Hasil untuk "Highway engineering. Roads and pavements"

Mikio Nakano, Hironori Takeuchi, Sadahiro Yoshikawa et al.

This paper proposes to refer to the field of software engineering related to the life cycle of dialogue systems as Dialogue Systems Engineering, and surveys this field while also discussing its future directions. With the advancement of large language models, the core technologies underlying dialogue systems have significantly progressed. As a result, dialogue system technology is now expected to be applied to solving various societal issues and in business contexts. To achieve this, it is important to build, operate, and continuously improve dialogue systems correctly and efficiently. Accordingly, in addition to applying existing software engineering knowledge, it is becoming increasingly important to evolve software engineering tailored specifically to dialogue systems. In this paper, we enumerate the knowledge areas of dialogue systems engineering based on those of software engineering, as defined in the Software Engineering Body of Knowledge (SWEBOK) Version 4.0, and survey each area. Based on this survey, we identify unexplored topics in each area and discuss the future direction of dialogue systems engineering.

Woomin Song, Jihoon Tack, Sangwoo Mo et al.

State-space models (SSMs) offer a promising architecture for sequence modeling, providing an alternative to Transformers by replacing expensive self-attention with linear recurrences. In this paper, we propose a simple yet effective trick to enhance SSMs within given computational budgets by sparsifying them. Our intuition is that tokens in SSMs are highly redundant due to gradual recurrent updates, and dense recurrence operations block the delivery of past information. In particular, we observe that upper layers of SSMs tend to be more redundant as they encode global information, while lower layers encode local information. Motivated by this, we introduce Simba, a hierarchical sparsification method for SSMs based on token pruning. Simba sparsifies upper layers more than lower layers, encouraging the upper layers to behave like highways. To achieve this, we propose a novel token pruning criterion for SSMs, measuring the global impact of tokens on the final output by accumulating local recurrences. We demonstrate that Simba outperforms the baseline model, Mamba, with the same FLOPS in various natural language tasks. Moreover, we illustrate the effect of highways, showing that Simba not only enhances efficiency but also improves the information flow across long sequences. Code is available at https://github.com/woominsong/Simba.

Mahdi Jaberzadeh Ansari, Ann Barcomb

Although it has been more than four decades that the first components-based software development (CBSD) studies were conducted, there is still no standard method or tool for component selection which is widely accepted by the industry. The gulf between industry and academia contributes to the lack of an accepted tool. We conducted a mixed methods survey of nearly 100 people engaged in component-based software engineering practice or research to better understand the problems facing industry, how these needs could be addressed, and current best practices employed in component selection. We also sought to identify and prioritize quality criteria for component selection from an industry perspective. In response to the call for CBSD component selection tools to incorporate recent technical advances, we also explored the perceptions of professionals about AI-driven tools, present and envisioned.

Franko Yezhak, Iryna Haliuk, Lesia Tarayevska

Introduction. AI is actively being implemented in various areas of economic activity, automating routine processes, reducing manual labour and increasing productivity. Current market trends indicate that the growing share of Generation Z and Alpha employees will contribute to even greater automation, as it is the younger generation that positively perceives the replacement of humans with technology in the performance of repetitive tasks. Problem statement. Despite the active interest of businesses in the possibilities of using artificial intelligence in their activities, companies still lack sufficient knowledge and understanding of how AI can be used — comprehensively or fragmentarily, improving the work of individual departments or the enterprise as a whole, what resources are needed for this and what the results will be, what data is needed for AI to work in a particular company, and how accurate the results of artificial intelligence are, etc. These issues need to be addressed in order to develop an understanding of a systematic approach to the application of artificial intelligence in the activities of an enterprise, starting with marketing forecasting processes and ending with the resulting optimisation of resource use and increased competitiveness of the company. Purpose. The purpose of the article is to present a comprehensive approach to the application of artificial intelligence in the work of an enterprise, to identify problematic issues in the application of AI and the advantages of its use in all areas of the company’s activities in order to optimise the use of its resources and, as a result, gain competitive advantages in the market. Materials and methods. To achieve the objective of the study, the following methods were used: the method of analysis and synthesis to study literary sources and publications, the comparative method to compare traditional forecasting methods with approaches based on artificial intelligence, and a systematic approach to consider artificial intelligence as a tool that comprehensively affects all areas of the enterprise’s activities. Results. Particular attention is paid to the use of AI in marketing forecasting as a key element of enterprise planning. The advantages of using modern forecasting methods, in particular based on Big Data and machine learning, compared to traditional statistical approaches are described. The practical aspects of applying S&OP to improve the management of production resources, supply and personnel are highlighted. The paper highlights the importance of quality forecasts for operational planning, resource provision, logistics decisions and demand management. Traditional statistical forecasting methods are compared with approaches based on Big Data and machine learning. The latter enable higher accuracy, reveal complex interrelationships and respond quickly to changes in the market environment. Attention is also focused on the need for high-quality information support and qualified personnel for the effective use of these technologies.

