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Infrastructures, Volume 9, Issue 6 (June 2024) – 9 articles

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13 pages, 3236 KiB  
Article
Improved Blob-Based Feature Detection and Refined Matching Algorithms for Seismic Structural Health Monitoring of Bridges Using a Vision-Based Sensor System
by Luna Ngeljaratan, Mohamed A. Moustafa, Agung Sumarno, Agus Mudo Prasetyo, Dany Perwita Sari and Maidina Maidina
Infrastructures 2024, 9(6), 97; https://doi.org/10.3390/infrastructures9060097 - 14 Jun 2024
Viewed by 232
Abstract
The condition and hazard monitoring of bridges play important roles in ensuring their service continuity not only throughout their entire lifespan but also under extreme conditions such as those of earthquakes. Advanced structural health monitoring (SHM) systems using vision-based technology, such as surveillance, [...] Read more.
The condition and hazard monitoring of bridges play important roles in ensuring their service continuity not only throughout their entire lifespan but also under extreme conditions such as those of earthquakes. Advanced structural health monitoring (SHM) systems using vision-based technology, such as surveillance, traffic, or drone cameras, may assist in preventing future impacts due to structural deficiency and are critical to the emergence of sustainable and smart transportation infrastructure. This study evaluates several feature detection and tracking algorithms and implements them in the vision-based SHM of bridges along with their systematic procedures. The proposed procedures are implemented via a two-span accelerated bridge construction (ABC) system undergoing a large-scale shake-table test. The research objectives are to explore the effect of refined matching algorithms on blob-based features in improving their accuracies and to implement the proposed algorithms on large-scale bridges tested under seismic loads using vision-based SHM. The procedure begins by adopting blob-based feature detectors, i.e., the scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and KAZE algorithms, and their stability is compared. The least medium square (LMEDS), least trimmed square (LTS), random sample consensus (RANSAC), and its generalization maximum sample consensus (MSAC) algorithms are applied for model fitting, and their sensitivity for removing outliers is analyzed. The raw data are corrected using mathematical models and scaled to generate displacement data. Finally, seismic vibrations of the bridge are generated, and the seismic responses are compared. The data are validated using target-tracking methods and mechanical sensors, i.e., string potentiometers. The results show a good agreement between the proposed blob feature detection and matching algorithms and target-tracking data and reference data obtained using mechanical sensors. Full article
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29 pages, 19840 KiB  
Article
Mass and Stiffness Correlation Using a Transformation Matrix
by Natalia García Fernández, Pelayo Fernández Fernandez, Rune Brincker and Manuel Aenlle López
Infrastructures 2024, 9(6), 96; https://doi.org/10.3390/infrastructures9060096 - 13 Jun 2024
Viewed by 196
Abstract
Model correlation techniques are methods used to compare two different models, usually a numerical model and an experimental model. According to the structural dynamic modification theory, the experimental mode shapes estimated by modal analysis can be expressed as a linear combination of the [...] Read more.
Model correlation techniques are methods used to compare two different models, usually a numerical model and an experimental model. According to the structural dynamic modification theory, the experimental mode shapes estimated by modal analysis can be expressed as a linear combination of the numerical mode shapes through a transformation matrix T. In this paper, matrix T is proposed as a novel model correlation technique to detect discrepancies between the numerical and the experimental models in terms of mass. The discrepancies in stiffness can be identified by combining the numerical natural frequencies and the matrix T. This methodology can be applied to correlate the numerical and experimental results of civil (bridges, dams, towers, buildings, etc.), aerospace and mechanical structures and to detect damage when using structural health monitoring techniques. The technique was validated by numerical simulations on a lab-scaled two-span bridge considering different degradation scenarios and experimentally on a lab-scaled structure, which was correlated with two numerical models. Full article
(This article belongs to the Special Issue Sustainable Practices in Bridge Construction)
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22 pages, 7922 KiB  
Article
Flexible Permeable-Pavement System Sustainability: A Methodology for Stormwater Management Based on PM Granulometry
by Vittorio Ranieri, Stefano Coropulis, Veronica Fedele, Paolo Intini and John Joseph Sansalone
Infrastructures 2024, 9(6), 95; https://doi.org/10.3390/infrastructures9060095 - 11 Jun 2024
Viewed by 251
Abstract
Permeable-pavement design methodologies can improve the hydrologic and therefore the environmental benefits of rural and urban roadway systems. By contrast, conventional impervious pavements perturb the hydrologic cycle, altering the relationship between the rainfall loading and runoff response. Impervious pavements create a hydraulically conductive [...] Read more.
