Hasil untuk "Highway engineering. Roads and pavements"

Al Muttakin, Saikat Mondal, Chanchal K. Roy

Replication packages are crucial for enabling transparency, validation, and reuse in software engineering (SE) research. While artifact sharing is now a standard practice and even expected at premier SE venues such as ICSE, the practical usability of these replication packages remain underexplored. In particular, there is a marked lack of studies that comprehensively examine the executability and reproducibility of replication packages in SE research. In this paper, we aim to fill this gap by evaluating 100 replication packages published in ICSE proceedings over the past decade (2015 - 2024). We assess the (1) executability of the replication packages, (2) efforts and modifications required to execute them, (3) challenges that prevent executability, and (4) reproducibility of the original findings for those that are executable. We spent approximately 650 person-hours in total to execute the artifacts and reproduce the study findings. Our analysis shows that only 40 of the 100 evaluated artifacts were fully executable. Among these, 32.5% ran without any modification. However, even executable artifacts required varying levels of effort: 17.5% required low effort, while 82.5% required moderate to high effort to execute successfully. We identified five common types of modifications and 13 challenges that lead to execution failure, encompassing environmental, documentation, and structural issues. Among the executable artifacts, only 35% (14 out of 40) reproduced the original results. These findings highlight a notable gap between artifact availability, executability, and reproducibility. Our study proposes three actionable guidelines to improve the preparation, documentation, and review of research artifacts, thereby strengthening the rigor and sustainability of open science practices in SE research.

Tomasz Stoeck

Abstract: The article presents selected organizational changes in Szczecin's public transportation system between 2014 and 2024. The focus is primarily on direct connections between the city's two central hubs, located on opposite sides of the Oder River. The main evaluation criterion was the number of bus and tram line services, considering the type of rolling stock used, dysfunctions within the transportation system, and alternative travel options. The discussion also includes ongoing and planned investments, whose completion may influence the transportation preferences of passengers. Keywords: Public transportation; Direct connections; Rolling stock and rail vehicles

Sukanya Randhawa, Guntaj Randhawa, Clemens Langer et al.

Resilient road infrastructure is a cornerstone of the UN Sustainable Development Goals. Yet a primary indicator of network functionality and resilience is critically lacking: a comprehensive global baseline of road surface information. Here, we overcome this gap by applying a deep learning framework to a global mosaic of Planetscope satellite imagery from 2020 and 2024. The result is the first global multi-temporal dataset of road pavedness and width for 9.2 million km of critical arterial roads, achieving 95.5% coverage where nearly half the network was previously unclassified. This dataset reveals a powerful multi-scale geography of human development. At the planetary scale, we show that the rate of change in pavedness is a robust proxy for a country's development trajectory (correlation with HDI = 0.65). At the national scale, we quantify how unpaved roads constitute a fragile backbone for economic connectivity. We further synthesize our data into a global Humanitarian Passability Matrix with direct implications for humanitarian logistics. At the local scale, case studies demonstrate the framework's versatility: in Ghana, road quality disparities expose the spatial outcomes of governance; in Pakistan, the data identifies infrastructure vulnerabilities to inform climate resilience planning. Together, this work delivers both a foundational dataset and a multi-scale analytical framework for monitoring global infrastructure, from the dynamics of national development to the realities of local governance, climate adaptation, and equity. Unlike traditional proxies such as nighttime lights, which reflect economic activity, road surface data directly measures the physical infrastructure that underpins prosperity and resilience - at higher spatial resolution.

Xinxing Zhou, Ziyang Zhang, Haopeng Wang et al.