Aivaras Mockus, Vilma Jasiūnienė

To enhance the safety and integration of vulnerable road users (pedestrians and cyclists) into the transportation system, a growing number of European cities, including Vilnius, are aligning their traffic management solutions with the concept of street humanisation. The Naujamiestis district is the first city district in Vilnius to undergo these changes. To assess the impact of street humanisation strategies on road safety, this preliminary paper analyses data from the road accident history database for the period 2018–2021 and results from instantaneous vehicle speed analysis. The results of the road accident database history are presented graphically and using heat maps. The data from the instantaneous speed analysis are analysed using the Kruskal- Wallis and Mann-Whitney-Wilcoxon non-parametric statistical criteria. The findings indicate that the concept of street humanisation is effective in enforcing 30 km/h speed limit. The analysis of road accident data history shows a decrease in the number of accidents and their consequences in the study area and the entire city of Vilnius between 2018 and 2022. However, it is currently challenging to determine whether street humanisation concept directly contributes to the decline in road accidents. Therefore, it is proposed to revisit the analysis in the future.

Ario Bintang Koesalamwardi, Susy Fatena Rostiyanti, Fitri Amaliny

Bangunan hijau (green building) merupakan salah satu cara untuk mengatasi permasalahan lingkungan yang bersumber dari aktivitas konstruksi dan operasional bangunan karena pada dasarnya konsep bangunan hijau dimulai dari perancangan, pembangunan, hingga operasional dan perawatannya memperhatikan dampaknya terhadap lingkungan dan penghuninya. Namun sampai saat ini pembangunan dengan konsep bangunan hijau masih sangat sedikit terutama untuk bangunan hunian. Konsumen enggan untuk menerapkan konsep bangunan hijau untuk hunian mereka, demikian pula dengan pihak pengembang, dimana keengganan untuk membuat dan memasarkan unit hunian dengan konsep bangunan hijau. Penelitian ini bertujuan untuk mengidentifikasi faktor-faktor yang menjadi penghambat dari penerapan bangunan hijau untuk bangunan residensial dari perspektif konsumen dan developer menggunakan metode meta analisis dari penelitian terdahulu dalam 10 tahun ke belakang. Penelitian ini menemukan 5 faktor utama yang menghambat penerapan bangunan hijau untuk bangunan residensial dari perspektif konsumen, yaitu: biaya investasi yang lebih tinggi, tidak adanya insentif dari pemerintah, kurangnya pendidikan dan pengetahuan tentang bangunan hijau, kurangnya kontraktor yang ahli dan berpengalaman dalam bangunan hijau serta kurangnya kesadaran masyarakat tentang bangunan ramah lingkungan. Dengan teridentifikasinya 5 faktor utama ini, penelitian selanjutnya dapat lebih dapat difokuskan untuk permasalahan utama tersebut.

Rahul Raj Singh, Mumtahin Hasnat, Muhammed Emin Kutay et al.