Permeable-pavement design methodologies can improve the hydrologic and therefore the environmental benefits of rural and urban roadway systems. By contrast, conventional impervious pavements perturb the hydrologic cycle, altering the relationship between the rainfall loading and runoff response. Impervious pavements create a hydraulically conductive interface for the transport of traffic-generated chemicals and particulate matter (PM), deleteriously impacting their proximate environments. Permeable-pavement systems are countermeasures to mitigate hydrologic, chemical, and PM impacts. However, permeable pavements are not always equally implementable due to costs, PM loadings, and design constraints. A potential solution to facilitate environmental benefits while meeting the traffic load capacity is the combination of two filtration systems placed at the pavement shoulders and/or pedestrian sidewalks: a bituminous-pavement open-graded friction course (BPFC) and an aggregate-filled infiltration trench. This solution is presented in this manuscript together with the methodological framework and the first results of the investigations into designing and validating such a combined system. The research was conducted at the laboratories of the Polytechnic University of Bari and the University of Florida, while an operational and full-scale physical model was constructed in Bari, Italy. The first results presented characterize the PM deposition on public roads based on granulometry (particle size distributions (PSDs) and particle number densities (PNDs)). Samples (n = 16) were collected and analyzed at eight different sites with different land uses, traffic, and pavements from different cities (Bari and Taranto, Italy). The PM analysis showed similar distributions (PSDs and PNDs), except for two samples. The gravimetric-based PSDs of the PM had granulometric distributions in the sand-size range. In contrast, the PNDs, modeled by a Power Law Model (PLM) (R2 ≥ 0.92), illustrated an exponentially increasing number of particles in the fine silt and clay-size range, representing less than 10% of the PSD mass. Moreover, the results indicate that PM sourced from permeable-pavement systems has differing impacts on the pavement service life. Full article
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20 pages, 2257 KiB  
Review
An Overview of Methods to Enhance the Environmental Performance of Cement-Based Materials
by Daniel Suarez-Riera, Luciana Restuccia, Devid Falliano, Giuseppe Andrea Ferro, Jean-Marc Tuliani, Matteo Pavese and Luca Lavagna
Infrastructures 2024, 9(6), 94; https://doi.org/10.3390/infrastructures9060094 - 11 Jun 2024
Viewed by 501
Abstract
Urbanization and demographic growth have led to increased global energy consumption in recent years. Furthermore, construction products and materials industries have contributed significantly to this increase in fossil fuel use, due to their significant energy requirements, and consequent environmental impact, during the extraction [...] Read more.
Urbanization and demographic growth have led to increased global energy consumption in recent years. Furthermore, construction products and materials industries have contributed significantly to this increase in fossil fuel use, due to their significant energy requirements, and consequent environmental impact, during the extraction and processing of raw materials. To address this environmental problem, architectural design and civil engineering are trying to implement strategies that enable the use of high-performance materials while minimizing the usage of energy-intensive or toxic and dangerous building materials. These efforts also aim to make buildings less energy-consuming during their useful life. Using waste materials, such as Construction and Demolition Waste (CdW), is one of the most promising approaches to address this issue. In recent years, the European Union (EU) has supported recovery strategies focused on using CdW, as they account for more than 30% of the total waste production in the EU. In this regard, reuse techniques—such as incorporating concrete fragments and bricks as road floor fillers—have been the subject of targeted scientific research. This review will outline various strategies for producing green cement and concrete, particularly emphasizing the reuse of Construction and Demolition Waste (CdW). Full article
(This article belongs to the Special Issue Innovative Solutions for Concrete Applications)
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22 pages, 3222 KiB  
Article
Bond Stress Behavior of a Steel Reinforcing Bar Embedded in Geopolymer Concrete Incorporating Natural and Recycled Aggregates
by Qasim Shaukat Khan, Haroon Akbar, Asad Ullah Qazi, Syed Minhaj Saleem Kazmi and Muhammad Junaid Munir
Infrastructures 2024, 9(6), 93; https://doi.org/10.3390/infrastructures9060093 - 31 May 2024
Viewed by 189
Abstract
Abstract: The rise in greenhouse gases, particularly carbon dioxide (CO2) emissions, in the atmosphere is one of the major causes of global warming and climate change. The production of ordinary Portland cement (OPC) emits harmful CO2 gases, which contribute to [...] Read more.
