Infrastructures

12 pages, 2250 KiB

Open AccessArticle

Capacity Assessment in Freight-Passengers Complex Railway Nodes: Trieste Case Study

by Atieh Kianinejadoshah and Stefano Ricci

Infrastructures 2022, 7(8), 106; https://doi.org/10.3390/infrastructures7080106 - 22 Aug 2022

Cited by 3 | Viewed by 2713

An integrated approach to node and station operation analysis is possible by means of analytical methods, customized to this scope. Alternatively, the simulation models allow more in-depth analyses aiming at the optimization of the use of capacity. The general goals of the research [...] Read more.

An integrated approach to node and station operation analysis is possible by means of analytical methods, customized to this scope. Alternatively, the simulation models allow more in-depth analyses aiming at the optimization of the use of capacity. The general goals of the research are the comparison of methods for the assessment of railway lines and nodes’ capacity, suitability for specific tasks, and stability of the results under variable scenarios. The comparison is finalised to quantify the relative level of confidence of the concerned literature methods. The work is part of a larger research project with the final goal of identifying the most appropriate approach for the optimization of the network capacity and the setup of specific guidelines. In this framework and perspective, the paper introduces synthetically the methods and applies them systematically to a real complex mixed-traffic network in Trieste, situated in Northeast Italy, including the main passengers and freight stations and a set of lines used for both services. Full article

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16 pages, 1179 KiB

Open AccessArticle

Evaluating the Effect of Dynamic Message Signs and Lane Control Signs on Driver Behavior in a Developing Country

by Khaled Shaaban and Mohammed Alsoub

Infrastructures 2022, 7(8), 105; https://doi.org/10.3390/infrastructures7080105 - 16 Aug 2022

Cited by 4 | Viewed by 2290

Abstract

Developing countries are continuously upgrading their transportation systems. The latest improvement in Qatar, a fast-developing country in the Middle East, was the installation of dynamic message signs (DMS) and lane control signs (LCS). These signs were installed in multiple areas in the city [...] Read more.

Developing countries are continuously upgrading their transportation systems. The latest improvement in Qatar, a fast-developing country in the Middle East, was the installation of dynamic message signs (DMS) and lane control signs (LCS). These signs were installed in multiple areas in the city of Doha, the capital of Qatar. However, there have been no studies in Qatar or the region regarding the effectiveness of such signs on driver behavior. This study aims to evaluate and compare the impact of DMS and LCS on driving behavior on different types of roads. A real-life driving experiment was conducted along a defined route in Doha that consists of three sections: arterial road, freeway with electronic signs, and freeway without electronic signs. The details of the trips were recorded using multiple methods. The results showed that the introduction of DMS and LCS did not significantly affect speed compliance. The results also indicated that LCS and DMS did not have a major effect on other driver behavior variables such as harsh braking and lane changing. The study provided several recommendations to road authorities concerning the deployment of electronic signs and highlighted a few topics for future research work. Full article

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18 pages, 8633 KiB

Open AccessArticle

Damage Sensitive Signals for the Assessment of the Conditions of Wind Turbine Rotor Blades Using Electromagnetic Waves

by Zainab Riyadh Shaker Al-Yasiri, Hayder Majid Mutashar, Klaus Gürlebeck and Tom Lahmer

Infrastructures 2022, 7(8), 104; https://doi.org/10.3390/infrastructures7080104 - 12 Aug 2022

Cited by 2 | Viewed by 1985

Abstract

One of the most important renewable energy technologies used nowadays are wind power turbines. In this paper, we are interested in identifying the operating status of wind turbines, especially rotor blades, by means of multiphysical models. It is a state-of-the-art technology to test [...] Read more.