Pavement infrastructure is vital in providing services and links between various sectors of society. Therefore, the preservation and maintenance of these roads are critical to attaining a pavement network in good condition throughout its service life. Various performance indicators like the international roughness index (IRI), pavement condition index (PCI), and present serviceability rating (PSR) have been used by the state department of transportation (DOT) and highway agencies for evaluating pavement surface conditions and planning future maintenance strategies. Limited data availability, multiple distresses depending on region, lack of correlation of these condition indices to maintenance strategies, and data collection limitations pose a challenge for applying these indices to local conditions. This paper compares condition indices of different states for rigid pavements. Further, using a specific condition index for local conditions is also highlighted. For this purpose, five states and their corresponding condition indices were evaluated and compared to the Michigan DOT distress index (DI). These states include Virginia, Minnesota, North Dakota, Louisiana, and Oregon. The corresponding distresses of each condition index were converted to make them compatible with the MDOT DI. This study used the MDOT's pavement management system (PMS) database to evaluate each condition index for 433 rigid pavement sections. Each distress index was plotted against MDOT DI and compared using a paired t-test. Results show that the condition indices of Virginia and Minnesota are comparable to DI in terms of the Spearman correlation value. The t-test results show that except for Virgina, condition indices from other states statistically differ from DI. Therefore, one can't use those directly for local conditions in Michigan. This paper presents the evaluation and data requirements for each condition index and its impact on selecting a maintenance treatment.

Roman Smolyanyuk, Vitaly Onufriev

Introduction. Current developments that are being implemented by modern organizations must include not only initial methodological complexes of disciplines (modules), but also security programs aimed at mastering professional competencies. The optimal way to develop competencies is through virtual laboratories, which are modeled in an electronic lighting environment on real-world objects. Problem Statement. The problem of this article is the need to promote current technologies of virtual modeling, which is considered as a tool for increasing the brightness of engineering lighting. Purpose. development and implementation of virtual 3D laboratories to obtain practical skills from testing real-life materials. Materials and methods. 3D graphics editor, software Unity 2022.3.4.

Ali Saleh, László Gáspár

Introduction. There is a world-wide trend to also increase the sustainability of the road sector. The growing use of various industrial by-products, together with economical and eco-friendly construction and maintenance techniques can be observed in many countries. Problem Statement. The utilization of warm mix asphalt and the use of relatively high share of reclaimed asphalt materials in new asphalt mixtures can have negative features, as well. Purpose. Modelling indirect tensile strength of warm mix asphalt with variable reclaimed asphalt pavement (RAP) content was aimed at based on Hungarian laboratory test series. Materials and Methods. Three models were developed for the prediction of indirect tensile strength, this important asphalt mechanical parameter of warm mix asphalt as a function of Foamed Bitumen Content (FBC) and the RAP share in the new asphalt mixture. Among others, linear regression analysis and support vector regression (SVR) models were applied. Results. A comparison performed between Random Forest and Neural Network models illustrates and proves the versatility of machine learning techniques in predicting asphalt indirect tensile strength values both in wet and dry conditions. The research work enhances our understanding of the multifaceted dynamics influencing the performance of asphalt mixtures, offering valuable insights for optimizing pavement design and construction practices in diverse environmental conditions. The model developed successfully captures the relationship between the ITS (wet and dry) metric and its contributing factors, Foamed Bitumen Content (FBC) and RAP, with a high R-squared value.

Hanna Abi Akl

The "Information Retrieval in Software Engineering (IRSE)" at FIRE 2023 shared task introduces code comment classification, a challenging task that pairs a code snippet with a comment that should be evaluated as either useful or not useful to the understanding of the relevant code. We answer the code comment classification shared task challenge by providing a two-fold evaluation: from an algorithmic perspective, we compare the performance of classical machine learning systems and complement our evaluations from a data-driven perspective by generating additional data with the help of large language model (LLM) prompting to measure the potential increase in performance. Our best model, which took second place in the shared task, is a Neural Network with a Macro-F1 score of 88.401% on the provided seed data and a 1.5% overall increase in performance on the data generated by the LLM.