Abstract: The rise in greenhouse gases, particularly carbon dioxide (CO2) emissions, in the atmosphere is one of the major causes of global warming and climate change. The production of ordinary Portland cement (OPC) emits harmful CO2 gases, which contribute to sporadic heatwaves, rapid melting of glaciers, flash flooding, and food shortages. To address global warming and climate change challenges, this research study explores the use of a cement-less recycled aggregate concrete, a sustainable approach for future constructions. This study uses fly ash, an industrial waste of coal power plants, as a 100% substitute for OPC. Moreover, this research study also uses recycled coarse aggregates (RCAs) as a partial to complete replacement for natural coarse aggregates (NCAs) to preserve natural resources for future generations. In this research investigation, a total of 60 pull-out specimens were prepared to investigate the influence of steel bar diameter (9.5 mm, 12.7 mm, and 19.1 mm), bar embedment length, (4 and 6), and percentage replacements of NCA with RCA (25%, 50%, 75%, and 100%) on the bond stress behavior of cement-less RA concrete. The test results exhibited that the bond stress of cement-less RCA concrete decreased by 6% with increasing steel bar diameter. Moreover, the bond stress decreased by 5.5% with increasing bar embedment length. Furthermore, the bond stress decreased by 7.6%, 7%, 8.8%, and 20.4%, respectively, with increasing percentage replacements (25%, 50%, 75%, and 100%) of NCA with RCA. An empirical model was developed correlating the bond strength to the mean compressive strength of cement-less RCA concrete, which matched well with the experimental test results and predictions of the CEB-FIP model for OPC. The CRAC mixes exhibited higher costs but significantly lower embodied CO2 emissions than OPC concrete. Full article
20 pages, 1524 KiB  
Article
Using Multiple Machine Learning Models to Predict the Strength of UHPC Mixes with Various FA Percentages
by Hussam Safieh, Rami A. Hawileh, Maha Assad, Rawan Hajjar, Sayan Kumar Shaw and Jamal Abdalla
Infrastructures 2024, 9(6), 92; https://doi.org/10.3390/infrastructures9060092 - 28 May 2024
Viewed by 441
Abstract
Ultra High-Performance Concrete (UHPC) has shown extraordinary performance in terms of strength and durability. However, having a cost-effective and sustainable UHPC mix design is a challenge in the construction sector. This study aims on building a predictable model that can help in determining [...] Read more.
Ultra High-Performance Concrete (UHPC) has shown extraordinary performance in terms of strength and durability. However, having a cost-effective and sustainable UHPC mix design is a challenge in the construction sector. This study aims on building a predictable model that can help in determining the compressive strength of UHPC. The research focuses on applying multiple machine learning (ML) models and evaluating their performance in predicting the strength prediction of UHPC. Two reliable metrics are used to evaluate the performance of the model which are the coefficient of determination (R2) and mean squared error (MSE). The parameters that are affecting the compressive strength of UHPC are fly ash percentage levels (FA%), superplasticizer content, water to binder ratio (w/b), and curing period. A total of 54 ML models were used, consisting of Linear Regression, Support Vector Machines (SVM), Neural Networks, and Random forests algorithms. Among these models, Random Forest proved to be the most effective in capturing the relationships in UHPC’s behaviour with an R squared score of 0.8857. The Random Forest ML model is also used in this paper to conduct a parametric study that will help in obtaining the compressive strength of UHPC with higher content of FA%, which is not sufficiently studied in the literature. Full article
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21 pages, 7785 KiB  
Article
Experiences Using MEMS Accelerometers on Railway Bearers at Switches and Crossings to Obtain Displacement—Awkward Situations
by Jou-Yi Shih, Paul Weston, Mani Entezami, Clive Roberts and Mark O’Callaghan
Infrastructures 2024, 9(6), 91; https://doi.org/10.3390/infrastructures9060091 - 28 May 2024
Viewed by 486
Abstract
A sleeper, or more generally a “bearer”, moves vertically under a passing train load. The extent of this motion depends on the static and dynamic load of the train, the train speed, and the support conditions at the bearer and its neighbours. Excessive [...] Read more.
