ORCID

Structural health monitoring (SHM) techniques have been widely used in long-span bridges. However, due to limitations of computational ability and data analysis methods, the knowledge in massive SHM data is not well interpreted. Big data (BD) and artificial intelligence (AI) techniques are seen as promising ways to address the data interpretation problem. This paper aims to clarify the scope of BD and AI techniques on what and how regarding bridge SHM. The BD and AI techniques are summarized, and the requirements of bridge SHM for new techniques are generalized. Applications of BD and AI techniques in bridge SHM are reviewed, respectively. BD techniques can be divided into two categories, namely computing techniques and data analysis methods. The computing techniques are employed in SHM to build a BD-oriented SHM framework and to address computing problems, while the data analysis methods are introduced under a pipeline of BD analysis, application scenarios of BD techniques in bridge SHM are proposed in each step of this pipeline. The state of the art of deep learning in SHM is introduced to represent AI applications, which are concerned with processing unstructured data for visual inspection and time series for structural damage detection. Finally, the upper limit, challenges, and future trends are discussed. As a review, the paper offers meaningful perspectives and suggestions for employing BD and AI techniques in the field of bridge SHM.

Despite its growing ubiquitous presence, the smart city continues to struggle for definitional clarity and practical import. In response, this study interrogates the smart city as global discourse network by examining a collection of key texts associated with cities worldwide. Using a list of 5,553 cities, a systematic webometric exercise was conducted to measure hit counts produced by searching for “smart city.” Consequently, 27 cities with the highest validated hit counts were selected. Next, 346 online texts were collected from among the top 20 hits across each of the selected cities, and analyzed both quantitatively and qualitatively using AntConc software. The findings confirm, first, the presence of a strong globalizing narrative which emphasizes world cities as “best practice” models. Second, they reveal the smart city’s association—beyond the quest for incremental, technical improvements of current urban systems and processes—with a pronounced transformative governance agenda. The article identifies five critical junctures at the heart of the evolving smart city discourse regime; these shed light on the ongoing boundary work in which the smart city is engaged and which contain significant unresolved tensions. The paper concludes with a discussion of resulting implications for research, policy, and practice.

RC shear walls are commonly used as lateral load-resisting elements in seismic regions, and the estimation of their shear strengths can become simultaneously design-critical and complex when they have so-called squat geometries, i.e., height-to-length ratios less than two. This paper presents a study on the training and interpretation of an advanced machine-learning model that strategically combines two algorithms for the said purpose. To train the model, a comprehensive shear strength database of 434 samples of squat RC walls is utilized. First, the eXtreme Gradient Boosting (XGBoost) algorithm is used to establish a predictive model for estimating the shear strength, wherein 70% and 30% of the data are respectively used for training and validation. This effort resulted in an approximately 97% validation accuracy, which well exceeds current mechanics-based/semiempirical models. Second, the SHapley Additive exPlanations (SHAP) algorithm is used to estimate the relative importance of the factors affecting XGBoost’s shear strength estimates. This step thus enabled physical and quantitative interpretations of the input-output dependencies, which are nominally hidden in conventional machine-learning approaches. Through this setup, several squat wall attributes are identified as being critical in shear strength estimates.

With the advancement of machine learning (ML) and deep learning (DL), there is a great opportunity to enhance the development of automatic crack detection algorithms. In this paper, the authors organize and provide up-to-date information on on ML-based crack detection algorithms for researchers to more efficiently seek potential focus and direction. The authors first reviewed 68 ML-based crack detection methods to identify the current trend of development, pixel-level crack segmentation. The authors then conducted a performance evaluation on 8 ML-based crack segmentation models using consistent evaluation metrics and three-dimensional (3D) pavement images with diverse conditions to identify remaining challenges and potential directions for future development. Based on the comparison results, deeper backbone networks in FCN models and skip connections in U-Net both improved the performance. Within different categories of pavement images, except for the Other Distress category, FCN and U-Net scored over 90 on the enhanced Hausdorff distance metric. Results showed that solving the false-positive problem is an important step in further improving ML-based crack detection models.