Aldona Jarašūnienė, Domantas Lapėnas

The rapidly growing global production and trade increase the demand for transport and logistics, leading to the establishment of increasingly more new companies providing these services. This also results in a much larger number of vehicles, which is not always a positive thing. Excessive vehicle traffic leads to an increase in environmental pollution, noise and traffic accidents, which have a highly negative impact on society and, more specifically, on human health. The negative impact on the environment has been increasing, and therefore representatives of national politics, the transport and logistics sectors should be alarmed and take action to reduce the environmental pollution. The development of the transport system should be carefully planned and geared towards achieving green and sustainable transport. In order to create a sustainable freight transport network, certain alternatives that could help to stop or at least reduce the process of environmental pollution should be pursued. One of such alternatives is the use of multimodal transport, which involves several different modes of transport for transporting one freight. This both distributes and reduces the burden on the currently dominant road transport mode, which contributes significantly to environmental pollution and is not sufficiently sustainable.

José Pablo Díaz-Arrieta, Josué Rivas Valverde, Jaime Allen Monge

En este trabajo se aborda, de forma general, un análisis de las políticas públicas enfocadas en el transporte eléctrico modalidad autobús en Costa Rica durante los años 2018- 2022. Para esto, se aporta un análisis documental sobre el marco jurídico de Costa Rica que incentiva la movilidad eléctrica, los planes de descarbonización y reducción de Gases de Efecto Invernadero (GEI) y las leyes que establecen las competencias institucionales para llevar a cabo el proceso de implementación de buses eléctricos. Asimismo, se incorpora un análisis de entrevistas semiestructuradas a actores de la Asamblea Legislativa, el Instituto Costarricense de Electricidad (ICE), la Agencia de Cooperación Alemana para el Desarrollo (GIZ) y de la Dirección del Consejo Nacional de Transporte Público (CTP). A raíz del análisis, se concluye que existen barreras institucionales y limitaciones económicas significativas que obstaculizan la implementación de buses eléctricos, pero se encuentra evidencia de oportunidades para generar modelos de negociación con el sector autobusero en aras de mejorar el sistema de transporte público en beneficio del interés público. 

Tomislav Maric, Dennis Gläser, Jan-Patrick Lehr et al.

University research groups in Computational Science and Engineering (CSE) generally lack dedicated funding and personnel for Research Software Engineering (RSE), which, combined with the pressure to maximize the number of scientific publications, shifts the focus away from sustainable research software development and reproducible results. The neglect of RSE in CSE at University research groups negatively impacts the scientific output: research data - including research software - related to a CSE publication cannot be found, reproduced, or re-used, different ideas are not combined easily into new ideas, and published methods must very often be re-implemented to be investigated further. This slows down CSE research significantly, resulting in considerable losses in time and, consequentially, public funding. We propose a RSE workflow for Computational Science and Engineering (CSE) that addresses these challenges, that improves the quality of research output in CSE. Our workflow applies established software engineering practices adapted for CSE: software testing, result visualization, and periodical cross-linking of software with reports/publications and data, timed by milestones in the scientific publication process. The workflow introduces minimal work overhead, crucial for university research groups, and delivers modular and tested software linked to publications whose results can easily be reproduced. We define research software quality from a perspective of a pragmatic researcher: the ability to quickly find the publication, data, and software related to a published research idea, quickly reproduce results, understand or re-use a CSE method, and finally extend the method with new research ideas.