One of the most important renewable energy technologies used nowadays are wind power turbines. In this paper, we are interested in identifying the operating status of wind turbines, especially rotor blades, by means of multiphysical models. It is a state-of-the-art technology to test mechanical structures with ultrasonic-based methods. However, due to the density and the required high resolution, the testing is performed with high-frequency waves, which cannot penetrate the structure in depth. Therefore, there is a need to adopt techniques in the fields of multiphysical model-based inversion schemes or data-driven structural health monitoring. Before investing effort in the development of such approaches, further insights and approaches are necessary to make the techniques applicable to structures such as wind power plants (blades). Among the expected developments, further accelerations of the so-called “forward codes” for a more efficient implementation of the wave equation could be envisaged. Here, we employ electromagnetic waves for the early detection of cracks. Because in many practical situations, it is not possible to apply techniques from tomography (characterized by multiple sources and sensor pairs), we focus here on the question of whether the existence of cracks can be determined by using only one source for the sent waves. Full article

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23 pages, 5437 KiB

Open AccessArticle

GIS-Based Spatial Analysis of Accident Hotspots: A Nigerian Case Study

by Abayomi Afolayan, Said M. Easa, Oladapo S. Abiola, Funmilayo M. Alayaki and Olusegun Folorunso

Infrastructures 2022, 7(8), 103; https://doi.org/10.3390/infrastructures7080103 - 9 Aug 2022

Cited by 11 | Viewed by 5825

Abstract

This study identified high-risk locations (hotspots) using geographic information systems (GIS) and spatial analysis. Five years of accident data (2013–2017) for the Lokoja-Abuja-Kaduna highway in Nigeria were used. The accident concentration analysis was conducted using the mean center analysis and Kernel density estimation [...] Read more.

This study identified high-risk locations (hotspots) using geographic information systems (GIS) and spatial analysis. Five years of accident data (2013–2017) for the Lokoja-Abuja-Kaduna highway in Nigeria were used. The accident concentration analysis was conducted using the mean center analysis and Kernel density estimation method. These locations were further verified using Moran’s I statistics (spatial autocorrelation) to determine their clustering with statistical significance. Fishnet polygon and network spatial weight matrix approaches of the Getis–Ord Gi* statistic were used in the hotspot analysis. Hotspots exist for 2013, 2014, and 2017 with a significance level between 95–99%. However, hotspots for 2015 and 2016 have a low significance level and the pattern is random. The spatial autocorrelation analysis of the overall accident locations and the Moran’s I statistic showed that the distribution of the accidents on the study route is random. Thus, preventive measures for hotspot locations should be based on a yearly hotspot analysis. The average daily traffic values of 31,270 and 16,303 were obtained for the northbound and southbound directions of the Abaji–Abuja section. The results show that hotspot locations with high confidence levels are at points where there are geometric features. Full article

(This article belongs to the Section Infrastructures Inspection and Maintenance)

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18 pages, 5772 KiB

Open AccessArticle

Seismic Vulnerability Assessment of Critical Port Infrastructure Components by Modelling the Soil-Wharf-Crane Interaction

by Ali Güney Özcebe, Francesca Bozzoni and Barbara Borzi

Infrastructures 2022, 7(8), 102; https://doi.org/10.3390/infrastructures7080102 - 4 Aug 2022

Cited by 3 | Viewed by 2273

Abstract

This paper aims to investigate the seismic vulnerability of key port infrastructure components by using the outcomes of advanced numerical analysis. For the first time, to the best knowledge of the authors, a pile-supported wharf structure, the soil deposits where the wharf lies, [...] Read more.

This paper aims to investigate the seismic vulnerability of key port infrastructure components by using the outcomes of advanced numerical analysis. For the first time, to the best knowledge of the authors, a pile-supported wharf structure, the soil deposits where the wharf lies, and a crane typically operating on the wharf are numerically modelled as a combined system. The starting point for building the numerical model is the main components of strategic facilities at the port of Gioia Tauro (Italy), which is a strategic hub for container traffic located in one of the most seismically active regions of the Mediterranean Sea. Based on the results obtained from two-dimensional (2D) dynamic analyses, fragility curves were developed for single port components and the wharf-crane-soil system. A scenario-based seismic damage assessment was then exemplified to compare the predictions resulting from the fragility model presented in this work with the relevant data available in the literature. It turns out that, besides some inevitable variations, expected damage percentages were in general agreement. As the main contribution of this work, derived fragility curves might be adopted as an effective tool for rapid evaluation of the seismic performance of port components during the development of strategies for risk mitigation and also the emergency management in case of an earthquake. Full article

