Showing papers in "Frontiers in Built Environment in 2020"

The Concept of Sustainability in Smart City Definitions

Abstract: Smart cities have emerged as a possible solution to sustainability problems deriving from rapid urbanization. They are considered imperative for a sustainable future. Despite their recent popularity, the literature reveals the lack of conceptual clarity around the term of smart city, due to the plethora of existing definitions. This comprehensive literature review has identified 43 smart city definitions assessed according to the dimensions of sustainability that they consider, environmental, economic or social, and the priority in which they accord the concept of sustainability. The study revealed the common and opposite characteristics of the definitions according to the sustainability dimensions they consider and discussed the limitations they present. Such limitations appear to be related to citizen accessibility, misrepresentation and the particularity of existing urban fabrics. Taking into account these issues, as well as the difference between the smart city vision and its actual implementation, a new updated definition is proposed. The findings of the present study contribute to knowledge and practice by aiding conceptual clarity and, in particular, by drawing attention to underlying assumptions about the role of sustainability in smart city development.

A comparative study of differential evolution variants in constrained structural optimization

Abstract: Differential evolution (DE) is a population-based metaheuristic algorithm that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Such algorithms make few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions. DE is arguably one of the most versatile and stable population-based search algorithms that exhibits robustness to multi-modal problems. In the field of structural engineering, most real-world optimization problems are associated with one or several constraints. Constrained optimization problems are often challenging to solve due to their complexity and high nonlinearity. In this work we examine the performance of several DE variants, namely the traditional DE, the composite DE (CODE), the adaptive DE with optional external archive (JADE) and the self-adaptive DE (JDE and SADE), for handling constrained structural optimization problems associated with truss structures. The performance of each DE variant is evaluated by using five well-known benchmark structures in 2D and 3D. The evaluation is done on the basis of final optimum result and the rate of convergence. Valuable conclusions are obtained from the statistical analysis which can help a structural engineer in practice to choose the suitable algorithm for these kind of problems.

A Framework for Convergence Research in the Hazards and Disaster Field: The Natural Hazards Engineering Research Infrastructure CONVERGE Facility

Abstract: The goal of this article is twofold: to clarify the tenets of convergence research and to motivate such research in the hazards and disaster field. Here, convergence research is defined as an approach to knowledge production and action that involves diverse teams working together in novel ways—transcending disciplinary and organizational boundaries—to address vexing social, economic, environmental, and technical challenges in an effort to reduce disaster losses and promote collective well-being. The increasing frequency and intensity of disasters coupled with the growth of the field suggests an urgent need for a more coherent approach to help guide what we study, who we study, how we conduct studies, and who is involved in the research process itself. This article is written through the lens of the activities of the National Science Foundation-supported CONVERGE facility, which was established in 2018 as the first social science-led component of the Natural Hazards Engineering Research Infrastructure (NHERI). Convergence principles and the Science of Team Science undergird the work of CONVERGE, which brings together networks of researchers from geotechnical engineering, the social sciences, structural engineering, nearshore systems, operations and systems engineering, sustainable material management, and interdisciplinary science and engineering. CONVERGE supports and advances research that is conceptually integrative, and this article describes a convergence framework that includes the following elements: (1) identifying researchers; (2) educating and training researchers; (3) setting a convergence research agenda that is problem-focused and solutions-based; (4) connecting researchers and coordinating functionally and demographically diverse research teams; and (5) supporting and funding convergence research, data collection, data sharing, and solutions implementation.

Lessons Learned on the Tensile and Bond Behavior of Fabric Reinforced Cementitious Matrix (FRCM) Composites

Abstract: Fabric Reinforced Cementitious Matrix (FRCM) composites represent an effective, compatible and cost-efficient solution for strengthening and retrofitting existing structures. A strong research effort was done to investigate the tensile and bond properties of these materials, as well as the overall behaviour of strengthened members. A Round Robin Test was organized by Rilem TC 250-CSM on 28 FRCM composites comprising basalt, carbon, glass, PBO, aramid and steel textiles, embedded in either cement, lime or geopolymer mortars, to collect an experimental dataset and define test protocols. This paper collects the outcomes of this study to highlight fundamental properties of FRCM and to investigate the variability of test results. Grid spacing, equivalent thickness of the textiles and mechanical properties of FRCM composites, such as stiffness, tensile and bond strength, are provided. Based on the comparison of experimental outcomes, the scatter of the mechanical properties is estimated, as a consequence of the quasi-brittle behaviour of the inorganic matrix and its sensitivity to manufacturing, curing and handling processes. Eventually, the influence of testing implementation, such as gripping method and measuring techniques, are outlined.