Bio-asphalt has a great application prospect in the replacement of petroleum-based asphalt to pave and maintain asphalt pavement. However, the problems of flow-induced crystallization and phase separation caused by flow-induced crystallization had severely restricted its application. This paper describes the progress of research on preparation, property evaluation and phase separation mechanism of bio-asphalt. The advantages and disadvantages of preparation methods of bio-asphalt are states. The fundamental physical and rheological properties of bio-asphalt are investigated, especially for flow-induced crystallization. There exists obvious flow-induced crystallization because bio-asphalt is rich in waxes that crystallize easily. Owing to the existence of excess biochar, bio-asphalt appears phase separation. A brief review of the effect of bio-oil and biochar on asphalt volatile organic compounds (VOCs) is presented. Research find that bio-oil/biochar are not only replenish the light components of asphalt, but also improve the flow-induced crystallization and phase separation of bio-asphalt. There exists synergistic effect of biochar and bio-oil in asphalt modification. Moreover, biochar can improve the durability of bio-oil modified asphalt, but excessive addition of biochar to bio-oil modified asphalt can cause phase separation. Adding an appropriate amount of bio-oil and biochar to asphalt can improve its high-temperature resistance, low-temperature crack resistance, and system compatibility.

Benjamin Decardi-Nelson, Abdulelah S. Alshehri, Akshay Ajagekar et al.

This article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE). These cutting-edge GenAI models, particularly foundation models (FMs), which are pre-trained on extensive, general-purpose datasets, offer versatile adaptability for a broad range of tasks, including responding to queries, image generation, and complex decision-making. Given the close relationship between advancements in PSE and developments in computing and systems technologies, exploring the synergy between GenAI and PSE is essential. We begin our discussion with a compact overview of both classic and emerging GenAI models, including FMs, and then dive into their applications within key PSE domains: synthesis and design, optimization and integration, and process monitoring and control. In each domain, we explore how GenAI models could potentially advance PSE methodologies, providing insights and prospects for each area. Furthermore, the article identifies and discusses potential challenges in fully leveraging GenAI within PSE, including multiscale modeling, data requirements, evaluation metrics and benchmarks, and trust and safety, thereby deepening the discourse on effective GenAI integration into systems analysis, design, optimization, operations, monitoring, and control. This paper provides a guide for future research focused on the applications of emerging GenAI in PSE.

Makgotso Jacqueline Maotwana

Poor roads are a major issue for cars, drivers, and pedestrians since they are a major cause of vehicle damage and can occasionally be quite dangerous for both groups of people (pedestrians and drivers), this makes road surface condition monitoring systems essential for traffic safety, reducing accident rates ad also protecting vehicles from getting damaged. The primary objective is to develop and evaluate machine learning models that can accurately classify road conditions into four categories: good, satisfactory, poor, and very poor, using a Kaggle dataset of road images. To address this, we implemented a variety of machine learning approaches. Firstly, a baseline model was created using a Multilayer Perceptron (MLP) implemented from scratch. Secondly, a more sophisticated Deep Neural Network (DNN) was constructed using Keras. Additionally, we developed a Logistic Regression model from scratch to compare performance. Finally, a wide model incorporating extensive feature engineering was built using the K-Nearest Neighbors (KNN) algorithm with sklearn.The study compared different models for image-based road quality assessment. Deep learning models, the DNN with Keras achieved the best accuracy, while the baseline MLP provided a solid foundation. The Logistic Regression although it is simpler, but it provided interpretability and insights into important features. The KNN model, with the help of feature engineering, achieved the best results. The research shows that machine learning can automate road condition monitoring, saving time and money on maintenance. The next step is to improve these models and test them in real cities, which will make our cities better managed and safer.

Carlos Lucio Raffo Suclupe, Leyner Oswaldo Calva Herrera

El presente estudio se desarrolló de forma experimental, aplicado al diseño de una mezcla asfáltica en caliente (MAC). Se buscó mitigar el impacto negativo en el medio ambiente generado por los aceites reciclados de motor (ARM) o comúnmente llamados aceites quemados que, por lo general, son eliminados a la intemperie. Es así que se adicionó ARM en porcentajes de acuerdo con PEN 60/70 de la mezcla patrón, planteándose el objetivo determinar las propiedades físico-mecánicas de la mezcla asfáltica modificada y evaluar si esta satisface los estándares que exigen las normas. Se evaluó mediante la metodología Marshall una población total de 135 briquetas que incluyen las mezclas asfálticas modificadas con 0,5 %, 1,5 %, 2,5 % y 3,5 % de aceite reciclado. Se determinó que el porcentaje óptimo de asfalto es de 5,75 %, ensayado a temperaturas de 120 °C y 130 °C. Se concluye que la incorporación de ARM en la mezcla asfáltica mejora sus propiedades físico-mecánicas (rigidez, flujo, estabilidad) y asegura el cumplimiento de los parámetros mínimos de una MAC.