Daniele Di Pompeo, Michele Tucci

Software model optimization is the task of automatically generate design alternatives, usually to improve quality aspects of software that are quantifiable, like performance and reliability. In this context, multi-objective optimization techniques have been applied to help the designer find suitable trade-offs among several non-functional properties. In this process, design alternatives can be generated through automated model refactoring, and evaluated on non-functional models. Due to their complexity, this type of optimization tasks require considerable time and resources, often limiting their application in software engineering processes. In this paper, we investigate the effects of using a search budget, specifically a time limit, to the search for new solutions. We performed experiments to quantify the impact that a change in the search budget may have on the quality of solutions. Furthermore, we analyzed how different genetic algorithms (i.e., NSGA-II, SPEA2, and PESA2) perform when imposing different budgets. We experimented on two case studies of different size, complexity, and domain. We observed that imposing a search budget considerably deteriorates the quality of the generated solutions, but the specific algorithm we choose seems to play a crucial role. From our experiments, NSGA-II is the fastest algorithm, while PESA2 generates solutions with the highest quality. Differently, SPEA2 is the slowest algorithm, and produces the solutions with the lowest quality.

Allan Gerardo Ureña Bermúdez, Jaime Allen

El factor camión es uno de los principales factores dentro de la estimación de la demanda de cargas que tendrá el pavimento de una carretera. El objetivo de este estudio es analizar los pesajes realizados entre los años 2008 y 2017. A partir de lo anterior, se contabilizaron más de 12 millones de vehículos pesados, los cuales estaban distribuidos en la estación de pesaje de Limón y Cartago. Para cada vehículo se calculó el factor camión asociado y se determinó si el vehículo, según el peso de cada uno de sus ejes, incumplía lo establecido en el Decreto N° 31363 del Ministerio de Obras Públicas y Transportes A partir de las variables factor camión y el incumplimiento se realizan modelos econométricos. Estos modelos toman el efecto de distintas variables de tipo de camión, espaciales y temporales, y el tipo de medición, sobre el factor camión y el incumplimiento. Los modelos econométricos consideran las siguientes variables: año, mes, tipo de vehículo, día, hora y el tipo de medición aplicado en las estaciones analizadas. Uno de los resultados más relevantes del presente estudio es que hubo un incremento en los factores camión y porcentajes de incumplimiento analizados al pasar de un sistema de pesaje manual a uno automatizado.

Jorge Alberto Vasquez Rodriguez

La reorientación de la política pública de la infraestructura vial nacional de 1998 incorporó aspectos claves del Nuevo Estilo Nacional de Desarrollo vigente en el país en el momento de su oficialización. Este estilo desde principios de los años 80 promulga el protagonismo del mercado, en deterioro del papel del Estado, de ahí, que concordante con el mismo se trasladó la construcción, conservación y mantenimiento de la Red Vial Nacional del Estado al empresario privado, mediante contrataciones administrativas a cargo de un ente público denominado el Consejo Nacional de Vialidad, en donde en su consejo de administración, máximo jerarca de esa institución, estarían incorporados tres miembros de los empresarios organizados del mercado de la construcción de carreteras, con voz y voto, que coadministrarían la institución, con otros tres miembros representantes del gobierno de turno y uno de los gobiernos locales, con recursos provenientes de un Fondo Vial Nacional. La formulación de la política presenta un déficit de formulación, generada principalmente por la reformulación del problema público y de la estrategia de intervención de la política pública, generada desde “lo político” en la Asamblea Legislativa en el primer cuatrimestre de 1998, de ahí, que no se le brindó una posible respuesta a algunas de las causas del problema público como la relocalización de servicios, las expropiaciones, la coordinación con entes públicos que determinan el avance de la obra vial, la protección del medio ambiente, el control y calidad de las carreteras, el control excesivo sobre las obras viales, ejercidos directamente o indirectamente por entes públicos controladores, generándose así un déficit de formulación en la política pública, que incide negativamente en los resultados de la misma