A sleeper, or more generally a “bearer”, moves vertically under a passing train load. The extent of this motion depends on the static and dynamic load of the train, the train speed, and the support conditions at the bearer and its neighbours. Excessive motion, typically from voiding see-sawing, low support stiffness or possibly excessive stiffness, or even too little stiffness, are all of interest to maintainers. Typically, problems arise around transition zones, switches and crossings, but plain track with poor support can also be a problem. Within the last decade, low-cost micro-electro-mechanical system (MEMS) accelerometers have been used to capture the time history of vertical motion for use in condition monitoring. Existing condition monitoring systems often overlook or sometimes even ignore the possibility of problematic data, which seem to be common in monitored locations. It is essential to understand whether such “bad” data require further attention. Three problematic sites are presented, focussing on examples where the acceleration was higher than expected or the computed displacement was not as expected. Potential causes include wheel defects, hammering of the ballast by a hanging bearer, or high acceleration at some structural resonant frequency. The present paper aims to show the challenges of using MEMS accelerometers to collect data for condition monitoring and offers insights into the sort of problematic data that may be collected from real sites. Full article
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30 pages, 2850 KiB  
Review
Civil Integrated Management (CIM) for Advanced Level Applications to Transportation Infrastructure: A State-of-the-Art Review
by Ali Taheri and John Sobanjo
Infrastructures 2024, 9(6), 90; https://doi.org/10.3390/infrastructures9060090 - 24 May 2024
Viewed by 552
Abstract
The recent rise in the applications of advanced technologies in the sustainable design and construction of transportation infrastructure demands an appropriate medium for their integration and utilization. The relatively new concept of Civil Integrated Management (CIM) is such a medium; it enhances the [...] Read more.
The recent rise in the applications of advanced technologies in the sustainable design and construction of transportation infrastructure demands an appropriate medium for their integration and utilization. The relatively new concept of Civil Integrated Management (CIM) is such a medium; it enhances the development of digital twins for infrastructure and also embodies various practices and tools, including the collection, organization, and data-management techniques of digital data for transportation infrastructure projects. This paper presents a comprehensive analysis of advanced CIM tools and technologies and categorizes its findings into the following research topics: application of advanced surveying methods (Advanced Surveying); geospatial analysis tools for project planning (Geospatial Analysis); multidimensional virtual design models (nD Modeling); Integrated Geospatial and Building Information Modeling (GeoBIM); and transportation infrastructure maintenance and rehabilitation planning (Asset Management). Despite challenges such as modeling complexity, technology investment, and data security, the integration of GIS, BIM, and artificial intelligence within asset-management systems hold the potential to improve infrastructure’s structural integrity and long-term performance through automated monitoring, analysis, and predictive maintenance during its lifetime. Full article
(This article belongs to the Special Issue Recent Progress in Transportation Infrastructures)
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16 pages, 3066 KiB  
Article
Analysis of Traffic Injury Crash Proportions Using Geographically Weighted Beta Regression
by Alan Ricardo da Silva and Roberto de Souza Marques Buffone
Infrastructures 2024, 9(6), 89; https://doi.org/10.3390/infrastructures9060089 - 23 May 2024
Viewed by 350
Abstract
The classical linear regression model allows for a continuous quantitative variable to be modeled simply from other variables. However, this model assumes independence between observations, which, if ignored, can lead to methodological issues. Additionally, not all data follow a normal distribution, prompting the [...] Read more.
The classical linear regression model allows for a continuous quantitative variable to be modeled simply from other variables. However, this model assumes independence between observations, which, if ignored, can lead to methodological issues. Additionally, not all data follow a normal distribution, prompting the need for alternative modeling methods. In this context, geographically weighted beta regression (GWBR) incorporates spatial dependence into the modeling process and analyzes rates or proportions using the beta distribution. In this study, GWBR was applied to the traffic injury (fatal and non-fatal) crash proportions in Fortaleza, Ceará, Brazil, from 2009 to 2011. The results demonstrated that the local approach using the beta distribution is a viable model for explaining the traffic injury crash proportions, due to its flexibility in handling both symmetric and skewed distributions. Therefore, when analyzing rates or proportions, the use of the GWBR model is recommended. Full article
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