Problem, research strategy, and findings: We draw on a multidisciplinary body of research to consider how planning for urban agriculture can foster food justice by benefitting socioeconomically disadvantaged residents. The potential social benefits of urban agriculture include increased access to food, positive health impacts, skill building, community development, and connections to broader social change efforts. The literature suggests, however, caution in automatically conflating urban agriculture’s social benefits with the goals of food justice. Urban agriculture may reinforce and deepen societal inequities by benefitting better resourced organizations and the propertied class and contributing to the displacement of lower-income households. The precariousness of land access for urban agriculture is another limitation, particularly for disadvantaged communities. Planners have recently begun to pay increased attention to urban agriculture but should more explicitly support the goals of food justice in their urban agriculture policies and programs.Takeaway for practice: We suggest several key strategies for planners to more explicitly orient their urban agriculture efforts to support food justice, including prioritizing urban agriculture in long-term planning efforts, developing mutually respectful relationships with food justice organizations and urban agriculture participants from diverse backgrounds, targeting city investments in urban agriculture to benefit historically disadvantaged communities, increasing the amount of land permanently available for urban agriculture, and confronting the threats of gentrification and displacement from urban agriculture. We demonstrate how the city of Seattle (WA) used an equity lens in all of its programs to shift its urban agriculture planning to more explicitly foster food justice, providing clear examples for other cities.

Bitcoin’s emergence has the potential to pave the way for a technological revolution in financial markets. What determines its valuation is an important open question with far-reaching business and policy implications. Building on information systems and finance literature, we examine the dynamic interactions between social media and the monetary value of bitcoin using textual analysis and vector error correction models. We show that more bullish forum posts are associated with higher future bitcoin values. Interestingly, social media’s effects on bitcoin are driven primarily by the silent majority, the 95 percent of users who are less active and whose contributions amount to less than 40 percent of total messages. In addition, messages on an Internet forum, relative to tweets, have a stronger impact on future bitcoin value. Overall, our findings reveal that social media sentiment is an important predictor in determining bitcoin’s valuation, but not all social media messages are of equal impact. This study offers new insights into the digital currency market and the economic impact of social media.

Ultrahigh-performance concrete (UHPC) offers significant potential to address a variety of needs in bridge design, construction, and performance enhancement. Bridge owners have shown willingness to embrace novel solutions that could address specific challenges related to the cost, speed of construction, durability, and service life of their projects. There are hundreds of bridges worldwide that, largely in the past decade, have utilized UHPC. These applications range from minor field-cast closures to precast segments for long-span bridges to kilometer-long bridge deck overlays on a signature structure. The objective of this paper is to promote the application of this class of cementitious material in bridge engineering by presenting the progress that has been made in different regions of the world in the past two decades. Today, UHPC is being widely used in Malaysia to design and construct many bridges of different types and spans as they build out their roadway network. In South Korea, the unique characteristics of UHPC are being utilized to advance the state-of-the-art in long-span bridges. The French were early adopters and pioneers in building a strong foundation for using UHPC in a variety of bridge applications. In Switzerland, UHPC is employed to address major bridge rehabilitation needs. The United States bridge sector has embraced UHPC for a variety of field-cast connections. Current research and development efforts are promoting the use of UHPC in major rehabilitation projects and construction of primary bridge components. The adoption of UHPC solutions into the bridge sector is progressing rapidly because of the unique opportunities provided by the strength and durability of the material. It is expected that additional innovations and refinements of solutions will occur as knowledge of the material proliferates.

ABSTRACT The Open Researcher & Contributor ID (ORCID) registry presents a unique opportunity to solve the problem of author name ambiguity. At its core the value of the ORCID registry is that it crosses disciplines, organizations, and countries, linking ORCID with both existing identifier schemes as well as publications and other research activities. By supporting linkages across multiple datasets – clinical trials, publications, patents, datasets – such a registry becomes a switchboard for researchers and publishers alike in managing the dissemination of research findings. We describe use cases for embedding ORCID identifiers in manuscript submission workflows, prior work searches, manuscript citations, and repository deposition. We make recommendations for storing and displaying ORCID identifiers in publication metadata to include ORCID identifiers, with CrossRef integration as a specific example. Finally, we provide an overview of ORCID membership and integration tools and resources.

