Infrastructure Maintenance Research Articles

Auxcoformer: Auxiliary and Contrastive Transformer for Robust Crack Detection in Adverse Weather Conditions

Crack detection is integral in civil infrastructure maintenance, with automated robots for detailed inspections and repairs becoming increasingly common. Ensuring fast and accurate crack detection for autonomous vehicles is crucial for safe road navigation. In these fields, existing detection models demonstrate impressive performance. However, they are primarily optimized for clear weather and struggle with occlusions and brightness variations in adverse weather conditions. These problems affect automated robots and autonomous vehicle navigation that must operate reliably in diverse environmental conditions. To address this problem, we propose Auxcoformer, designed for robust crack detection in adverse weather conditions. Considering the image degradation caused by adverse weather conditions, Auxcoformer incorporates an auxiliary restoration network. This network efficiently restores damaged crack details, ensuring the primary detection network obtains better quality features. The proposed approach uses a non-local patch-based 3D transform technique, emphasizing the characteristics of cracks and making them more distinguishable. Considering the connectivity of cracks, we also introduce contrastive patch loss for precise localization. Then, we demonstrate the performance of Auxcoformer, comparing it with other detection models through experiments.

Self-Healing Concrete Techniques and Technologies and Applications

The main weakest point of concrete is its exposure to cracks, and concrete structure repair is expensive, especially for infrastructure maintenance, which is difficult to access. The ability of self-healing concrete (SHC) to successfully heal fractures without the assistance of humans has received much attention since it increases operational life and lowers maintenance expenses. This paper reviews various techniques and technologies of autogenous and autonomous self-healing concrete. Much more attention is given to the autonomous SHC, including the encapsulation materials, capsule geometries, and healing agents. This is due to its accuracy for healing locations and better healing capabilities compared to the uniform hydration of autogenous SHC. Polymeric materials have shown great potential in both capsules and healing agents. Because they can meet the unusual demands of capsules, which include being flexible when mixing concrete and becoming brittle when cracks develop, the healing agent's viscosity must be low enough to allow it to flow out of the capsules and fill tiny cracks. In contrast, if the viscosity is too low, the healing agent will either seep out of the fracture or be absorbed by the pores of the concrete matrix. Additionally, some projects have been cited to demonstrate the feasibility of self-healing concrete in the construction industry.

Sustainable Urban Mobility for Road Information Discovery-Based Cloud Collaboration and Gaussian Processes

A novel cloud-based collaborative estimation framework for traffic management, utilizing a Gaussian Process Regression approach is introduced in this work. Central to addressing contemporary challenges in sustainable transportation, the framework is engineered to enhance traffic flow efficiency, reduce vehicular emissions, and support the maintenance of urban infrastructure. By leveraging real-time data from Priority Vehicles (PVs), the system optimizes road usage and condition assessments, contributing significantly to environmental sustainability in urban transport. The adoption of advanced data analysis techniques not only improves accuracy in traffic and road condition predictions but also aligns with global efforts to transition towards more eco-friendly transportation systems. This research, therefore, provides a pivotal step towards realizing efficient, sustainable urban mobility solutions.

HARNESSING DATA ANALYTICS FOR SUSTAINABLE BUSINESS GROWTH IN THE U.S. RENEWABLE ENERGY SECTOR

The U.S. renewable energy sector is at the forefront of addressing global environmental challenges, with a growing emphasis on sustainability and clean energy alternatives. This review explores the pivotal role of data analytics in fostering sustainable business growth within this dynamic industry. As renewable energy sources continue to gain prominence, leveraging data analytics becomes imperative for companies aiming to optimize operations, enhance efficiency, and contribute to long-term ecological objectives. Data analytics serves as a powerful tool for harnessing insights from vast and complex datasets generated by renewable energy systems. This review delves into the ways in which organizations can extract actionable intelligence from data, enabling informed decision-making and strategic planning. From predictive maintenance of renewable infrastructure to real-time monitoring of energy production, data analytics empowers businesses to streamline processes and mitigate operational risks. Furthermore, the review sheds light on the role of data analytics in promoting innovation and research within the U.S. renewable energy sector. By analyzing patterns, trends, and historical data, organizations can identify areas for technological advancements, thereby accelerating the development of cutting-edge solutions. This proactive approach not only contributes to sustainable business growth but also positions the sector as a driver of innovation in the broader landscape of clean energy technologies. The review also highlights the challenges and opportunities associated with the integration of data analytics in the renewable energy domain, considering factors such as data security, regulatory compliance, and the need for skilled professionals. The synthesis of data-driven insights with sustainability objectives not only enhances the competitiveness of businesses in the U.S. renewable energy sector but also aligns them with global efforts to address climate change and achieve a more sustainable energy future. 
 Keywords: Data, Business, USA, Renewable Energy, Sustainability, Review.

