Thomas Matarazzo

Massachusetts Institute of Technology | MIT · Department of Urban Studies and Planning

A large number of vehicles routinely navigate through city streets; with on-board sensors, they can be transformed into a dynamic network that monitors the urban environment comprehensively and efficiently. In this paper, drive-by approaches are discussed as a form of mobile sensing, that offer a number of advantages over more traditional sensing a...

Cities are encountering extensive deficits in infrastructure service while they are experiencing rapid technological advancements and overhauls in transportation systems. Standard bridge evaluation methods rely on visual inspections, which are infrequent and subjective, ultimately affecting the structural assessments on which maintenance plans are...

This article uses the formulation of the structural identification using expectation maximization (STRIDE) algorithm for compatibility with the truncated physical model (TPM) to enable scalable, output-only modal identification using dynamic sensor network (DSN) data. The DSN data class is an adaptable and efficient technique for storing measuremen...

There are many occasions throughout structural health monitoring (SHM) in which collected data sets contain missing observations; such instances may occur as a result of failed communications or packet losses in a wireless sensor network or as a result of sensing and sampling methods, e.g., mobile sensing. By implementing modified Expectation and M...

Historically, structural health monitoring (SHM) has relied on fixed sensors, which remain at specific locations in a structural system throughout data collection. This paper introduces state-space approaches for processing data from sensor networks with time-variant configurations, for which a novel truncated physical model (TPM) is proposed. The...

Structural information deficits about our aging bridges allowed 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. Sma...

Monitoring and managing the structural health of bridges requires expensive specialized sensor networks. In the past decade, researchers predicted that cheap ubiquitous mobile sensors would revolutionize infrastructure maintenance; yet extracting useful information in the field with sufficient precision remains challenging. Herein we report the acc...

The knowledge gap in the expected and actual conditions of bridges has created worldwide deficits in infrastructure service and funding challenges. Despite rapid advances over the past four decades, sensing technology is still not a part of bridge inspection protocols. Every time a vehicle with a mobile device passes over a bridge, there is an oppo...

There is a growing attention in real-time bridge condition assessment using data from drive-by vehicles as a potentially scalable approach. Most system identification methods are based on synchronized vibration data collection for this purpose. This study presents an approach for bridge modal identification that estimates high-resolution absolute v...

Modern bridge health monitoring methods require specialized sensor networks, which have costs that are prohibitive to bridge owners. Mobile sensor networks reduce costs by capturing vibrational signatures and indicators of structural decay using substantially fewer devices. Over the last decade, researchers have hypothesized that crowd-sourced mobi...

This study introduces a simplified model for bridge–vehicle interaction for medium- to long-span bridges subject to random traffic loads. Previous studies have focused on calculating the exact response of the vehicle or the bridge based on an interaction force derived from the compatibility between two systems. This process requires multiple iterat...

This study presents a flexible approach for bridge modal identification using smartphone data collected by a large pool of passing vehicles. With each trip of a mobile sensor, the spatio-temporal response of the bridge is sampled, plus various sources of noise, e.g., vehicle dynamics, environmental effects, and road profile. This paper provides fur...

Vehicles commuting over bridge structures respond dynamically to the bridge’s vibrations. An acceleration signal collected within a moving vehicle contains a trace of the bridge’s structural response, but also includes other sources such as the vehicle suspension system and surface roughness-induced vibrations. This paper introduces two general met...

This study introduces a simplified model for bridge-vehicle interaction for medium- to long-span bridges subject to random traffic loads. Previous studies have focused on calculating the exact response of the vehicle or the bridge based on an interaction force derived from the compatibility between two systems. This process requires multiple iterat...

Dynamic sensor networks have the potential to significantly increase the speed and scale of infrastructure monitoring. Structural health monitoring (SHM) methods have been long developed under the premise of utilizing fixed sensor networks for data acquisition. Over the past decade, applications of mobile sensor networks have emerged for bridge hea...

Vehicles crossing bridge structures respond dynamically to the bridge's vibrations. An acceleration signal collected within a moving vehicle contains a trace of the bridge's structural response, but also includes other sources such as the vehicle suspension system and surface roughness-induced vibrations. This paper introduces two methods for the b...

Top-down simulations of autonomous intersections neglect considerations for the human experience of being in cars driving through these autonomous intersections. To understand the impact that perspective has on perception of autonomous intersections, we conducted a driving simulator experiment and studied the experience in terms of perception, feel...