Numerical methods such as finite element have been flourishing in the past decades for modeling solid mechanics problems via solving governing partial differential equations (PDEs). A salient aspect that distinguishes these numerical methods is how they approximate the physical fields of interest. Physics-informed deep learning (PIDL) is a novel approach developed in recent years for modeling PDE solutions and shows promise to solve computational mechanics problems without using any labeled data (e.g., measurement data is unavailable). The philosophy behind it is to approximate the quantity of interest (e.g., PDE solution variables) by a deep neural network (DNN) and embed the physical law to regularize the network. To this end, training the network is equivalent to minimization of a well-designed loss function that contains the residuals of the governing PDEs as well as initial/boundary conditions (I/BCs). In this paper, we present a physics-informed neural network (PINN) with mixed-variable output to model elastodynamics problems without resort to the labeled data, in which the I/BCs are forcibly imposed. In particular, both the displacement and stress components are taken as the DNN output, inspired by the hybrid finite-element analysis, which largely improves the accuracy and the trainability of the network. Since the conventional PINN framework augments all the residual loss components in a soft manner with Lagrange multipliers, the weakly imposed I/BCs may not be well satisfied especially when complex I/BCs are present. To overcome this issue, a composite scheme of DNNs is established based on multiple single DNNs such that the I/BCs can be satisfied forcibly in a forcible manner. The proposed PINN framework is demonstrated on several numerical elasticity examples with different I/BCs, including both static and dynamic problems as well as wave propagation in truncated domains. Results show the promise of PINN in the context of computational mechanics applications.

To meet energy-reduction goals, cities are challenged with assessing building energy performance and prioritizing efficiency upgrades across existing buildings. Although current top-down building energy benchmarking approaches are useful for identifying overall efficient and poor performers across a portfolio of buildings at a city scale, they are limited in their ability to provide actionable insights regarding efficiency opportunities. Concurrently, advances in smart metering data analytics combined with new data streams available via smart metering infrastructure present the opportunity to incorporate previously undetectable temporal fluctuations into top-down building benchmarking analyses. This paper leveraged smart meter electricity data to develop daily building energy benchmarks segmented by strategic periods to quantify their variation from conventional, annual energy benchmarking strategies and investigate how such metrics can lead to near real-time energy management. The periods considered include occupied periods during the school year, unoccupied periods during the school year, occupied periods during the summer, unoccupied periods during the summer, and peak summer demand periods. Results showed that temporally segmented building energy benchmarks are distinct from a building’s overall benchmark. This demonstrates that a building’s overall benchmark masks periods in which a building is over- or underperforming during the day, week, or month; thus, temporally segmented energy benchmarks can provide a more specific and accurate measure for building efficiency. We discussed how these findings establish the foundation for digital twin–enabled urban energy management platforms by enabling identification of building retrofit strategies and near-real-time efficiency in the context of the performance of an entire building portfolio. Temporally segmented energy benchmarking measures generated from smart meter data streams are a critical step for integrating smart meter analytics with building energy benchmarking techniques, and for conducting smarter energy management across a large geographic scale of buildings.

Agroecology is in fashion, and now constitutes a territory in dispute between social movements and institutionality. This new conjuncture offers a constellation of opportunities that social movements can avail themselves of to promote changes in the food system. Yet there is an enormous risk that agroecology will be co-opted, institutionalized, colonized and stripped of its political content. In this paper, we analyze this quandary in terms of political ecology: will agroecology end up as merely offering a few more tools for the toolbox of industrial agriculture, to fine tune an agribusiness system that is being restructured in the midst of a civilizational crisis or, alternatively, will it be strengthened as a politically mobilizing option for building alternatives to development? We interpret the contemporary dispute over agroecology through the lenses of contested material and immaterial territories, political ecology, and the first and second contradictions of capital.

Natural hazards engineering plays an important role in minimizing the effects of natural hazards on society through the design of resilient and sustainable infrastructure. The DesignSafe cyberinfrastructure has been developed to enable and facilitate transformative research in natural hazards engineering, which necessarily spans across multiple disciplines and can take advantage of advancements in computation, experimentation, and data analysis. DesignSafe allows researchers to more effectively share and find data using cloud services, perform numerical simulations using high performance computing, and integrate diverse datasets so that researchers can make discoveries that were previously unattainable. This paper describes the design principles used in the cyberinfrastructure development process, introduces the main components of the DesignSafe cyberinfrastructure, and illustrates the use of the DesignSafe cyberinfrastructure in research in natural hazards engineering through various examples.