(This article belongs to the Special Issue Seismic Performance Improvement and Energy Optimization for Structures)

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17 pages, 2609 KiB

Open AccessArticle

Developing Bridge Deterioration Models Using an Artificial Neural Network

by Essam Althaqafi and Eddie Chou

Infrastructures 2022, 7(8), 101; https://doi.org/10.3390/infrastructures7080101 - 31 Jul 2022

Cited by 17 | Viewed by 3243

Abstract

The condition of a bridge is critical in quality evaluations and justifying the significant costs incurred by maintaining and repairing bridge infrastructures. Using bridge management systems, the department of transportation in the United States is currently supervising the construction and renovations of thousands [...] Read more.

The condition of a bridge is critical in quality evaluations and justifying the significant costs incurred by maintaining and repairing bridge infrastructures. Using bridge management systems, the department of transportation in the United States is currently supervising the construction and renovations of thousands of bridges. The inability to obtain funding for the current infrastructures, such that they comply with the requirements identified as part of maintenance, repair, and rehabilitation (MR&R), makes such bridge management systems critical. Bridge management systems facilitate decision making about handling bridge deterioration using an efficient model that accurately predicts bridge condition ratings. The accuracy of this model can facilitate MR&R planning and is used to confirm funds allocated to repair and maintain the bridge network management system. In this study, an artificial neural network (ANN) model is developed to improve the bridge management system (BMS) by improving the prediction accuracy of the deterioration of bridge decks, superstructures, and substructures. A large dataset of historical bridge condition assessment data was used to train and test the proposed ANN models for the deck, superstructure, and substructure components, and the accuracy of these models was 90%, 90%, and 89% on the testing set, respectively. Full article

(This article belongs to the Special Issue Resilient Bridge Infrastructures)

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19 pages, 2477 KiB

Open AccessArticle

Traffic and Climate Impacts on Rutting and Thermal Cracking in Flexible and Composite Pavements

by Alexa Raffaniello, Matthew Bauer, Md. Safiuddin and Mohab El-Hakim

Infrastructures 2022, 7(8), 100; https://doi.org/10.3390/infrastructures7080100 - 29 Jul 2022

Cited by 10 | Viewed by 4785

Abstract

The study presented in this paper analyzed four long-term pavement performance (LTPP) test sections located in the states of New York (NY) and California (CA). Two of them are flexible pavement sections, whereas the other two are composite pavement sections. Two levels of [...] Read more.

The study presented in this paper analyzed four long-term pavement performance (LTPP) test sections located in the states of New York (NY) and California (CA). Two of them are flexible pavement sections, whereas the other two are composite pavement sections. Two levels of analysis—in-state analysis and cross-state analysis—were performed for these pavement sections to determine the impacts of traffic and climate conditions. The performance of the pavement sections was evaluated in respect of thermal cracking and rutting resistance. The in-state analysis focused on comparing the pavement sections located in the same state. The two pavement sections located in CA exhibited insignificant variation in thermal cracking, although one of them had an additional 1.5” (38 mm) dense-graded asphaltic concrete (AC) layer. On the other hand, the additional 1.5” (38 mm) AC layer resulted in a significant reduction in the rutting depth in one pavement section. The in-state analysis of the two pavement sections located in NY revealed that the 0.8” (20.4 mm) chip seal layer had significantly low resistance to thermal cracking and rutting. The cross-state analysis examined pavement sections of comparable structural capacities—two with low structural capacity, and two with high structural capacity. The performance comparison of the two pavement sections with low structural capacity revealed that the chip seal layer exhibited a significantly high rutting depth, i.e., low rutting resistance under high traffic loads in a freezing climate. On the contrary, the two pavement sections with high structural capacity showed relatively high rutting resistance in both warmer and freezing climates. Furthermore, this paper presents the pavement deterioration models for rutting and thermal cracking in the LTPP test sections. These models were developed using multiple linear regression considering the pavement service life (age), traffic load (average annual daily truck traffic, AADTT), and climate impact (freezing index, FI). The deterioration models had coefficients of determination (r²) in the range of 0.82–0.99 and standard errors varying from 0.01 to 9.92, which indicate that the models are reliable. Full article