Peng Xu, Alejandro Salado, Xinwei Deng

Verification is a critical process in the development of engineered systems. Through verification, engineers gain confidence in the correct functionality of the system before it is deployed into operation. Traditionally, verification strategies are fixed at the beginning of the system's development and verification activities are executed as the development progresses. Such an approach appears to give inferior results as the selection of the verification activities does not leverage information gained through the system's development process. In contrast, a set-based design approach to verification, where verification activities are dynamically selected as the system's development progresses, has been shown to provide superior results. However, its application under realistic engineering scenarios remains unproven due to the large size of the verification tradespace. In this work, we propose a parallel tempering approach (PTA) to efficiently explore the verification tradespace. First, we formulate exploration of the verification tradespace as a tree search problem. Second, we design a parallel tempering (PT) algorithm by simulating several replicas of the verification process at different temperatures to obtain a near-optimal result. Third, We apply the PT algorithm to all possible verification states to dynamically identify near-optimal results. The effectiveness of the proposed PTA is evaluated on a partial model of a notional satellite optical instrument.

Deng Yongqiang, Wang Dengjiang, Cao Gang et al.

As the roadside perception plays an increasingly significant role in the Connected Automated Vehicle Highway(CAVH) technologies, there are immediate needs of challenging real-world roadside datasets for bench marking and training various computer vision tasks such as 2D/3D object detection and multi-sensor fusion. In this paper, we firstly introduce a challenging BAAI-VANJEE roadside dataset which consist of LiDAR data and RGB images collected by VANJEE smart base station placed on the roadside about 4.5m high. This dataset contains 2500 frames of LiDAR data, 5000 frames of RGB images, including 20% collected at the same time. It also contains 12 classes of objects, 74K 3D object annotations and 105K 2D object annotations. By providing a real complex urban intersections and highway scenes, we expect the BAAI-VANJEE roadside dataset will actively assist the academic and industrial circles to accelerate the innovation research and achievement transformation in the field of intelligent transportation in big data era.

Thilanka Munasinghe, HR Pasindu

We propose how a developing country like Sri Lanka can benefit from privacy-enabled machine learning techniques such as Federated Learning to detect road conditions using crowd-sourced data collection and proposed the idea of implementing a Digital Twin for the national road system in Sri Lanka. Developing countries such as Sri Lanka are far behind in implementing smart road systems and smart cities compared to the developed countries. The proposed work discussed in this paper matches the UN Sustainable Development Goal (SDG) 9: "Build Resilient Infrastructure, Promote Inclusive and Sustainable Industrialization and Foster Innovation". Our proposed work discusses how the government and private sector vehicles that conduct routine trips to collect crowd-sourced data using smartphone devices to identify the road conditions and detect where the potholes, surface unevenness (roughness), and other major distresses are located on the roads. We explore Mobile Edge Computing (MEC) techniques that can bring machine learning intelligence closer to the edge devices where produced data is stored and show how the applications of Federated Learning can be made to detect and improve road conditions. During the second phase of this study, we plan to implement a Digital Twin for the road system in Sri Lanka. We intend to use data provided by both Dedicated and Non-Dedicated systems in the proposed Digital Twin for the road system. As of writing this paper, and best to our knowledge, there is no Digital Twin system implemented for roads and other infrastructure systems in Sri Lanka. The proposed Digital Twin will be one of the first implementations of such systems in Sri Lanka. Lessons learned from this pilot project will benefit other developing countries who wish to follow the same path and make data-driven decisions.

Malgorzata Linek, Piotr Nita, Adam Poswiata

Abstract: The technical reliability of concrete airport pavements evolves mainly when the project and its technical and functional solutions in all types of industry works is being developed. In the operation process, the technical reliability of the pavement can be shaped as a result of its' modernization and reconstruction. The effect depends primarily on the quality of the works. Currently, the contractors have high-quality materials and specialized equipment minimising human factor in the process of preparing and placing the concrete mix. Potentially high qualifications of technical personnel and other members of the works crew should be a guarantee of high quality works. The operation process must be characterized by a high level of technical culture and rational maintenance procedures. The publication presents the theoretical model of modernized and reconstructed airport's reliability, paying particular attention to the accuracy and efficiency of its technological implementation. Keywords: Pavement reliability; Quality of pavement works; Operational condition; AFE (airport functional elements)