An IoT Framework for Modeling and Controlling Thermal Comfort in Buildings

Abstract: Humans spend more than 90% of their day in buildings, where their health and productivity are demonstrably linked to thermal comfort Building thermal comfort systems account for the largest share of US energy consumption Despite this high-energy cost, due to building design complexity and the variety of building occupant needs, addressing thermal comfort in buildings remains a difficult problem To overcome this challenge, this paper presents an Internet of Things (IoT) approach to efficiently model and control comfort in buildings In the model phase, a method to access and exploit wearable device data to build a personal thermal comfort model has been presented Various supervised machine-learning algorithms are evaluated to produce accurate personal thermal comfort models for each building occupant that exhibit superior performance compared to a general model for all occupants The developed comfort models were used to simulate an intelligent comfort controller that uses the particle swarm optimization(PSO) method to search for optimal control parameter values to achieve maximum comfort Finally, a framework for experimental validation of the new proposed comfort controller that interactively works with the HVAC element has been introduced

Vehicle Connectivity and Automation: A Sibling Relationship

Abstract: The evolution of several scientific advancements has been characterized by the amalgamation of two or more technologies. With respect to vehicle connectivity and automation, recent literature suggests that these two emerging transportation technologies will jointly shape profoundly, the future of transportation. As such, it may be considered useful to revisit the primary concepts of automation and connectivity, and to identify any current and expected future synergies between them. Doing this can help generate knowledge that could be used to justify investments related to systems connectivity and automation. In this paper, we discuss the sibling relationship between the concepts of automation and connectivity in the specific context of road transportation vehicles. This paper discusses the technological concepts of systems automation and systems connectivity, and how they work individually and together to contribute to transportation system efficiency and safety. The paper also discusses the separate and common benefits of the connectivity and automation, and their possible holistic effects in terms of these benefits where they overlap. The paper suggests that vehicle connectivity has immense potential to enhance vehicle automation. Automation, on the other hand, may not significantly promote vehicle connectivity directly, at least not in the short term but possibly in the long term.

Artificial Intelligence for Efficient Thermal Comfort Systems: Requirements, Current Applications and Future Directions

Abstract: Author(s): Ghahramani, Ali; Galicia, Parson; Lehrer, David; Varghese, Zubin; Wang, Zhe; Pandit, Yogesh | Abstract: In buildings, one or a combination of systems (e.g., central HVAC system, ceiling fan, desk fan, personal heater, and foot warmer) are often responsible for providing thermal comfort to the occupants. While thermal comfort has been shown to differ from person to person and vary over time, these systems are often operated based on prefixed setpoints and schedule of operations or at the request/routine of each individual. This leads to occupants’ discomfort and energy wastes. To enable the improvements in both comfort and energy efficiency autonomously, in this paper, we describe the necessity of an integrated system of sensors (e.g., wearable sensors/infrared sensors), infrastructure for enabling system interoperability, learning and control algorithms, and actuators (e.g., HVAC system setpoints, ceiling fans) to work under a governing central intelligent system. To assist readers with little to no exposure to artificial intelligence (AI), we describe the fundamentals of an intelligent entity (rational agent) and components of its problem-solving process (i.e., search algorithms, logic inference, and machine learning) and provide examples from the literature. We then discuss the current application of intelligent personal thermal comfort systems in buildings based on a comprehensive review of the literature. We finally describe future directions for enabling application of fully automated systems to provide comfort in an efficient manner. It is apparent that improvements in all aspects of an intelligent system are be needed to better ascertain the correct combination of systems to activate and for how long to increase the overall efficiency of the system and improve comfort.

Discrete and Continuous Approaches for the Failure Analysis of Masonry Structures Subjected to Settlements

Abstract: Numerical modelling of masonry structures is nowadays still an active research field, given a number of open issues related to preservation and restoration of historical constructions and the availability of computational tools that have become more and more refined. This work focuses on the analysis of settlement-induced failure patterns characterizing the in plane response of two-dimensional dry-joints masonry panels, which differ in terms of texture, geometry and settlement configuration. Brick-block masonry, interpreted as a jointed assembly of prismatic particles in dry contact, can be modelled as a discrete system of rigid blocks interacting through contact surfaces with no tensile strength and finite friction, modelled as zero thickness elasto-plastic Mohr-Coulomb interfaces. Different approaches and numerical models are adopted herein: Limit Analysis (LA), a discrete model DEM and a continuous Finite Element Model (FEM). Limit Analysis is able to provide fast and reliable results in terms of collapse multiplier and relative kinematics. In this work, a standard LA procedure is coded through Linearised Mathematical Programming to take into account sliding mechanisms through dilatant joints. Discrete models are particularly suitable to study historical masonry materials, where rigid bodies interacts between contact and friction. Here, a combined Finite/Discrete Element approach (FEM/DEM) is adopted. Finally, analyses are conducted through the Finite Element approach, resorting to a continuum anisotropic elastic-perfectly plastic constitutive model. Some selected case-studies have been investigated adopting the above mentioned models and numerical results have been interpreted to highlight the capability of the approaches to predict failure patterns for various geometrical features of the structure and settlement configurations.

