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.

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.

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.

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 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.

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.

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.

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.

The strength properties of basalt fiber–reinforced biocemented (BFRB) sand specimens with various calcite contents and fiber contents are investigated through a series of unconfined compressive and splitting tensile tests. Reverse injection is introduced to improve the uniformity of the calcium carbonate precipitation. The test results show that both the unconfined compressive strength (UCS) and splitting tensile strength (STS) at a given basalt fiber content increase significantly with increasing calcite content, whereas the axial strain of the peak failure state decreases with increasing calcite content. The improved ductility has implications for loading conditions where large deformations may be anticipated. The UCS, STS, and peak failure state strain increase with increasing fiber content at a given calcite content, which is interpreted to be due to the interlocking, reinforcing, and bonding effects observed in scanning electron microscopy (SEM) images. A phase-volume framework for determining the porosity of BFRB sand is developed and used within an existing empirical formulation developed for other types of fiber-reinforced, cemented geomaterials. Moreover, the UCS and STS of the BFRB sand can be described by the existing empirical formulations incorporating the cementing factor index expressed in terms of the porosity and calcite volumetric content. Predictions based on the empirical formulations are in good agreement with the test results for the UCS and STS of the BFRB sand specimens.

Exploiting smart cities with digital twins (SCDT) requires the integration of sensing and simulation across diverse infrastructure systems into community management. Community disaster management can provide a valuable foundation for SCDT development. The current work proposes and tests a conceptual model of a SCDT for disaster management and describes two threats to SCDT development that can be mitigated by focusing development on disaster management. Information loops, as opposed to individual components, are identified as a critical future focus of SCDT development. Primary contributions include support for SCDT development for disaster management, a conceptual SCDT for disaster management model, and a discussion of issues to be addressed in the development and deployment of SCDT for disaster management.

Determination of interface shear strength is crucial in the design of many geotechnical structures. To study the effect of interface roughness, particle geometry (size and shape), and sand gradation on the interface friction angle, direct interface shear tests were performed for 10 sands with varying particle sizes, shapes, and gradations and four steel surfaces with different levels of rusting. When sheared along the same interface, the interface friction angle was greater for sands with smaller particle sizes and more angular or elongated particle shapes. For a given sand, the interface friction angle increases with increasing surface roughness. For sands with uniform particle size, a unique relationship was found between the normalized surface roughness and the ratio of the sand–steel critical-state interface friction angle to the internal critical-state friction angle of the sand. Given the same surface roughness and mean particle size, smaller critical-state interface friction angles were mobilized for graded sands than for sands with uniform particle sizes.

Equipment and workers are two important resources in the construction industry. Performance monitoring of these resources would help project managers improve the productivity rates of construction jobsites and discover potential performance issues. A typical construction workface monitoring system consists of four major levels: location tracking, activity recognition, activity tracking, and performance monitoring. These levels are employed to evaluate work sequences over time and also assess the workers’ and equipment’s well-being and abnormal edge cases. Results of an automated performance monitoring system could be used to employ preventive measures to minimize operating/repair costs and downtimes. The authors of this paper have studied the feasibility of implementing a wide range of technologies and computational techniques for automated activity recognition and tracking of construction equipment and workers. This paper provides a comprehensive review of these methods and techniques as well as describes their advantages, practical value, and limitations. Additionally, a multifaceted comparison between these methods is presented, and potential knowledge gaps and future research directions are discussed.

One of the most important requirements for successful learning experiences is learning activity on a regular basis. The problem with today’s learning system is that the learners often get stuck while using traditional learning systems because they can’t motivate them to fast learning and make a creative mind. Successful learning requires getting knowledge on regular bases and keeping it memorable as long as possible. The problem with traditional learning methods is that the learner's mind glued in its state and it does not provide any motivation to them to get new knowledge and improve their skills. Microlearning provides a new teaching paradigm which can allow knowledge and information to divided into small chunks and deliver it to the learners. Microlearning can make the learning subjects easy to understand and memorable for a longer period. In this work, we tested microlearning teaching methods for ICT subject in the Primary school. We chose two groups from a Primary school in Sulaimani city. Then we teach the class using microlearning methods in one of them and traditional methods in the other for six weeks. After testing both groups getting the results, Microlearning group showed around 18% better learning than traditional group. We can conclude that using microlearning techniques, the effectiveness, and efficiency of learning can be improved. Also, the knowledge can stay memorable for longer periods.