Nurdicky Febrian Aditya, Suharyanto, Suripin

Sungai Sringin berada di wilayah Semarang Timur, Kecamatan Genuk merupakan daerah yang bertopografi rendah dan berbatasan langsung dengan laut Jawa. Perkembangan industri, perdagangan, pelabuhan, serta pertumbuhan penduduk yang cepat sebesar 2,84% per tahun (BPS Kota Semarang, 2021) menjadikan kawasan Semarang Timur khususnya Kecamatan Genuk sebagai pusat pertumbuhan utama. dan terminal jasa distribusi. Hal ini akan mempengaruhi daerah Sungai Sringin sebagai daerah tangkapan air. Pada 6 Februari 2021 telah terjadi limpasan air permukaan di beberapa wilayah Sub Das Sringin dikarenakan intensitas hujan yang tinggi serta berkurangnya daerah infiltrasi air hujan yang ada. Maka dari itu diperlukan metode LID untuk mengurangi limpasan air permukaan. Dari pemodelan yang di lakukan menggunakan Software Strom Water Management Model (SWMM) di dapat bahwa puncak limpasan air permukaan tertinggi berapa pada Subcatchment 6 dengan nilai 23.10 m3/s. Untuk mereduksi limpasan air perumakaan yang ada digunakan metode pembangunan berdampak rendah. Metode pembangunan berdampak rendah (LID) merupakah cara mengelola air hujan dalam skala mikro terutama pada kawasan tangkapan air hujan yang besar. Pada penelitian ini digunakan metode Rain Barrel, Bioretention Cell, dan Infiltration Trench yang merupakan contoh dari penerapan pembangunan berdampak rendah. Hasil dari pemodelan dengan menggunakan ketiga metode tersebut didapatkan pada nilai limpasan air permukaan mengalami penurunan

Yu Zhang, Lin Zhang

Fracture is one of the main failure modes of engineering structures such as buildings and roads. Effective detection of surface cracks is significant for damage evaluation and structure maintenance. In recent years, the emergence and development of deep learning techniques have shown great potential to facilitate surface crack detection. Currently, most reported tasks were performed by a convolutional neural network (CNN), while the limitation of CNN may be improved by the transformer architecture introduced recently. In this study, we investigated nine promising models to evaluate their performance in pavement surface crack detection by model accuracy, computational complexity, and model stability. We created 711 images of 224 by 224 pixels with crack labels, selected an optimal loss function, compared the evaluation metrics of the validation dataset and test dataset, analyzed the data details, and checked the segmentation outcomes of each model. We find that transformer-based models generally are easier to converge during the training process and have higher accuracy, but usually exhibit more memory consumption and low processing efficiency. Among nine models, SwinUNet outperforms the other two transformers and shows the highest accuracy among nine models. The results should shed light on surface crack detection by various deep-learning models and provide a guideline for future applications in this field.

Andy Zou, Long Phan, Sarah Chen et al.

In this paper, we identify and characterize the emerging area of representation engineering (RepE), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience. RepE places population-level representations, rather than neurons or circuits, at the center of analysis, equipping us with novel methods for monitoring and manipulating high-level cognitive phenomena in deep neural networks (DNNs). We provide baselines and an initial analysis of RepE techniques, showing that they offer simple yet effective solutions for improving our understanding and control of large language models. We showcase how these methods can provide traction on a wide range of safety-relevant problems, including honesty, harmlessness, power-seeking, and more, demonstrating the promise of top-down transparency research. We hope that this work catalyzes further exploration of RepE and fosters advancements in the transparency and safety of AI systems.

Byungkyu Moon, Hosin (David) Lee

Gopi Krishnan Rajbahadur, Shaowei Wang, Yasutaka Kamei et al.