Deep Convolutional Networks for Construction Object Detection Under Different Visual Conditions

Abstract: Sensing and reality capture devices are widely used in construction sites. Among different technologies, vision-based sensors are by far the most common and ubiquitous. A large volume of images and videos is collected from construction projects every day to track work progress, measure productivity, litigate claims, and monitor safety compliance. Manual interpretation of such colossal amounts of data, however, is non-trivial, error-prone, and resource-intensive. This has motivated new research on soft computing methods that utilize high-power data processing, computer vision, and deep learning (DL) in the form of convolutional neural networks (CNN). A fundamental step toward machine-driven interpretation of construction site scenery is to accurately identify objects of interest for a particular problem. The accuracy requirement, however, may offset the computational speed of the candidate method. While light-weight DL algorithms (e.g., Mask R-CNN) can perform visual recognition with relatively high accuracy, they suffer from low processing efficacy which hinders their use in real-time decision-making. One of the most promising DL algorithms that balance speed and accuracy is YOLO (you-only-look-once). This paper investigates YOLO-based CNN models in fast detection of construction objects. First, a large-scale image dataset, named Pictor-v2, is created which contains about 3,500 images and approximately 11,500 instances of common construction site objects (e.g., building, equipment, worker). To assess the agility of object detection, transfer learning is used to train two variations of this model, namely YOLO-v2 and YOLO-v3, and test them on different data combinations (crowdsourced, web-mined, or both). Results indicate that performance is higher if the model is trained on both crowdsourced and web-mined images. Additionally, YOLO-v3 outperforms YOLO-v2 by focusing on smaller, harder-to-detect objects. The best performing YOLO-v3 model has a 78.2% mAP when tested on crowdsourced data. Sensitivity analysis of the output shows that the model’s strong suit is in detecting larger objects in less crowded and well-lit spaces. The proposed methodology can be also extended to predict the relative distance of the detected objects with reliable accuracy. Findings of this work lay the foundation for further research on technology-assistive systems to augment human capacities in quickly and reliably interpreting visual data in complex environments.

Detection of Personal Protective Equipment (PPE) Compliance on Construction Site Using Computer Vision Based Deep Learning Techniques

Abstract: Construction safety is a matter of great concern for both practitioners and researchers world-wide. Even after enough risk assessments and implementations of adequate controls on the work environments, construction workers are still subject to safety hazards. The need for personal protective equipment (PPE) becomes important in this context. Automatic and real-time detection of non-compliance of workers towards PPE is an important concern. The developments in the field of computer vision and data analytics, especially using deep learning algorithms have the potential to address this challenge in construction. Through this study a framework is developed to sense in real time, the safety compliance of construction workers with respect to PPE, thereby allowing to integrate this framework into the safety workflow of an organization. The study makes use Convolutional Neural Networks model developed by applying transfer learning to a base version of YOLOv3 deep learning network. Based on the presence of hardhat and safety jacket, the model predicts the compliance in four categories such as NOT SAFE, SAFE, NoHardHat and NoJacket. A data set of 2509 images collected from video recordings from several construction sites and web-based collection has been used to train the model. The model reported an F1 score of 0.96 with an average precision and recall rates at 96% on test data set. Once a non “SAFE” category is detected by the model, an alarm and a time-stamped report are also incorporated to enable a real-time integration and adoption on the construction sites. Overall, the study provides evidence on the feasibility and utility of computer vision-based techniques in automating the safety related compliance processes at construction sites.

Force and Shape Control Strategies for Minimum Energy Adaptive Structures

Abstract: This work presents force and shape control strategies for adaptive structures subjected to quasi-static loading. The adaptive structures are designed using an integrated structure-control optimization method developed in previous work, which produces minimum ‘whole-life energy’ configurations through element sizing and actuator placement optimization. The whole-life energy consists of an embodied part in the material and an operational part for structural adaptation during service. Depending on the layout, actuators are placed in series with the structural elements (internal) and/or at the supports (external). The effect of actuation is to modify the element forces and node positions through length changes of the internal actuators and/or displacements of the active supports. Through active control, the stress is homogenized and the displacements are kept within required limits so that the design is not governed by peak demands. Actuation has been modelled as a controlled non-elastic strain distribution, here referred to as eigenstrain. Any eigenstrain can be decomposed into two parts: an impotent eigenstrain only causes a change of geometry without altering element forces while a nilpotent eigenstrain modify element forces without causing displacements. Four control strategies are formulated: (C1) force and shape control to obtain prescribed changes of forces and node positions; (C2) shape control through impotent eigenstrain when only displacement compensation is required without affecting the forces; (C3) force control through nilpotent eigenstrain when displacement compensation is not required and (C4) force and shape control through operational energy minimization. Closed-form solutions to decouple force and shape control through nilpotent and impotent eigenstrain are given. Simulations on a slender high-rise structure and an arch bridge are carried out to benchmark accuracy and energy requirements for each control strategy and for different actuator configurations that include active elements, active supports and a combination of both.