Quantitative reports on postearthquake infrastructure condition are valuable to engineers, building owners, local government entities, and the public. Despite their utility, sensor-based protocols have not been widely embraced, primarily due to a lack of proper demonstrations. This paper introduces a methodology for determining the postearthquake s...

Knowledge on the dynamic properties of bridges in a city can improve condition assessments, maintenance scheduling, and emergency planning to better serve the public. Currently, bridge vibration data is obtained primarily by researchers through the use of a sophisticated sensor network that is composed of fixed sensor nodes. Recent studies have sup...

This paper studies how a particular variation in a wireless mobile sensor configuration can influence modal identification accuracy. A mobile sensor network simultaneously measures vibration data in time while scanning over a large set of points in space. Previous research has demonstrated that such data can be specified under the dynamic sensor ne...

In SHM, fixed sensor networks with long-term monitoring capabilities, dense sensor arrays, or high sampling rates are perceived to produce BIGDATA. As the temporal and spatial resolution of monitoring data is improved by advances in sensing technology and with the adaptation of new data collection techniques, it is expected that efficient BIGDATA a...

The spatial information within data recorded by a fixed sensor network is restricted by the number of sensors available and the sensor configuration. Mobile sensor networks reduce setup and equipment efforts by simultaneously recording data in time while moving in space. It has been proven that a mobile sensor network produces superior spatial info...

Output-only system identification methods assist engineers in determining operational modal properties of constructed structural systems. Sensors and data acquisition systems have a finite reliability; even state-of-the-art technologies are susceptible to missing packets, erroneous values, and other malfunctions. It is expected that missing data po...

Structural health monitoring (SHM) procedures such as system identification (SID) or damage detection were originally developed to process data from fixed sensor networks. Interest in mobile sensors has recently grown with the popularity of mobile devices and smartphones. As documented in the literature, a mobile sensor network is capable of collec...

This paper discusses data from sensor networks with time-variant configurations called dynamic sensor network (DSN) data, which have a higher capacity for storing spatial information than fixed sensor data. DSN datasets are applicable to high-resolution mobile sensor networks and the processing of BIGDATA. The defining attribute of these data matri...

In SHM, BIGDATA is currently perceived as the result of special applications such as long-term monitoring, dense sensor arrays, or high sampling rates. Along the development of novel sensing techniques as well as advances in sensing devices and data acquisition technology, it is expected that BIGDATA will become more easily obtained. In a previous...

Modern system identification (SID) procedures rely on fixed sensor networks for data collection. Ideally, sensors are fixed at locations and contain profitable structural responses, however, such sensing areas are often limited by the accessibility of the structure and environmental hazards. Not only are fixed sensor placements limited, the data co...

This paper introduces a set of sensitivity metrics to be used along likelihood-based modal identification methods. In maximum likelihood (ML) estimation theory, the precision of ML point estimates can be measured by the curvature of the likelihood function. This paper presents closed-form partial derivatives, observed information, and variance expr...

This paper introduces structural identification using expectation maximization (STRIDE), a novel application of the expectation maximization (EM) algorithm and approach for output-only modal identification. The EM algorithm can be used to estimate the maximum likelihood parameters of a state-space model. In this context, the state-space model repre...

Large SHM datasets often result from special applications such as long-term monitoring, dense sensor arrays, or high sampling rates. Through the development of novel sensing techniques as well as advances in sensing devices and data acquisition technology, it is expected that such large volumes of measurement data become commonplace. In anticipatio...

This paper presents an application of a novel data collection method: mobile sensing. Mobile sensor networks can provide extensive information similar to dense fixed sensor networks while conserving the ease of smaller networks. However, mobile sensing data is expected to have missing observations in time and space, leaving data matrices incompatib...

This paper will address a main concern in using mobile sensor data for SHM. Mobile sensors can offer comprehensive data similar to a “maximum fixed sensor networks” while keeping the simplicity of “minimum fixed sensor networks”. However, the data is expected to have an undesirable incompleteness feature and would not be readily compatible with mod...

System identification algorithms currently require a full data set, i.e., no missing observations, to estimate the natural vibration properties of a structural system. These algorithms are often based on parameters estimated from a state-space model. There are circumstances in which a Missing Data Problem can arise during data collection; therefore...