Researchers usually discretize a continuous dependent variable into two target classes by introducing an artificial discretization threshold (e.g., median). However, such discretization may introduce noise (i.e., discretization noise) due to ambiguous class loyalty of data points that are close to the artificial threshold. Previous studies do not provide a clear directive on the impact of discretization noise on the classifiers and how to handle such noise. In this paper, we propose a framework to help researchers and practitioners systematically estimate the impact of discretization noise on classifiers in terms of its impact on various performance measures and the interpretation of classifiers. Through a case study of 7 software engineering datasets, we find that: 1) discretization noise affects the different performance measures of a classifier differently for different datasets; 2) Though the interpretation of the classifiers are impacted by the discretization noise on the whole, the top 3 most important features are not affected by the discretization noise. Therefore, we suggest that practitioners and researchers use our framework to understand the impact of discretization noise on the performance of their built classifiers and estimate the exact amount of discretization noise to be discarded from the dataset to avoid the negative impact of such noise.

Anne de Bortoli, Yacine Baouch, Mustapha Masdan

Transforming the construction sector is key to reaching net-zero, and many stakeholders expect its decarbonization through digitalization. But no quantified evidence has been brought to date. We propose the first environmental quantification of the impact of Building Information Modeling (BIM) in the construction sector. Specifically, the direct and indirect greenhouse gas (GHG) emissions generated by a monofunctional BIM to plan road maintenance, a Pavement Management System (PMS), are evaluated using field data from France. The related carbon footprints are calculated following a life cycle approach, using different sources of data, including ecoinvent v3.6, and the IPCC 2013 GWP 100a characterization factors. Three design-build-maintain pavement alternatives are compared: scenario 1 relates to a massive design and surface maintenance, scenario 2 to a progressive design and pre-planned structural maintenance, and scenario 3 to a progressive design and tailored structural maintenance supported by the PMS. First, results show negligible direct emissions due to the PMS existence: 0.02% of the life cycle emissions of scenario 3. Second, complementary sensitivity analyses show that using a PMS is climate-positive over the life cycle when pavement subgrade bearing capacity improves over time, and climate-neutral otherwise. The GHG emissions savings using BIM can reach up to 30% of the life cycle emissions compared to other scenarios, and 65% when restraining the scope to maintenance and rehabilitation and excluding original pavement construction. Third, the neutral effect of BIM in case of a deterioration of the bearing capacity of the subgrade may be explained by design practices and safety margins, that could be enhanced using BIM. Fourth, the decarbonization potential of a multifunctional BIM is discussed, and research perspectives are presented.

T.F. Fwa

Adequate pavement skid resistance is a key requirement for safe road operations. Unfortunately, the measurement and prediction of the skid resistance property of an in-service road pavement, or pavement mixture specimens in the laboratory, is a highly challenging process from both theoretical and practical points of view. For more than 60 years, owing to the lack of theoretical solutions to the complex tire-fluid-pavement interaction problem, the practice of pavement skid resistance determination and prediction has essentially been derived from experimental and field observed data. The rapid development of efficient numerical computational techniques and high-power computing facilities in the last two decades made it possible for researchers to numerically solve the tire-fluid-pavement interaction problem. It enables the numerical evaluation and prediction of high-speed wet skid resistance, and the determination of the tire-pavement kinetic friction coefficient in the evaluation of low-speed skid resistance. This paper presents a state-of-the-art review of the research development of theoretical mechanistic approaches in the determination and prediction of pavement skid resistance. It covers the following main aspects of the subject matter: (i) mechanisms of skid resistance generation in dry, wetted (i.e., damp), wet and flooded pavements; (ii) theoretical evaluation of pavement skid resistance in dry, wetted, wet and flooded states; (iii) theoretical approaches in pavement skid resistance prediction; and (iv) concepts of representing the skid resistance state of pavement. The capability of finite element simulation approach for wet skid resistance evaluation with good accuracy is explained. Also highlighted is the practical significance of the Concept of Skid Resistance State. Areas of practical applications of the concept, coupled with the simulation model, are introduced. They include applications in driving safety analysis, road safety design and control, design of paving mixtures, safety maintenance and management of pavements, and harmonization of skid resistance measurements and predictions.