Research Needs, Challenges, and Strategic Approaches for Natural Hazards and Disaster Reconnaissance

Abstract: Natural hazards and disaster reconnaissance investigations have provided many lessons for the research and practice communities and have greatly improved our scientific understanding of extreme events. Yet, many challenges remain for these communities, including improving our ability to model hazards, make decisions in the face of uncertainty, enhance community resilience, and mitigate risk. State-of-the-art instrumentation and mobile data collection applications have significantly advanced the ability of field investigation teams to capture quickly perishable data in post-disaster settings. The NHERI RAPID Facility convened a community workshop of experts in the professional, government, and academic sectors to determine reconnaissance data needs and opportunities, and to identify the broader challenges facing the reconnaissance community that hinder data collection and use. Participants highlighted that field teams face many practical and operational challenges before and during reconnaissance investigations, including logistics concerns, safety issues, emotional trauma, and after-returning, issues with data processing and analysis. Field teams have executed many effective missions. Among the factors contributing to successful reconnaissance are having local contacts, effective teamwork, and pre-event training. Continued progress in natural hazard reconnaissance requires adaptation of new, strategic approaches that acquire and integrate data over a range of temporal, spatial, and social scales across disciplines.

Damage probability matrices and empirical fragility curves from damage data on masonry buildings after Sarpol-e-zahab and Bam Earthquakes of Iran

Abstract: The weakness of tensile strength and high weight in masonry structures under the dynamic loads of earthquakes have always led to structural damage, financial losses, injuries and deaths. In spite of the cheap and affordable masonry materials, it has been very limited their use in constructions over the past three decades. However, common masonry materials are still found in monumental and historical structures, deteriorated texture and rural buildings. Identifying the seismic behaviour and the probability of the structural damage is vital for pre-earthquake seismic risk reduction of urban areas and the rapid post-earthquake assessment. The earthquake event occurred in Ezgeleh on 2017 November 12th with Mw=7.3 triggered the greatest damage in the Sarpol-e-zahab city at a distance of about 37 kilometres from the epicentre. Post-earthquake reconnaissance, microtremor analysis and rapid visual inventory of structural damages in different zones were performed by the research teams. In the present study, the strong ground motion, the peak ground acceleration and its corresponding intensity distribution, which are based on the site response analysis in different parts of the city, are introduced. Afterward, damage probability matrices of different types of masonry buildings, namely unreinforced masonry and confined masonry buildings, are determined for both bins of peak ground accelerations and intensities. Finally, the fragility curves of two types of masonry structures are extracted based on RISK-UE level 1 (LM1) method by assuming beta distribution to estimate the probability distribution function of the damage. These curves are useful in assessing pre-earthquake possible damages in masonry structures with similar construction methods and similar materials to reduce seismic risks.

Thunderstorm Downbursts and Wind Loading of Structures: Progress and Prospect

Abstract: In 1961, Davenport published a paper, considered by most a constitutive deed of wind engineering, in which meteorology, micrometeorology, climatology, aerodynamics and structural dynamics were embedded in a homogeneous framework of the wind loading of structures. This framework, known as Davenport chain and based on a wind model coherent with synoptic-scale extra-tropical cyclones, is so limpid and elegant as to become, in the course of the years, a sort of axiom. Between 1976 and 1978 Gomes and Vickery separated thunderstorm from non-thunderstorm winds, determined their extreme wind speed marginal distributions and from them derived a mixed statistical model later extended to other wind phenomena. This viewpoint, dealt with as a milestone in the emerging issue of mixed climatology, proved the impossibility to label a heterogeneous range of phenomena endowed with different velocity fields, frequencies, durations and sizes by the generic term “wind”. Many wind types, in particular tropical cyclones, tornadoes and downslope winds, occur in limited and well-known areas. Extra-tropical cyclones and thunderstorms are natural hazards that affect the whole planet. This paper provides a state-of-the-art about thunderstorm downburst, one of the most spectacular and damaging events produced by nature, and its wind loading of structures. Also in the light of planet's climatology evolution, this topic is a key issue of structural safety and sustainability.

The Flying Car—Challenges and Strategies Toward Future Adoption

Abstract: In recent years, our surface transportation infrastructure is suffering from overuse, extreme traffic congestion, and roadway disrepair. Instead of following the traditional infrastructure expansion policy, current transportation research focuses on developing innovative and novel solutions to the aforementioned issues. Current pathways to overcoming these issues include the gradual transition towards a number of emerging transportation technologies, such as, autonomous motor vehicles for human transport, as well as unmanned aerial vehicles (UAV’s) and "drone" technologies for surveillance, and package deliveries. However, as a long-term solution, transportation scientists are also investigating the once-seemingly futuristic notion of flying car technology - a convergent form of ground/air vehicle transportation, and assessing associated regulations. In this paper, an extensive review of current literature is conducted to explore the technological capabilities of flying cars – each requiring appropriate regulations and governance – to become fully sustainable. Specifically, issues pertinent to training, safety, environment, navigation, infrastructure, logistics/sustainability, and cybersecurity and human factors are explored. This paper concludes with a preliminary quantitative analysis exploring the public perceptions associated with flying cars – including anticipated benefits, concerns, and willingness to both hire and acquire the technology once available to consumers. Insights offered by this data will help inform next-generation policies and standards associated with the gradual advancement of flying cars.

