Urban sprawl and increasing population density in urban centers create the challenge to finding ways of sustainable transportation solutions that preserve the convenience of residents while reducing ...emissions. Therefore, walkability is a core urban design element because of being advantageous onto three fronts: health, livability, and sustainability. Adopting walkability as urban solution relieves conceptual and practical tensions between the individualistic interests manifested in the desire to own and use private cars, and the need to reduce transportation-based consumption. This review advocates that long-term health benefits from walking and physical activity are the premier incentive to repurpose our cities to be more sustainable and more walking friendly, and spark behavioral change into reducing car dependency for all daily transportations. The review inspects physical elements of the built environment that make the walking trip feasible and desirable, such as connectivity, accessibility, and closeness of destination points, presence of greenness and parks, commercial retail, and proximity to transit hubs and stations. Hence, this review explores a few popular walkability evaluation indices and frameworks that employ subjective, objective, and/or distinctive methods within variant environmental, cultural, and national context. There is no unified universal standardized walkability theory despite the need for rigorous evaluation tools for policy makers and developers. Furthermore, there is a lack of emphasis on air quality and thermal stress while approaching walkability, despite being important elements in the walking experience. Research opportunities in the field of walkability can leverage location tracking from smart devices and identify the interaction patterns of pedestrians with other transportation modes, especially for those with fundamental movement challenges such as wheelchair users.
An artificial neural network (ANN) model for predicting the stability of rectangular tunnels in rock masses based on the Hoek–Brown (HB) failure criterion is presented in this study. Since the safety ...assessment of the tunnel stability is one critical issue for civil engineers during the construction, it is very important to develop a reliable and accurate stability analysis of such problems. The finite element limit analysis (FELA) with the HB failure criterion is used to develop the numerical upper and lower bound solutions of the problem of rectangular tunnels in rock masses. A novel machine learning-aided prediction of this problem is then developed based on the datasets of the numerical bound solutions obtained from the FELA. The inputs consist of six dimensionless parameters including the cover-depth ratio of tunnels, the width ratio of tunnels, the normalized uniaxial compressive strength, the geological strength index, the
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parameter, and the degree of disturbance of rock masses. The results show that the optimal ANN models provide very great accuracy in predicting the stability of the rectangular tunnels based on the HB failure criterion. The solutions will provide a prompt assessment of tunnel stability in rock masses for geotechnical engineers during the construction of rock tunnels.
The prominence gained by Artificial Intelligence (AI) over all aspects of human activity today cannot be overstated. This technology is no newcomer to structural engineering, with logic-based AI ...systems used to carry out design explorations as early as the 1980s. Nevertheless, the advent of low-cost data collection and processing capabilities have granted new impetus and a degree of ubiquity to AI-based engineering solutions. This review paper ends by posing the question of how long will the human engineer be needed in structural design. However, the paper does not aim to answer this question, not least because all such predictions have a history of going wrong. Instead, the paper assumes throughout as valid the claim that the need for human engineers in conventional design practice has its days numbered. In order to build the case towards the final question, the paper starts with a general description of the currently available AI frameworks and their Machine Learning (ML) sub-classes. The paper then proceeds to review a selected number of studies on the application of AI in structural engineering design. A discussion of specific challenges and future needs is presented with emphasis on the much exalted roles of “engineering intuition” and “creativity”. Finally, the conclusion section of the paper compiles the findings and outlines the challenges and future research directions.
The renaissance bell tower of San Benedetto in Ferrara (Italy) has been investigated to understand its nonlinear dynamics correctly with the Non-Smooth Contact Dynamic (NSCD) method. The masonry ...structure has been modeled with the Discrete Element Methods (DEM), assuming rigid blocks and frictional joints, with the aim to recreate the tower in the actual configuration with the inclination and in a fictitious perfect vertical shape in order to assess the influence of the initial slope on its dynamics. The contacts between blocks are governed by the Signorini's impenetrability condition and by dry-friction Coulomb's law. Both configurations have been analyzed inducing real seismic excitations of various types and intensities, corresponding to the six main seismic events of the last few decades in Italy. Thus, the seismic vulnerability of the examined tower is clearly expressed in the numerical results, proving the effects due to the inclination on the amplification of the vulnerability and the several possible collapse mechanisms. Moreover, the NSCD has demonstrated to be a powerful numerical technique to obtain highly accurate results in the structural analyses of masonry structures in the nonlinear range.
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 (CNNs). 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 lightweight 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 also be 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.