The incidence of the acquired immune deficiency syndrome (AIDS) among persons infected with human T-lymphotropic virus type III (HTLV-III) was evaluated prospectively among 725 persons who were at high risk of AIDS and had enrolled before October 1982 in cohort studies of homosexual men, parenteral drug users, and hemophiliacs. A total of 276 (38.1 percent) of the subjects were either HTLV-III seropositive at enrollment or developed HTLV-III antibodies subsequently. AIDS had developed in 28 (10.1 percent) of the seropositive subjects before August 1985. By actuarial survival calculations, the 3-year incidence of AIDS among all HTLV-III seropositive subjects was 34.2 percent in the cohort of homosexual men in Manhattan, New York, and 14.9 percent (range 8.0 to 17.2 percent) in the four other cohorts. Out of 117 subjects followed for a mean of 31 months after documented seroconversion, five (all hemophiliacs) developed AIDS 28 to 62 months after the estimated date of seroconversion, supporting the hypothesis that there is a long latency between acquisition of viral infection and the development of clinical AIDS. This long latency could account for the significantly higher AIDS incidence in the New York cohort compared with other cohorts if the virus entered the New York homosexual population before it entered the populations from which the other cohorts were drawn. However, risk of AIDS development in different populations may also depend on the presence of as yet unidentified cofactors.

The advent of wearable sensing technologies has produced unprecedented opportunities for the near real-time collection and analysis of workers’ safety and health data. To encourage the proactive safety management these opportunities present, extensive research efforts have explored using various wearable sensing technologies—including motion sensors (e.g., inertial measurement units) and physiological sensors (e.g., heart-rate sensors, electrodermal-activity sensors, skin-temperature sensors, eye trackers, and brainwave monitors)—to detect potential safety hazards and to continuously monitor a worker’s health on a construction jobsite. However, these efforts tend to be piecemeal or fragmented, which presents a challenge for both the practitioners and the researchers who wish to fully understand the current developments in this area. In this context, this paper provides a critical review of the state of the art of wearable applications in construction safety and health. The review first identifies five general applications within the literature: preventing musculoskeletal disorders, preventing falls, assessing physical workload and fatigue, evaluating hazard-recognition abilities, and monitoring workers’ mental status. Second, this study identifies the challenges impeding further development and deployment of wearable applications, specifically, signal artifacts and noise in wearable-sensors’ field measurements, variable standards for personal safety and health risks in construction, users’ resistance to technology adoption, and uncertainty regarding the return on investment. Lastly, this paper recommends future research opportunities for advancing the field, especially in terms of conducting sensor fusion for wearable applications, developing a business case, and engaging wearables in risk assessment and post-injury compensability assessment.

Based on the theoretical concepts of social networks and technology affordances, this article argues that different social media platforms influence political participation through unique, yet complementary, routes. More specifically, it proposes that Facebook and Twitter are conducive to protest behavior through two distinct mechanisms: whereas the influence of Facebook use is more effective through communication with strong-tie networks, the impact of Twitter use is more effective through communication with weak-tie networks. To test these expectations, we analyze data from a cross-sectional, face-to-face survey on a representative sample of Chilean youths conducted in 2014. Findings in the study lend empirical support for these hypotheses. Consequently, while different social media (in this case, Facebook and Twitter) are similar in their participatory effects, the paths through which this influence occurs are distinct, a finding that highlights the importance of studying political behavior across different media platforms.

Unmanned aerial vehicles (UAV), or drones, have become popular tools for practitioners and researchers alike. Recent years have seen a significant increase in UAV uses for many applications in the fields of science and engineering. A broad array of research development in UAVs has been reported in the literature. This paper provides a summary review of efforts related to UAV development with a focus on civil infrastructure applications. First, guidance is provided for researchers looking to newly incorporate UAVs into their research efforts. The advantages and disadvantages between different UAV types are outlined and performance characteristics discussed. Examples of different sensor payloads that demonstrate expanded functionality are provided. The review also provides an overview of research efforts in the emerging domain of wireless sensor networks and data processing algorithms specific to UAV-collected data. Highlights of recent achievements of UAVs in post-disaster reconnaissance, infrastructure component monitoring, geotechnical engineering, and construction management are presented. Lessons learned from UAV implementation and considerations for good practice are also discussed. The paper concludes with a discussion of the emerging and future research domains that address the most pressing knowledge gaps in current practice.

