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There are many big machines and busy people involved in building a road, and this picture book, with its rambunctious rhymes and noisy fun, follows them every step of the way, from clearing a pathway to rolling the tar to sweeping up at the end.
Book
Utilization of SiOsub.2 Nanoparticles in Developing Superhydrophobic Coatings for Road Construction: A Short Review
The application of superhydrophobic (SH) coatings in road construction has attracted growing attention due to their potential to improve surface durability, reduce cracking, and enhance skid resistance. Among various materials, SiO[sub.2] nanoparticles have emerged as key components in SH coatings by contributing essential surface roughness and hydrophobicity. This review paper analyzes the role of SiO[sub.2] nanoparticles in enhancing the water-repellent properties of coatings applied to road surfaces, particularly concrete and asphalt. Emphasis is placed on their influence on road longevity, reduced maintenance, and overall performance under adverse weather conditions. Furthermore, this review compares functionalization techniques for SiO[sub.2] using different hydrophobic modifiers, evaluating their efficiency, cost effectiveness, and scalability for large-scale infrastructure. In addition to highlighting recent advancements, this study discusses persistent challenges—including environmental compatibility, mechanical wear, and long-term durability—that must be addressed for practical implementation. By offering a critical assessment of current approaches and future prospects, this short review aims to guide the development of robust, high-performance SH coatings for sustainable road construction.
Journal Article
Energy efficient non-road hybrid electric vehicles : advanced modeling and control
Analyzing the main problems in the real-time control of parallel hybrid electric powertrains in non-road applications, which work in continuous high dynamic operation, this book gives practical insight in to how to maximize the energetic efficiency and drivability of such powertrains. The book addresses an energy management control structure, which considers all constraints of the physical powertrain and uses novel methodologies for the prediction of the future load requirements to optimize the controller output in terms of an entire work cycle of a non-road vehicle. The load prediction includes a methodology for short term loads as well as for an entire load cycle by means of a cycle detection. A maximization of the energetic efficiency can so be achieved, which is simultaneously a reduction in fuel consumption and exhaust emissions. Readers will gain a deep insight into the necessary topics to be considered in designing an energy and battery management system for non-road vehicles and that only a combination of the management systems can significantly increase the performance of a controller.
Book
Monitoring and Identification of Road Construction Safety Factors via UAV
The safety of road construction is one of the most important concerns of construction managers for the following reasons: long-span construction operation, no fixed monitoring cameras, and huge impacts on existing traffic, while the managers still rely on manual inspection and a lack of image records. With the fast development of Unmanned Aerial Vehicle (UAV) and Artificial Intelligence (AI), monitoring safety concerns of road construction sites becomes easily accessible. This research aims to integrate UAVs and AI to establish a UAV-based road construction safety monitoring platform. In this study, road construction safety factors including constructors, construction vehicles, safety signs, and guardrails are defined and monitored to make up for the lack of image data at the road construction site. The main findings of this study include three aspects. First, the flight and photography schemes are proposed based on the UAV platform for information collection for road construction. Second, deep learning algorithms including YOLOv4 and DeepSORT are utilized to automatically detect and track safety factors. Third, a road construction dataset is established with 3594 images. The results show that the UAV-based monitoring platform can help managers with security inspection and recording images.
Journal Article
Evolution of a retail streetscape : DP Architects on Orchard Road
Singapore's Orchard Road sits alongside New York's Fifth Avenue and Paris' Champs Elysees in the pantheon of prestigious retail districts. For more than 40 years, DP Architects has contributed to the development of Orchard Road's architectural typography, the regionalism of which is flavoured with a uniqueness born of inventiveness and an accommodation of contextual forces. 'The Orchard Road Experience' is much more than simply a lush visual and textual narrative showcasing DP Architects' extensive contribution to the character, growth and personality of the famous Singapore shopping and entertainment precinct. It also explores the concept of retail architectural typology generally, provides a brief contextual history of Singapore from British colonisation onwards, outlines the development and evolution of Orchard Road in particular, and explores some of the world's other famous shopping streets.
Book
Highway Construction Safety Analysis Using Large Language Models
The highway construction industry carries substantial safety risks for workers, necessitating thorough accident analyses to implement effective preventive measures. Current research lacks comprehensive investigations into safety incidents, relying heavily on conventional statistical methods and overlooking valuable textual information in publicly available databases. This study leverages a state-of-the-art large language model (LLM), specifically OpenAI’s GPT-3.5 model. The primary focus is to enhance text-based incident analysis that is sourced from OSHA’s Severe Injury Reports (SIR) database. By incorporating novel natural language processing (NLP) techniques, dimensionality reduction, clustering algorithms, and LLM prompting of incident narratives, the study aims to develop an approach to the analysis of major accident causes in highway construction. The resulting cluster analysis, coupled with LLM summarization and cause identification, reveals the major accident types, such as heat-related and struck-by injuries, as well as commonalities between incidents. This research showcases the potential of artificial intelligence (AI) and LLM technology in data-driven analysis. By efficiently processing textual data and providing insightful analysis, the study fosters practical implications for safety professionals and the development of more effective accident prevention and intervention strategies within the industry.
