of the data and file structure The data is saved in MAT file format and can be accessed using MATLAB or various Python libraries. The file names are labeled with the bridge identifier in the first three digits, and trip index in the second three digits. In each file, the acceleration measurements are saved in a variable called "aOutput," a matrix 3xN where three rows represent the three axes of the accelerometer. Next, the position measurements are saved in a variable named "S_x," a 1xN matrix. Last, there is a variable named "dt" for the timestep of the measurements. These are the necessary variables to replicate the results in the associated works. The bridge ID and variables in the respective mat files are listed below: ggb_params.npy * Parameters saved in a python dictionary to recreate controlled GGB results in JupyterNotebook supplied in repository  Cadore (Bridge ID: 332) * aOutput * S_x * dt Ciampino (Bridge ID: 346) * aOutput * S_x * dt GGB Controlled (Bridge ID: 198) * aOutput * S_x * dt * uuid, seq, coverage, bridgeId (misc. indexes and variables used by research team, type sting or float) GHMB (Bridge ID: 334) * aOutput * S_x * dt * uuid, seq, coverage, bridgeId, date, phoneID, start_time* *(misc. indexes and variables used by research team, type sting or float) UBER (Bridge ID: 298) * aOutput * S_x * dt ## Code The Jupyter Notebook supplied with the code is self-contained and can be run using Google Colab. To run the code, simply upload the code and data into google drive. Open the notebook using the Google application Google Colab and follow the instructions within the notebook. The "ggb_params.npy" is included in the repository to replicate the controlled GGB absolute mode shape results.