Experimental Assessment of the Seismic Response of a Base-Isolated Building Through a Hybrid Simulation Technique

Abstract: Base-isolated structural systems have been more and more investigated through both numerical and experimental campaigns, in order to evaluate the effective advantages, in terms of vulnerability reduction. Thanks to the lateral response of proper isolation devices, large displacement demands can be accommodated, and the overall energy of the seismic event can be dissipated, by means of hysteretic behaviors. Among the common typologies of isolators, Curved Surface Slider devices represent a special technologic solution, with potentially high dissipative capacities, provided by innovative sliding materials. On the other hand, the overall behavior is highly non-linear, and a number of research works have been developed, aiming at the definition of the most comprehensive analytical model of such devices. The most realistic response of a base-isolated structure could be returned by a shake table test of a full-scale buildings. However, dimensions of the available shake tables do not allow to consider the common load conditions, which the isolation devices are subjected to, and consequently scaled specimens are needed, and unrealistic responses could be found. Hybrid simulations seem to solve such an issue, by accounting for an experimental sub-structuring, represented by a physical devices tested in a testing equipment, and a numerical sub-structuring, consisting of a numerical model of the superstructure. Thus, a much more realistic response of the full-scale structure can be computed. In this work the outcomes of a number of hybrid simulations have been deeply analyzed, and compared to similar numerical model, by accounting for proper non-linear constitutive laws for isolation devices, in order to evaluate the effectiveness of design and assessment procedures, commonly adopted in real practice applications.

A Cloud-Enabled Application Framework for Simulating Regional-Scale Impacts of Natural Hazards on the Built Environment

Abstract: With the goal to facilitate research to evaluate the risks from natural hazards and alternate risk mitigation strategies, the Natural Hazards Engineering Research Infrastructure’s Computational Modeling and Simulation Center (NHERI SimCenter) is developing computational workflows for regional hazard simulations. These simulations enable research to integrate detailed assessments of individual facilities with comprehensive regional-scale simulations of natural hazard effects. This approach enables fine (parcel level) granularity on assessing natural hazard impacts on buildings, infrastructures systems and other constructed facilities, which can enable engineering informed assessment of policy considerations and socio-economic impacts. Effective development of research platforms for high-resolution regional simulations requires modular workflows that can integrate state-of-the-art models with information technologies and high-performance computing resources. In this paper, the modular architecture of the computational workflow models is described and illustrated through testbed applications to evaluate regional building damage under an earthquake and a hurricane scenario. Developed and disseminated as open-source software on the NHERI Design Safe Cyberinfrastructure, the computational models and workflows are enabling multi-disciplinary collaboration on research to mitigate the effects of natural hazard disasters.

Geospatial environments for hurricane risk assessment : Applications to situational awareness and resilience planning in New Jersey

Abstract: Mitigation of losses due to coastal hazards has become an increasingly urgent and challenging problem in light of rising seas and the continued escalation of coastal population density. Unfortunately, stakeholders responsible for assuring the safety of these coastal communities are not equipped with the engineering research community’s latest tools for high-fidelity risk assessment and geospatial decision support. In the event of a hurricane or nor’easter, such capabilities are exceptionally vital to project storm impacts on critical infrastructure and other municipal assets and to inform preemptive actions that can save lives and mitigate property damage. In response, a web-based visualization environment was developed using the GeoNode content management system, informed by the needs of municipal stakeholders. Within this secure platform, registered users with roles in planning, emergency management and first response can simulate the impact of hurricanes and nor’easters using the platform’s storm Hazard Projection (SHP) Tool. The SHP Tool integrates fast-to-compute windfield models with surrogate models of high-fidelity storm surge and waves to rapidly simulate user-defined storm scenarios, considering the effects of tides, sea level rise, dune breaches and track uncertainty. In the case of a landfalling hurricane, SHP tool outputs are automatically loaded into the user’s dashboard to visualize the projected wind, storm surge and wave run-up based on the latest track information published by the National Hurricane Center. Under either use case, outputs of the SHP Tool are visualized within a robust collaborative geospatial environment supporting the seamless exploration of centralized libraries of geographic information system (GIS) data from federal, state, county and local authorities, with tools to add user-supplied annotations such as notes or other geospatial mark-ups. This paper will overview the development and deployment of this platform in the State of New Jersey, detailing the cyberinfrastructure design and underlying computational models, as well as the user stories that inspired the platform’s functionalities and interfaces. The study concludes with reflections from the process of piloting this project with stakeholders at the state and municipal level to support more risk-responsive and data-informed decision making.