Construction robots continue to be increasingly deployed on construction sites to assist human workers in various tasks to improve safety, efficiency, and productivity. Due to the recent and ongoing growth in robot capabilities and functionalities, humans and robots are now able to work side-by-side and to share workspaces. The emerging field of human–robot collaboration has significant potential applications in construction and continues to advance the state of the art in defining the responsibilities of both humans and robots during collaborative work. This paper proposes a new taxonomy for collaborative human–robot work in construction teams. The evolution of construction robots during the last two decades is first reviewed, and relevant bodies of work are categorized into one of five levels of human–robot collaboration: Preprogramming, Adaptive Manipulation, Imitation Learning, Improvisatory Control, and Full Autonomy. The categories of the proposed taxonomy are defined based on the level of robot autonomy and the corresponding human effort in collaborative teamwork. Second, this paper uses the categories of the proposed taxonomy as a contextual framework to identify current challenges and knowledge gaps in collaborative human–robot construction work and recommends directions for future research.

OBJECTIVES: To perform a meta-analysis of the risk of relapse after discontinuation of anti-tumor necrosis factor (anti-TNF) therapy in patients with Crohn's disease (CD) and ulcerative colitis (UC), to evaluate risk factors for relapse, and to assess the response to retreatment with the same anti-TNF. METHODS: Studies evaluating the incidence of relapse after anti-TNF discontinuation in patients with CD or UC who reached clinical remission with anti-TNFs were included. Bibliographies up to January 2015 were searched. Frequency of relapse after discontinuation of anti-TNF agents was determined; meta-analyses were performed using the inverse-variance method. RESULTS: We included 27 studies (21 infliximab and 6 infliximab/adalimumab). The overall risk of relapse after discontinuation of anti-TNF therapy was 44% for CD (95% confidence interval (CI) 36-51%; I(2)=79%; 912 patients) and 38% for UC (23-52%; I(2)=82%; 266 patients). In CD, the relapse rate was 38% at 6 months after discontinuation (short term), 40% at 12 months (medium term), and 49% at >25 months (long term). In UC, 28% of patients relapsed at 12 months. In CD, when clinical remission was the only criterion for stopping anti-TNF therapy, the relapse rate after 1 year was 42%, which decreased to 26% when endoscopic remission was also required. Retreatment with the same anti-TNF induced remission again in 80% of cases (68-91%). CONCLUSIONS: Approximately one-third of patients with inflammatory bowel disease in remission under anti-TNF treatment relapsed 1 year after discontinuation. This proportion increased to half in the long term. In CD patients, the risk of relapse was lower when the criterion for discontinuation was endoscopic remission and not only clinical remission. Response to retreatment with the same anti-TNF agent was favorable.

In recent years, the shortage in skilled labor was one of the most significant concerns for the construction industry. Previous studies explored the causes and effects of such shortages; however, these efforts featured limited documentation and implemented subjective methods for analysis. This phenomenon is very complex and dynamically responds to the labor market's behavior and its influence on construction projects. The main goal of this study is to provide a simulation-based method for analyzing the causes and impacts of skilled labor shortages. By adopting a system dynamics (SD) approach, an integrated SD model was developed to explain the dynamic interrelationships between the causes and effects of the skilled labor shortage in construction projects. A total of five scenarios were established to simulate shortages and construction project behaviors. Based on statistical regression and sensitivity analysis, this study contributes to a better understanding of the patterns among the causes of shortages, the shortages themselves, and their impacts on labor wages, cost overruns, and scheduling concerns in construction projects.