As the notion of data-driven analytics and turning data into action is becoming more salient in the construction industry, researchers and practitioners have recently devoted considerable effort to ...investigate the digital transformation of the industry. Along this journey, Digital Twin has been introduced to the industry as a concept that holds the promise to challenge the status-quo and address long standing problems of productivity, efficiency, and value. While this concept is becoming more familiar among practitioners, there is a lack of universal definitions of what the Digital Twin of a construction project is. Additionally, while identifying the purpose of Digital Twin is recognized as the first step in implementing Digital Twins, there is little discussion on the perception of construction practitioners of the extent to which Digital Twin can deliver value. To address these research gaps and building on the existing work on Digital Twins in the context of the construction industry, this paper first proposes a definition of the Digital Twin of a construction project. Next, a series of semi-structured interviews are conducted with nine construction practitioners to understand their perceptions on the use and challenges of Digital Twins. Thematic analysis is then used to analyze interview data and summarize Digital Twins applications, capabilities, and challenges. Forty direct applications were identified and grouped into seven capabilities. Digital Twins capabilities of Increase Transparency of Information and Real-Time Monitoring, Analysis, and Feedback were the most discussed with a total of eight applications each, followed by Better Stakeholder Collaboration which had seven applications. The discussion on challenges led to the identification of 34 challenges to implementing Digital Twin, grouped into six categories coded through thematic analysis. The category on Data Understanding, Preparation, and Usage Challenges was found to be the most critical for the interviewees. Additionally, the paper presents a case study on how building authority can be integrated into Digital Twins and leverage its use throughout the lifecycle of a building. Future work can further investigate the challenges and develop prototypes that can help in quantifying the benefits of implementing Digital Twins on a Construction Project.
The evolution of scientific advances has often 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 can and will jointly and profoundly shape the future of transportation. However, it is not certain how the individual and synergistic benefits to be earned from these technologies is related to their prevailing levels of development. 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 transportation systems connectivity and automation. In this discussion paper, we attempt to address some of these issues. The paper first reviews the technological concepts of systems automation and systems connectivity, and how they prospectively, from an individual and collective perspective, impact road transportation efficiency and safety. The paper also discusses the separate and common benefits of connectivity and automation, and their possible holistic effects in terms of these benefits where they overlap. The paper suggests that at the current time, the sibling relationship seems to be lopsided: 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. The paper argues that future trends regarding market adoption of these two technologies and their relative pace of advancement or regulation, will shape the future synergies between them.
Historical and future spatially explicit population and gross domestic product (GDP) data are essential for the analysis of future climate risks. Unlike population projections that are generally ...available, GDP projections—particularly for scenarios compatible with shared socioeconomic pathways (SSPs)—are limited. Our objective is to perform a high-resolution and long-term GDP estimation under SSPs utilizing a wide variety of geographic auxiliary information. We estimated the GDP in a 1/12-degree grid scale. The estimation is done through downscaling of historical GDP data for 1850–2010 and SSP future scenario data for 2010–2100. In the downscaling, we first modeled the spatial and economic interactions among cities and projected different future urban growth patterns according to the SSPs. Subsequently, the projected patterns and other auxiliary geographic data were used to estimate the gridded GDP distributions. Finally, the GDP projections were visualized via three-dimensional mapping to enhance the clarity for multiple stakeholders. Our results suggest that the spatial pattern of urban and peri-urban GDP depends considerably on the SSPs; the GDP of the existing major cities grew rapidly under SSP1, moderately grew under SSP 2 and SSP4, slowly grew under SSP3, and dispersed growth under SSP5.
Advances of the analytical, numerical, experimental and field-measurement approaches in wind engineering offers unprecedented volume of data that, together with rapidly evolving learning algorithms ...and high-performance computational hardware, provide an opportunity for the community to embrace and harness full potential of machine learning (ML). This contribution examines the state of research and practice of ML for its applications to wind engineering. In addition to ML applications to wind climate, terrain/topography, aerodynamics/aeroelasticity and structural dynamics (following traditional Alan G. Davenport Wind Loading Chain), the review also extends to cover wind damage assessment and wind-related hazard mitigation and response (considering emerging performance-based and resilience-based wind design methodologies). This state-of-the-art review suggests to what extend ML has been utilized in each of these topic areas within wind engineering and provides a comprehensive summary to improve understanding how learning algorithms work and when these schemes succeed or fail. Moreover, critical challenges and prospects of ML applications in wind engineering are identified to facilitate future research efforts.
Cities are losing green space driving an extinction of nature experiences for urban communities. Incremental green space loss can trigger a ratcheting-down effect where individuals' expectations of ...nature continually decrease through time. This loss of everyday nature experiences may produce a citizenry with reduced knowledge and appreciation of biodiversity and the environment. In this review, we examine how urban gardens, as urban spaces that bring people into close contact with nature in an otherwise built environment, can combat this ratcheting-down effect by encouraging interactions and knowledge of nature. We review three ways urban gardens may engender greater biophilia: (1) the provision of natural elements to expose urban dwellers to the diversity of plants, animals, and soils that they would otherwise not encounter in their daily life; (2) fostering a greater understanding of natural processes that affect food production (e.g., climate processes, pest control, pollination) and thus the natural world; and (3) the provision of a safe space in which humans can corporeally interact with nature elements to develop greater fascination with nature. Thus, urban gardens can engender biophilia for their participants by increasing exposure, positive interactions, and knowledge of nature, potentially changing people's attitudes to nature. We present examples from a variety of urban gardens to show how these spaces can be designed using biophilic thinking to enhance people's everyday nature experiences and their drive to interact with the natural world.
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