Adaptive Concrete Beams Equipped With Integrated Fluidic Actuators

Abstract: The rapidly growing world population is a great challenge for the building industry. With the scarcity of resources, it is not possible to provide the upcoming humanity with sufficient living- and work places and infrastructure with current construction methods. For wide-spanning beams and slabs the decisive design criteria is mainly determined by deformations rather than stresses, since deflections must be limited. This leads to structural elements, which are not fully exploited. However, if the deformations can be reduced significant material savings can be achieved. This article presents a solution, which allows beams to react actively to loads by use of integrated actuators. Sensors, actuators and a control unit enable components subjected to bending to adapt to these loads. The newly developed integrated hydraulic actuators allow the structure to react specifically to any possible load, by adjusting the internal hydraulic pressure. This is an enormous advantage in load bearing systems because there is often no dominant load case. This internal actuation concept is a new approach, as previous adaptive structures either have externally added actuators or compose of truss structures in which single bars are actuated. In this paper the concept is explained analytically, simulated with the finite element method and validated experimentally.

Promoting Autonomous Vehicles Using Travel Demand and Lane Management Strategies

Abstract: A key challenge facing cities of today is the persistent and growing urban congestion that has significant adverse effects on economic productivity, emissions, driver frustration, and quality of life. The concept of smart cities, which can revolutionize the management of metropolitan transportation operations and infrastructure, shows great promise in mitigating this problem. Specifically, the automation and connectedness (A&C) of smart city entities such as its infrastructure, services, and vehicles, can be helpful. In this regard, this paper focuses on the potential of autonomous vehicles (AVs) and AV infrastructure, particularly during prospective transition era where there will be mixed streams of AVs and human driven vehicles (HDVs). The paper considers two aspects of this potential: connectivity-enabled travel demand management and travel infrastructure supply through lane management. To demonstrate the opportunity associated with this potential, this paper first presents an AV-enabled tradable credit scheme (TCS) to manage travel demand. Here, the transportation authority distributes travel credits to travelers directly and instantaneously using the AV’s A&C features. Then, travelers use their A&C features to pay these credits for travel at specific locations or times-of-day according to their choices of lane types and links. With regard to supply, the paper considers that the road network consists of two lane types: AV-dedicated, and mixed traffic lanes, and develops a scheme for Travel Demand and Lane Management Strategies in AV transition era (TLMAV). First, the paper models the expected travel choices based on the user equilibrium concepts, at different levels of AV market penetration. Then, the existence of the optimal solution in terms of link flows and the prevailing travel credit price is demonstrated. Then, the paper establishes the optimal TLMAV that minimize total travel time subject to user equity constraints. The results demonstrate the extent to which HDV users suffer increase in travel cost if equity is not considered in the model. The results also show how the transportation agency can use TLMAV to keep HDV travel costs to acceptable levels, particularly during early periods of the AV transition period.

Equivalent-Frame Modeling of Two Shaking Table Tests of Masonry Buildings Accounting for Their Out-Of-Plane Response

Abstract: Equivalent frame models are an effective tool for the seismic assessment of existing masonry structures. Due to their simplicity, these models can be used to perform multiple nonlinear dynamic analyses, accounting explicitly for different sources of modeling and input uncertainty. In the past, equivalent frame models have been used to effectively estimate the global response of buildings whose behavior is dominated by in-plane failure modes of piers and spandrels. The recent development of a three-dimensional macroelement formulation for modeling both the in-plane and out-of-plane response extends the use of equivalent frame models to the additional study of local out-of-plane mechanisms of a building. This work applies the newly developed formulation, implemented in OpenSees, to the modeling of two shaking table tests on a stone masonry building and on a modern mixed concrete-unreinforced masonry structure. Since the approach explicitly accounts for the quality of connections in the building (i.e., wall-to-wall and floor-to-wall connections), specific elements and material models were developed for modeling these connections in an equivalent frame idealization of the three-dimensional structure. Through comparison with the experimental results, the performance of the modeling approach is discussed, and the sensitivity of the response to the major sources of modeling uncertainty (quality of connections, damping model) is assessed. The comparisons show that these new equivalent frame models can capture the onset of out-of-plane failure for historical structures with poor floor-to-wall connections and for modern URM buildings with stiff RC slabs, where the slab can uplift from the URM wall, which leads to changing static and kinematic boundary conditions of the out-of-plane loaded wall. The results further show that 1-2\% of damping leads to good agreements with the experimental results if initial stiffness proportional Rayleigh damping is used.

Reinforcement Learning and Graph Embedding for Binary Truss Topology Optimization Under Stress and Displacement Constraints

Abstract: This paper addresses a combined method of reinforcement learning and graph embedding for binary topology optimization of trusses to minimize total structural volume under stress and displacement constraints. Although conventional deep learning methods owe their success to a convolutional neural network that is capable of capturing higher level latent information from pixels, the convolution is difficult to apply to discrete structures due to their irregular connectivity. Instead, a method based on graph embedding is proposed here to extract the features of bar members. This way, all the members have a feature vector with the same size representing their neighbor information such as connectivity and force flows from the loaded nodes to the supports. The features are used to implement reinforcement learning where an action taker called agent is trained to sequentially eliminate unnecessary members from Level-1 ground structure, where all neighboring nodes are connected by members. The trained agent is capable of finding sub-optimal solutions at a low computational cost, and it is reusable to other trusses with different geometry, topology and boundary conditions.

