Productivity Measurement through IMU-Based Detailed Activity Recognition Using Machine Learning: A Case Study of Masonry Work.

Although measuring worker productivity is crucial, the measurement of the productivity of each worker is challenging due to their dispersion across various construction jobsites. This paper presents a framework for measuring productivity based on an inertial measurement unit (IMU) and activity classification. Two deep learning algorithms and three sensor combinations were utilized to identify and analyze the feasibility of the framework in masonry work. Using the proposed method, worker activity classification could be performed with a maximum accuracy of 96.70% using the convolutional neural network model with multiple sensors, and a minimum accuracy of 72.11% using the long short-term memory (LSTM) model with a single sensor. Productivity could be measured with an accuracy of up to 96.47%. The main contributions of this study are the proposal of a method for classifying detailed activities and an exploration of the effect of the number of IMU sensors used in measuring worker productivity.

Developing Human-Centered Urban Digital Twins for Community Infrastructure Resilience: A Research Agenda.

Urban digital twins (UDTs) have been identified as a potential technology to achieve digital transformative positive urban change through landscape architecture and urban planning. However, how this new technology will influence community resilience and adaptation planning is currently unclear. This article: (1) offers a scoping review of existing studies constructing UDTs, (2) identifies challenges and opportunities of UDT technologies for community adaptation planning, and (3) develops a conceptual framework of UDTs for community infrastructure resilience. This article highlights the need for integrating multi-agent interactions, artificial intelligence, and coupled natural-physical-social systems into a human-centered UDTs framework to improve community infrastructure resilience.

Simulating the Spatial Impacts of a Coastal Barrier in Galveston Island, Texas: A Three-Dimensional Urban Modeling Approach.

Due to its vulnerability to hurricanes, Galveston Island, TX, USA, is exploring the implementation of a coastal surge barrier (also referred to as the "Ike Dike") for protection from severe flood events. This research evaluates the predicted effects that the coastal spine will have across four different storm scenarios, including a Hurricane Ike scenario and 10-year, 100-year, and 500-year storm events with and without a 2.4ft. sea level rise (SLR). To achieve this, we develop a 1:1 ratio, 3-dimensional urban model and ran real-time flood projections using ADCIRC model data with and without the coastal barrier in place. Findings show that inundated area and property damages due to flooding will both significantly decrease if the coastal spine is implemented, with a 36% decrease in the inundated area and $4 billion less in property damage across all storm scenarios, on average. When including SLR, the amount of protection of the Ike Dike diminishes due to flooding from the bay side of the island. While the Ike Dike does appear to offer substantial protection from flooding in the short term, integrating the coastal barrier with other non-structural mechanisms would facilitate more long-term protection when considering SLR.

Location-based collective distress using large-scale biosignals in real life for walkable built environments.

Biosignals from wearable sensors have shown great potential for capturing environmental distress that pedestrians experience from negative stimuli (e.g., abandoned houses, poorly maintained sidewalks, graffiti, and so forth). This physiological monitoring approach in an ambulatory setting can mitigate the subjectivity and reliability concerns of traditional self-reported surveys and field audits. However, to date, most prior work has been conducted in a controlled setting and there has been little investigation into utilizing biosignals captured in real-life settings. This research examines the usability of biosignals (electrodermal activity, gait patterns, and heart rate) acquired from real-life settings to capture the environmental distress experienced by pedestrians. We collected and analyzed geocoded biosignals and self-reported stimuli information in real-life settings. Data was analyzed using spatial methods with statistical and machine learning models. Results show that the machine learning algorithm predicted location-based collective distress of pedestrians with 80% accuracy, showing statistical associations between biosignals and the self-reported stimuli. This method is expected to advance our ability to sense and react to not only built environmental issues but also urban dynamics and emergent events, which together will open valuable new opportunities to integrate human biological and physiological data streams into future built environments and/or walkability assessment applications.

Use of Connected Technologies to Assess Barriers and Stressors for Age and Disability-Friendly Communities.

Background: The benefits of engaging in outdoor physical activity are numerous for older adults. However, previous work on outdoor monitoring of physical activities did not sufficiently identify how older adults characterize and respond to diverse elements of urban built environments, including structural characteristics, safety attributes, and aesthetics. Objective: To synthesize emerging multidisciplinary trends on the use of connected technologies to assess environmental barriers and stressors among older adults and for persons with disability. Methods: A multidisciplinary overview and literature synthesis. Results: First, we review measurement and monitoring of outdoor physical activity in community environments and during transport using wearable sensing technologies, their contextualization and using smartphone-based applications. We describe physiological responses (e.g., gait patterns, electrodermal activity, brain activity, and heart rate), stressors and physical barriers during outdoor physical activity. Second, we review the use of visual data (e.g., Google street images, Street score) and machine learning algorithms to assess physical (e.g., walkability) and emotional stressors (e.g., stress) in community environments and their impact on human perception. Third, we synthesize the challenges and limitations of using real-time smartphone-based data on driving behavior, incompatibility with software data platforms, and the potential for such data to be confounded by environmental signals in older adults. Lastly, we summarize alternative modes of transport for older adults and for persons with disability. Conclusion: Environmental design for connected technologies, interventions to promote independence and mobility, and to reduce barriers and stressors, likely requires smart connected age and disability-friendly communities and cities.

A Framework of Human-Motion Based Structural Dynamics Simulation Using Mobile Devices.

Due to the nature of real-world problems in civil engineering, students have had limited hands-on experiences in structural dynamics classes. To address this challenge, this paper aims to bring real-world problems in structural dynamics into classrooms through a new interactive learning tool that promotes physical interaction among students and enhances their engagement in classrooms. The main contribution is to develop and test a new interactive computing system that simulates structural dynamics by integrating a dynamic model of a structure with multimodal sensory data obtained from mobile devices. This framework involves integrating multiple physical components, estimating students' motions, applying these motions as inputs to a structural model for structural dynamics, and providing students with an interactive response to observe how a given structure behaves. The mobile devices will capture dynamic movements of the students in real-time and take them as inputs to the dynamic model of the structure, which will virtually simulate structural dynamics affected by moving players. Each component of synchronizing the dynamic analysis with motion sensing is tested through case studies. The experimental results promise the potential to enable complex theoretical knowledge in structural dynamics to be more approachable, leading to more in-depth learning and memorable educational experiences in classrooms.