Consumers are relying increasingly on social commerce for making their purchase decisions, and e-vendors have great interests in applying social commerce features in the traditional e-commerce sites to increase sales. Although the importance of trust has been well recognized in the literature, the previous studies have mainly focused on trust in e-commerce sites and failed to incorporate its multidimensional nature to study consumer behavior. To gain further insights into consumer decision-making, this study aims to develop a social commerce trust-based consumer decision-making framework. Based on the social-technical theory, we conceptualize social commerce trust in a multidimensional view including trust in social media, trust in e-commerce sites, trust in social commerce features, and trust in consumers. Data were collected from an online survey taken by U.S. Amazon consumers. Our results strongly support our new conceptualization of social commerce trust and demonstrate its importance by examining its effects on e-commerce outcomes. Further, trust in consumers and trust in social commerce features have stronger effects than trust in e-commerce sites and trust in social media in the formation of social commerce trust. Our study contributes to the theory by introducing the new conceptualization of social commerce trust and advancing our understanding of how to enhance social commerce trust. Practitioners can gain insights into the implementation of social commerce for building consumer trust and increasing sales.

The storm water management model (SWMM) is a widely used tool for urban drainage design and planning. Hundreds of peer-reviewed articles and conference proceedings have been written describing applications of SWMM. This review focuses on collecting information on model performance with respect to calibration and validation in the peer-reviewed literature. The major developmental history and applications of the model are also presented. The results provide utility to others looking for a quick reference to gauge the integrity of their own unique SWMM application. A gap analysis assesses the model's ability to perform water-quality simulations considering green infrastructure (GI)/low impact development (LID) designs and effectiveness. It is concluded that the level of detail underlying the conceptual model of SWMM versus its overall computational parsimony is well balanced-making it an adequate model for large and medium-scale hydrologic applications. However, embedding a new mechanistic algorithm or providing user guidance for coupling with other models will be necessary to realistically simulate diffuse pollutant sources, their fate and transport, and the effectiveness of GI/LID implementation scenarios.

This paper examines panel data from two waves of the Youth Participatory Politics Survey, a nationally representative sample of young people in the United States. It employs a cross-lagged design to investigate the extent to which common forms of online activity create pathways to online and offline forms of political activity. Specifically, we examine the influence of Friendship-Driven (FD) and Interest-Driven (ID) online activity on online participatory politics and on offline forms of political action. Our findings reveal that FD and ID activity relate to political engagement, but in different ways. In addition, we find that the size of young people’s social networks interacts with both FD and ID online activity to promote political activity. This indicates that having exposure to “weak-ties” (resulting from large social networks) promote higher levels of political engagement. These findings demonstrate the need to specify the kinds of online activities in which youth are engaged and, more broadly, the political significance of social media and social networks

Today, municipal decision-makers, planners, and investors rely on valuation studies of ecosystem services, public health assessments, and real estate projections to promote a consensual view of urban greening interventions such as new parks, greenways, or greenbelts as a public good with widespread benefits for all residents. However, as new green projects often anchor major investment and high-end development, we ask: Does the green city fulfil its promise for inclusive and far-reaching environmental, health, social, and economic benefits or does it create new environmental inequalities and green mirages? Through case examples of diverse urban greening interventions in cities reflecting different urban development trajectories and baseline environmental conditions and needs (Barcelona, Medellin, and New Orleans), we argue that urban greening interventions increasingly create new dynamics of exclusion, polarization, segregation, and invisibilization. Despite claims about the public good, these interventions take place to the detriment of the most socially and racially marginalized urban groups whose land and landscapes are appropriated through the creation of a ‘green gap’ in property markets. In that sense, green amenities become GreenLULUs (Locally Unwanted Land Uses) and socially vulnerable residents and community groups face a green space paradox, whereby they become excluded from new green amenities they long fought for as part of an environmental justice agenda. Thus, as urban greening consolidates urban sustainability and redevelopment strategies by bringing together private and public investors around a tool for marketing cities with global reach, it also negates a deeper reflection on urban segregation, social hierarchies, racial inequalities, and green privilege.