Optimum Design of Frame Structures From a Stock of Reclaimed Elements

Abstract: This paper presents optimization methods to design frame structures from a stock of existing elements. These methods are relevant when reusing structural elements over multiple service lives. Reuse has the potential to reduce the environmental impact of building structures because it avoids sourcing new material, it reduces waste and it requires little energy. When reusing elements, cross-section and length availability have a major influence on the structural design. In previous own work, design of truss structures from a stock of elements was formulated as a mixed-integer linear programming (MILP) problem. It was shown that this method produces solutions which are global optima in terms of stock utilization. This work extends previous formulations to stock-constrained optimization of frame structures subject to ultimate and serviceability limit states hence expanding the range of structural typologies that can be designed through reuse. Fundamental to this method is the globally optimal assignment of available stock elements to member positions in the frame structure. Two scenarios are considered: A) the use of individual stock elements for each member of the frame, and B) a cutting stock approach, where multiple members of the frame are cut from a single stock element. Numerical case studies are presented to show the applicability of the proposed method to practical designs. To carry out the case studies, a stock of elements was inventoried from shop drawings of deconstructed buildings. Results show that through reusing structural elements a significant reduction of embodied greenhouse gas emissions could be achieved compared to optimized structures made of new elements.

COVID-19 as a Harbinger of Transforming Infrastructure Resilience

Abstract: The COVID-19 pandemic has shocked infrastructure systems in unanticipated ways Seemingly in the course of weeks, our demands for many basic and critical services have radically shifted With expected long-term effects (i e , years), COVID-19 is going to have profound impacts on every facet of infrastructure systems, and will shock these systems very differently than the hazards that we often focus on, such as extreme events, disrepair, and terrorist attacks At the beginning of this pandemic, infrastructure managers are scrambling to respond to changes in demand, and to understand what the long-term effects are for how they operate and maintain their systems We contend that COVID-19 is revealing several important limitations to how we approach and manage our infrastructure, that must be acknowledged and addressed as the pandemic persists, and in a future increasingly characterized by accelerating and increasingly uncertain conditions These limitations are how (i) we prepare for concurrent hazards, (ii) frame criticality based on traditional infrastructure sectors and not human capabilities, (iii) we emphasize efficiency at a cost to resilience, and (iv) leadership is largely focused on stable conditions Each of these challenges represents a call for major rethinking for how we approach infrastructure, and COVID-19 presents a window of opportunity for change © Copyright © 2020 Carvalhaes, Markolf, Helmrich, Kim, Li, Natarajan, Bondank, Ahmad and Chester

Review on Statistical Modeling of Travel Time Variability for Road-Based Public Transport

Abstract: Accurately modeling travel time of road-based public transport can help directly improve current passenger service and operating efficiency. Moreover, it paves the way for control of future high technology automated vehicles, which will share the same characteristics of sharing the road infrastructure with other vehicles; carrying multiple passengers, i.e. having a non-negligible dwell process, and run not completely demand-responsive but in general following a schedule or a target frequency. Recent advances in sensor and communications technology, leading eventually to comprehensive vehicle connectivity, have significantly increased the amount and quality of travel time data available making it possible to better model distributions of travel time of current buses. We assume that the choice of those distributions with regards to transport performance will hold also in the near future. This paper explains definitions of travel time components and explains how they contribute to variability. It focuses on the description of day-to-day variability and systematically reviews the current state of the art for statistically modeling bus travel, running, and dwell time distributions. It considers statistical distributions developed based on empirical data from the research literature. Statistical distributions are powerful tools, as they can describe the inherent variability in data with a limited number of parameters. The review finds that both spatial and temporal data aggregation have an important influence on the statistics as well as the choice of the most appropriate probability distribution. This influence is still not well understood and remains a question for further studies. Furthermore, the review finds that mixture distributions provide good fitting performance, however, it is important to improve the description of components in such distributions, in order to get meaningful and understandable distributions. The methodologies for fitting distributions, for proving if a distribution is suited, and for identifying best fitting, robust and reproducible distribution should be reconsidered. Such a distribution will enable reporting, controlling operations, and disseminating information to operators and travelers. Finally, this review proposes directions for further work.