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14 pages, 2651 KiB

Open AccessArticle

Thermally Treated Waste Silt as Geopolymer Grouting Material and Filler for Semiflexible Pavements

by Abbas Solouki, Piergiorgio Tataranni and Cesare Sangiorgi

Infrastructures 2022, 7(8), 99; https://doi.org/10.3390/infrastructures7080099 - 23 Jul 2022

Cited by 6 | Viewed by 2474

Abstract

Considering the future shortage of natural aggregates, various researchers have promoted the recycling of by-products into various asphalt pavement types. This paper promoted a double-recycling technique, where thermally treated waste silt was used as a filler for the bituminous skeleton and grouting material [...] Read more.

Considering the future shortage of natural aggregates, various researchers have promoted the recycling of by-products into various asphalt pavement types. This paper promoted a double-recycling technique, where thermally treated waste silt was used as a filler for the bituminous skeleton and grouting material of a geopolymer-based semiflexible pavement. Semiflexible pavements (SFP) inherit the flexibility of common asphalt pavements and simultaneously benefit from the rigidity of cement concrete pavements. For this purpose, waste silt obtained from a local asphalt plant was thermally treated at 750 °C and was used as the filler to produce the porous skeleton. Two different materials, including conventional cement-based and a geopolymer-based cement, were used as the grouting material. The geopolymer grout was produced by mixing metakaolin (MK), potassium-based liquid hardener and calcined silt as filler. The porous and grouted samples were characterized in terms of indirect tensile strength (ITS), the indirect tensile strength modulus (ITSM) and moisture sensitivity. The use of thermally treated waste silt as filler in porous asphalt demonstrated promising results and was comparable to the control samples produced with limestone as the filler. However, the control samples grouted with cement-based material outperformed the geopolymer grout in all aspects. Moreover, the addition of calcined silt improved the low-temperature fatigue performance of porous and grouted asphalt pavements. Full article

(This article belongs to the Special Issue Reusing, Recycling and Repurposing Infrastructure: Components and Construction Materials)

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22 pages, 22705 KiB

Open AccessArticle

Performance Evaluation of Blind Modal Identification in Large-Scale Civil Infrastructure

by Ali Abasi and Ayan Sadhu

Infrastructures 2022, 7(8), 98; https://doi.org/10.3390/infrastructures7080098 - 22 Jul 2022

Cited by 4 | Viewed by 2615

Abstract

The monitoring and maintenance of existing civil infrastructure has recently received worldwide attention. Several structural health monitoring methods have been developed, including time-, frequency-, and time–frequency domain methods of modal identification and damage detection to estimate the structural and modal parameters of large-scale [...] Read more.

The monitoring and maintenance of existing civil infrastructure has recently received worldwide attention. Several structural health monitoring methods have been developed, including time-, frequency-, and time–frequency domain methods of modal identification and damage detection to estimate the structural and modal parameters of large-scale structures. However, there are several implementation challenges of these modal identification methods, depending on the size of the structures, measurement noise, number of available sensors, and their operational loads. In this paper, two modal identification methods, Second-Order Blind Identification (SOBI) and Time-Varying Filtering Empirical Mode Decomposition (TVF-EMD), are evaluated and compared for large-scale structures including a footbridge and a wind turbine blade with a wide range of dynamic characteristics. The results show that TVF-EMD results in better accuracy in modal identification compared to SOBI for both structures. However, when the number of sensors is equal to or more than the number of target modes of the structure, SOBI results in better computational efficiencies compared to TVF-EMD. Full article

(This article belongs to the Special Issue Structural Health Monitoring of Civil Infrastructures)

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Infrastructures, Volume 7, Issue 8 (August 2022) – 9 articles

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