Journal Article
Advances in materials and pavement prediction II : contributions to the 2nd International Conference on Advances in Materials and Pavement Performance Prediction (AM3P 2020), 27-29 May 2020, San Antonio, TX, USA
\"Inspired from the legacy of the previous four 3DFEM conferences held in Delft and Athens as well as the successful 2018 AM3P conference held in Doha, the 2020 AM3P conference continues the pavement mechanics theme including pavement models, experimental methods to estimate model parameters, and their implementation in predicting pavement performance. The AM3P conference is organized by the Standing International Advisory Committee (SIAC), at the time of this publication chaired by Professors Tom Scarpas, Eyad Masad, and Amit Bhasin. Advances in Materials and Pavement Performance Prediction II includes over 111 papers presented at the 2020 AM3P Conference. The technical topics covered include: rigid pavements, pavement geotechnics, statistical and data tools in pavement engineering, pavement structures, asphalt mixtures, asphalt binders. The book will be invaluable to academics and engineers involved or interested in pavement engineering, pavement models, experimental methods to estimate model parameters, and their implementation in predicting pavement performance.\"-- Provided by publisher.
Book
Vibrotactile Alerting to Prevent Accidents in Highway Construction Work Zones: An Exploratory Study
Struck-by accidents are the leading cause of injuries in highway construction work zones. Despite numerous safety interventions, injury rates remain high. As workers’ exposure to traffic is sometimes unavoidable, providing warnings can be an effective way to prevent imminent threats. Such warnings should consider work zone conditions that can hinder the timely perception of alerts, e.g., poor visibility and high noise level. This study proposes a vibrotactile system integrated into workers’ conventional personal protective equipment (PPE), i.e., safety vests. Three experiments were conducted to assess the feasibility of using vibrotactile signals to warn workers in highway environments, the perception and performance of vibrotactile signals at different body locations, and the usability of various warning strategies. The results revealed vibrotactile signals had a 43.6% faster reaction time than audio signals, and the perceived intensity and urgency levels on the sternum, shoulders, and upper back were significantly higher than the waist. Among different notification strategies used, providing a moving direction imposed significantly lower mental workloads and higher usability scores than providing a hazard direction. Further research should be conducted to reveal factors that affect alerting strategy preference towards a customizable system to elicit higher usability among users.
Journal Article
Monitoring and stability analysis of roadbed & high slope prior to highway construction
Monitoring in highway projects is significant for the safety, efficiency and quality of construction. This paper proposes a detailed method for monitoring soft-soil roadbed and high-slope, and the layered settlement gauge and total station are employed to carry out experimental monitoring. The law and stability of soft-soil roadbed settlement and deformation under high-slope are further analyzed. The results show that the cumulative values of roadbed settlement and slope platform deformation in general both increase with the increase of monitoring time. However, near 180 days, an abnormal settlement phenomenon was monitored on both sides of the highway with a maximum value of 9.44 mm. This phenomenon was captured at gauge #1, #3, and #4 on observation stake 3, and exact oppositely, it was also observed at gauge #2 on observation stake 4. Moreover, unusual deformations of the high-slope platforms occurred over a period of 10 to 30 days, and these unusual settlements and deformations are indicative of the highway’s instability. Therefore, the monitoring on soft-soil roadbed settlement and high-slope deformation can provide reference for highway construction.
Journal Article
GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran
This study presents a landslide susceptibility assessment for the
Caspian forest using frequency ratio and index of entropy models within
geographical information system. First, the landslide locations were
identified in the study area from interpretation of aerial photographs
and multiple field surveys. 72 cases (70 %) out of 103 detected
landslides were randomly selected for modeling, and the remaining 31
(30 %) cases were used for the model validation. The
landslide-conditioning factors, including slope degree, slope aspect,
altitude, lithology, rainfall, distance to faults, distance to streams,
plan curvature, topographic wetness index, stream power index, sediment
transport index, normalized difference vegetation index (NDVI), forest
plant community, crown density, and timber volume, were extracted from
the spatial database. Using these factors, landslide susceptibility and
weights of each factor were analyzed by frequency ratio and index of
entropy models. Results showed that the high and very high
susceptibility classes cover nearly 50 % of the study area. For
verification, the receiver operating characteristic (ROC) curves were
drawn and the areas under the curve (AUC) calculated. The verification
results revealed that the index of entropy model (AUC = 75.59 %) is
slightly better in prediction than frequency ratio model (AUC = 72.68
%). The interpretation of the susceptibility map indicated that NDVI,
altitude, and rainfall play major roles in landslide occurrence and
distribution in the study area. The landslide susceptibility maps
produced from this study could assist planners and engineers for
reorganizing and planning of future road construction and timber
harvesting operations.
Journal Article