Ifan Nasution, Dyah Ari Wulandari

Sedimentasi merupakan masalah yang paling umum terjadi pada waduk. Sedimentasi juga merupakan tantangan terbesar dalam operasi waduk. Pemanfaatan kapasitas tampungan waduk merupakan faktor utama terpenuhinya manfaat sebuah bendungan. Penelitian ini bertujuan untuk mengetahui dinamika sedimentasi yang terjadi pada waduk Kedungombo selama beroperasi 28 tahun. Dinamika sedimentasi meliputi analisa perubahan kapasitas tampungan, laju sedimentasi, distribusi sedimentasi, pola sedimentasi dan umur teknis layanan waduk. Metode penelitian dilaksanakan berdasarkan peninjauan lapangan, studi literatur kajian-kajian terdahulu dan pengumpulan data sekunder dari pengelola bendungan. Berdasarkan penelitian yang dilakukan diketahui bahwa sisa tampungan total saat ini sebesar 94,47%, berdasarkan analisa distribusi sedimentasi pada 4 kali periode pengukuran (1994, 2003, 2012 dan 2017) didapatkan rata-rata sedimen yang mengendap ditampungan mati adalah 42,95%, tampungan efektif 51,08% dan tampungan banjir 5,97%, pola sedimentasi Waduk  adalah pola uniform, usia teknis waduk berakhir pada tahun 2064 atau 43 tahun lagi sejak tahun 2021.

Vita Ariesta Fitriana, Suripin, Ignatius Sriyana

Proyek Pembangunan Bendungan Karian merupakan salah satu proyek strategis nasional yang dilaksanakan sejak tahun 2015, memiliki tiga fungsi utama yaitu penyediaan air Rumah Tangga, Kota dan Industri (RKI) untuk Provinsi Banten dan DKI Jakarta, suplesi Daerah Irigasi Ciujung dan pengendalian banjir. Daerah Aliran Sungai Ciberang Kabupaten Lebak sebagai lokasi pembangunan Bendungan Karian mengalami bencana banjir bandang pada Januari 2020, akibat dari pengaruh cuaca ekstrim sebagai salah satu penyebabnya (Yahya, 2020). Hal ini mengakibatkan terputusnya jembatan konstruksi pada outlet terowongan pengelak. Pelaksanaan pembangunan Bendungan Karian direncanakan selesai pada tahun 2019 namun masih berlangsung hingga kini, mengacu kepada kajian ulang dokumen desain pada tahun 2015. Penelitian ini bertujuan untuk mengkaji ulang desain hidrologis bangunan pengelak/cofferdam hulu Bendungan Karian berdasar data hujan terbaru pada DAS Ciberang. Pemodelan dilakukan dengan menggunakan perangkat lunak HEC-HMS yang memiliki kemampuan untuk melakukan penelusuran banjir pada suatu Daerah Aliran Sungai (DAS). Hasil analisis terjadi peningkatan curah hujan rencana pada DAS Ciberang untuk kala ulang 25 tahun dari 180 mm/hari menjadi 210 mm/hari, dan debit banjir rencana dari 664 m3/detik menjadi 793.2 m3/detik, berturut-turut berdasar data periode 1982-2015 dan periode 1982-2019. Semua perubahan data tersebut masih sesuai dengan banjir desain yang digunakan pada desain cofferdam hulu Bendungan Karian Tahun 2015 Sehingga secara aspek hidrologis cofferdam hulu sebagai bagian pengaman pekerjaan konstruksi timbunan bendungan utama pada Bendungan Karian masih memenuhi kriteria desain awal atau aman walau telah terjadi perbedaan 5 tahun data hujan pada DAS Ciberang.