Dynamic Identification as a Tool to Constrain Numerical Models for Structural Analysis of Historical Buildings

Abstract: In recent few decades, operational modal analysis (OMA), also known as output-only or ambient vibration test (AVT), has become a powerful tool for a wide range of applications in the field of civil engineering. The output-only modal test represents an effective alternative to forced vibration techniques when historical structures are investigated. This method takes advantages from natural sources of vibration (wind, traffic, etc.) instead of having to shake the structure artificially. This paper shows how the dynamic characteristics of a building (i.e. frequency, modal shapes and damping ratios) can be automatically identified by enhanced frequency domain decomposition (EFDD) technique. The assessment of the dynamic characteristics is performed analyzing the Baptistery of San Giovanni in Firenze (Italy) where a network of seismic sensors was temporarily installed. The modal parameters are used to calibrate a 3D finite element (FE) numerical model, by using a genetic algorithm (GA). This procedure allows to obtain accurate and robust model capable to reproduce the actual dynamic behavior of the structure by minimizing the uncertainties related to the mechanical parameters. This paper, providing an illustrative case study in the field of health monitoring of monumental structures, confirms that the OMA technique is able to derive effective information on the dynamic behavior of the analyzed building, which is useful to tune reliable and robust numerical models to be employed for structural analysis.

Energy Saving at Work: Exploring the Role of Social Norms, Perceived Control and Ascribed Responsibility in Different Office Layouts

Abstract: Reducing energy consumption in office buildings is critical for improving energy efficiency and decarburization at the large scale. This study (N=854) investigated the influence of social-psychological factors on energy-saving intention and behaviors in single-person versus shared offices based on the extend model of Theory of Planned Behavior (TPB). We found that ascription of responsibility, a variable added to the TPB, is the strongest predictor of energy-saving intentions for both single-person and shared offices. Interestingly, while injunctive norms are an important predictor of behavioral intention for single-person offices, descriptive norms are an important one for shared offices. Energy-saving intention mediates the influences of the aforementioned variables on energy-saving behaviors. Contrary to our hypotheses, perceived control over energy-saving and perceived ease of access to building control features have no direct impacts on energy-saving behaviors in single-person offices, but they have impacts on energy-saving behaviors in shared offices. This study provides useful insights for building designers and occupant behavior and energy modeling researchers.

Experimental Testing and Numerical Modeling of Robust Unreinforced and Reinforced Clay Masonry Infill Walls, With and Without Openings

Abstract: This paper presents an overview of the experimental results obtained by combined in-plane/out-of-plane (IP/OOP) tests carried out on robust clay masonry infill walls. The combined tests were carried out on eight full-scale one-bay, one-storey infilled RC frames, plus one reference RC bare frame. In four cases, the masonry walls fully fill the RC frame: two walls are made of unreinforced masonry (URM), whereas other two are made of reinforced masonry (RM), with both vertical and horizontal reinforcement. Each pair of specimens was tested up to different levels of IP drift, before carrying out the OOP tests. Other four specimens, still made of URM and RM walls, are characterized by the presence of a central opening, and in one case, the effect of a lintel is analysed. On the basis of the tests carried out, an analytical model was developed for the analysis of the OOP behaviour. It was used to calibrate IP damage degradation models based on the experimental results, to define limits for the applicability of well-known flexural and arching mechanism models for the evaluation of the OOP capacity of the infill walls, and to evaluate the efficiency of vertical reinforcement. In this work, the experimental campaign is presented and the results of both experimental tests and numerical analyses are discussed.

Pullout Behavior of Different Geosynthetics-Influence of Soil Density and Moisture Content

Abstract: Geosynthetics have increasingly been used as reinforcement in permanent earth structures, such as road and railway embankments, steep slopes, retaining walls and bridge abutments. The understanding of soil-geosynthetic interaction is of primary importance for the safe design of geosynthetic-reinforced soil structures, such as those included in transportation infrastructure projects. In this study, the pullout behaviour of three different geosynthetics (geogrid, geocomposite reinforcement and geotextile) embedded in a locally available granite residual soil is assessed through a series of large-scale pullout tests involving different soil moisture and density conditions. Test results show that soil density is a key factor that affects the reinforcement pullout resistance and the failure mode at the interface, regardless of geosynthetic type or soil moisture content. The soil moisture condition may considerably influence the pullout response of the geosynthetics, particularly when the soil is in medium dense state. The geogrid exhibited higher peak pullout resistance than the remaining geosynthetics, which is associated with the significant contribution of the passive resistance mobilised against the geogrid transverse members to the overall pullout capacity of the reinforcement.

Constitutive Numerical Model of FRCM Strips Under Traction

Abstract: In this paper the tensile behavior of Fiber Reinforced Cementitious Matrix (FRCM) strips is investigated through Finite Element (FE) models. The most adopted numerical modeling approaches for the simulation of the fiber-matrix interface law are described. Among them, the cohesive model is then used for the generation of FE models which are able to simulate the response under traction of FRCM strips tested in laboratory whose results are available in the technical literature. Tests on basalt, PBO and carbon coated FRCM specimens are taken into account also considering different mechanical ratios of the textile reinforcement. The comparison between FE results and experimental data allows validating the adopted numerical modelling approach. Finally, some considerations are provided on the effects of the fiber fabric mechanical ratio and the strength and stiffness of the interface on the tensile capacity of the FRCM strip.