Xiaoning Zhang

To improve the quality of the Hong Kong–Zhuhai–Macao Bridge paving project, a new paving layer material, Guss-mastic asphalt (GMA), was proposed in this paper by combining the advantages of two types of cast asphalt mixtures: mastic asphalt (MA) and Guss asphalt (GA). Based on the characteristics of GMA, to simulate its actual production process, this study developed a small-simulated cooker mixing equipment. Moreover, the flow degree, 60 ​°C dynamic stability, and impact toughness were proposed to be used to evaluate the construction and ease, high temperature stability, and fatigue resistance of GMA cast asphalt mixtures, respectively. Moreover, the quality control standards for GMA paving materials by indoor tests, field trial mix GMA material performance tests, and accelerated loading tests were finalized. The study showed that the developed simulated cooker yielded consistent mixing results in the same working environment as the engineering cooker device. Increasing the coarse aggregate incorporation rate, coarsening the mastic epure (ME) gradation composition, and using a smaller oil to stone ratio can reduce the flowability of the GMA materials to varying degrees. The four-point bending fatigue life and impact toughness of the different GMA materials are correlated well. A mobility of <20 ​s, 60 ​°C dynamic stability of 400–800 ​times/mm, 15 ​°C impact toughness of ≥400 ​N·mm, and cooker car mixing temperature control standard of 210 ​°C–230 ​°C form an appropriate control index system for the design and production of GMA cast asphalt mixtures. Simultaneously, accelerated loading tests verified the accuracy and reliability of the quality control index system that has been used in the GMA paving project of the Hong Kong–Zhuhai–Macao Bridge deck and has achieved good application results.

Hee Su Kim, Hoki Ban, Won-Jun Park

Existing deicing technologies involving chloride and heating wires have limitations such as reduced durability of roads and surrounding structures, and high labor requirements and maintenance costs. Hence, in this study, we performed indoor experiments, numerical analyses, and field tests to examine the efficiency of deicing using carbon nanotubes (CNTs) to overcome these limitations. For indoor experiments, a CNT was inserted into the center of a concrete sample and then heated to 60 °C while maintaining the ambient and internal temperatures of the sample at −10 °C using a refrigeration chamber. Numerical analysis considering thermal conductivity was performed based on the indoor experimental results. Using the calculation results, field tests were conducted, and the thermal conduction performance of the heating element was examined. Results showed that the surface temperature between the heating elements exceeded 0 °C. Moreover, we found that the effective heating distance of the heating elements should be 20–30 cm for effective thermal overlap through the indoor experiments. Additionally, the numerical analysis results indicated that the effective heating distance increased to 100 cm when the heating element temperature and experiment time were increased. Field test results showed that 62 cm-deep snow melted between the heating elements (100 cm), thus, verifying the possibility of deicing.

Bednarek, Włodzimierz

In the paper is shown an analysis of a CWR track’s longitudinal displacements due to a local temperature difference on its length. The thermical forces on the railway track length arise due to a local temperature difference of rail, causing the local, zonal the longitudinal displacements of rail cross-sections. Axial displacements of track induce in succession a longitudinal reaction of roadbed in such a degree on which a arising displacements allow. Additionally a arising during track operating a variable longitudinal resistance on track’s length (generated among other things by different state of ballast compaction, different pressure force of rail foot to divider), periodical acting force from vehicles, different value of adhesion wheels with rails and also different stage of rail heating, cause a disturbance section of equilibrium state of CWR track. In certain cases it assumes a shape of rails micro displacements, which can take a form e.g. creep displacements leading to value changes of longitudinal forces on this segment length with arising displacements. In paper analytical form of considered problem is given and computational examples, diagrams and tables reflecting influence of analyzed parameters on obtained a CWR track’s longitudinal displacements due to local temperature difference on its length is inserted Keywords: CWR track; Longitudinal displacement; Local temperature difference

Stephen L. H. Lau, Edwin K. P. Chong, Xu Yang et al.

Automated pavement crack image segmentation is challenging because of inherent irregular patterns, lighting conditions, and noise in images. Conventional approaches require a substantial amount of feature engineering to differentiate crack regions from non-affected regions. In this paper, we propose a deep learning technique based on a convolutional neural network to perform segmentation tasks on pavement crack images. Our approach requires minimal feature engineering compared to other machine learning techniques. We propose a U-Net-based network architecture in which we replace the encoder with a pretrained ResNet-34 neural network. We use a "one-cycle" training schedule based on cyclical learning rates to speed up the convergence. Our method achieves an F1 score of 96% on the CFD dataset and 73% on the Crack500 dataset, outperforming other algorithms tested on these datasets. We perform ablation studies on various techniques that helped us get marginal performance boosts, i.e., the addition of spatial and channel squeeze and excitation (SCSE) modules, training with gradually increasing image sizes, and training various neural network layers with different learning rates.