When does infrastructure hybridisation outperform centralised infrastructure paradigms? – Exploring economic and hydraulic impacts of decentralised urban wastewater system expansion

We explore the dynamics of centralised and decentralised wastewater infrastructure across various scenarios and introduce novel insights into their performance regarding structural vulnerability, hydraulic capacity, and costs. This study determines circumstances under which infrastructure hybridisation outperforms traditional centralised infrastructure paradigms. We combined system analysis to map out the modelling problem with the model-based exploration of the transition space using the novel TURN-Sewers model. System diagramming was used to identify the parameters or combinations of parameters that significantly influence the performance indicators being assessed. This allowed the creation of relevant simulation scenarios to identify circumstances where a decentralised sewer system could outperform a centralised one. TURN-Sewers was applied to model the infrastructure maintenance and generation of new infrastructure over 20 years for a municipality on the Swiss Plateau, considering a population growth rate of 0.03 a−1. Results show that decentralisation in expansion areas with higher densification can outperform the hydraulic performance and structural vulnerability of expanding centralised sanitary wastewater infrastructure. Decentralised systems can also offer economic advantages when capital expenditure costs for small-scale wastewater treatment plants are significantly reduced compared to current costs, particularly at higher discount rates, e.g. reaping effects of economies of scale. The findings of this study emphasise the potential of transition pathways towards decentralisation in urban water infrastructures and the value of models that allow the exploration of this transition space.

A crowdsensing-based framework for sound and vibration data analysis in smart urban environments

In this paper, we introduce a framework tailored to the precise analysis of sound and vibration data in the dynamic context of smart cities. By harnessing the power of crowdsensing data, our system offers a robust and highly adaptable solution for the real-time monitoring and comprehensive assessment of acoustic and vibrational parameters within urban environments. The rise of smart cities, fueled by sensor tech and data analysis, demands advanced tools to tackle urban environmental issues and improve residents’ quality of life. Our framework empowers city stakeholders, offering insights for informed decisions in urban planning, transportation, infrastructure maintenance, and public health. Key features of our system include data acquisition through mobile crowdsensing application, advanced signal processing algorithms for noise and vibration identification, and the integration of geospatial information to provide location-specific context. Furthermore, our framework supports scalable and adaptable data analytics, ensuring the efficient utilization of resources and the effective management of urban environments. The presented framework addresses the challenges of noise pollution, structural health monitoring, transportation optimization, and public safety in smart cities. By exploiting crowdsourced data, it promotes a collaborative approach to data collection, analysis, and decision-making, fostering an environment where cities can continuously evolve and adapt to the evolving needs of their residents. This paper offers a detailed exploration of the system’s architecture, and showcases its practical implementation, affirming its potential as a powerful starting point for advancing the science of smart cities.

Spatial-Temporal Dynamic Evolution of Land Deformation Driven by Hydrological Signals around Chaohu Lake.

The frequent occurrence of extreme climate events has a significant impact on people's lives. Heavy rainfall can lead to an increase of regional Terrestrial Water Storage (TWS), which will cause land subsidence due to the influence of hydrological load. At present, regional TWS is mostly obtained from Gravity Recovery and Climate Experiment (GRACE) data, but the method has limitations for small areas. This paper used water level and flow data as hydrological signals to study the land subsidence caused by heavy rainfall in the Chaohu Lake area of East China (June 2016-August 2016). Pearson's correlation coefficient was used to study the interconnection between water resource changes and Global Navigation Satellites System (GNSS) vertical displacement. Meanwhile, to address the reliability of the research results, combined with the Coefficient of determination method, the research findings were validated by using different institutional models. The results showed that: (1) During heavy rainfall, the vertical displacement caused by atmospheric load was larger than non-tidal oceanic load, and the influence trends of the two were opposite. (2) The rapidly increasing hydrologic load in the Chaohu Lake area resulted in greater subsidence displacement at the closer CORS station (CHCH station) than the more distant CORS station (LALA station). The Pearson correlation coefficients between the vertical displacement and water level were as high as -0.80 and -0.64, respectively. The phenomenon confirmed the elastic deformation principle of disc load. (3) Although there was a systematic bias between the different environmental load deformation models, the deformation trends were generally consistent with the GNSS monitoring results. The average Coefficients of determination between the different models and the GNSS results were 0.63 and 0.77, respectively. The results demonstrated the effectiveness of GNSS in monitoring short-term hydrological load. This study reveals the spatial-temporal evolution of land deformation during heavy rainfall around Chaohu Lake, which is of reference significance for water resource management and infrastructure maintenance in this area.

Advanced Underwater Measurement System for ROVs: Integrating Sonar and Stereo Vision for Enhanced Subsea Infrastructure Maintenance

In the realm of ocean engineering and maintenance of subsea structures, accurate underwater distance quantification plays a crucial role. However, the precision of such measurements is often compromised in underwater environments due to backward scattering and feature degradation, adversely affecting the accuracy of visual techniques. Addressing this challenge, our study introduces a groundbreaking method for underwater object measurement, innovatively combining image sonar with stereo vision. This approach aims to supplement the gaps in underwater visual feature detection with sonar data while leveraging the distance information from sonar for enhanced visual matching. Our methodology seamlessly integrates sonar data into the Semi-Global Block Matching (SGBM) algorithm used in stereo vision. This integration involves introducing a novel sonar-based cost term and refining the cost aggregation process, thereby both elevating the precision in depth estimations and enriching the texture details within the depth maps. This represents a substantial enhancement over existing methodologies, particularly in the texture augmentation of depth maps tailored for subaquatic environments. Through extensive comparative analyses, our approach demonstrates a substantial reduction in measurement errors by 1.6%, showing significant promise in challenging underwater scenarios. The adaptability and accuracy of our algorithm in generating detailed depth maps make it particularly relevant for underwater infrastructure maintenance, exploration, and inspection.

Parametric image-based concrete defect assessment method

Structural health monitoring aims to ensure the integrity of infrastructure. Assessing structural integrity through image classification techniques based on human perception is often challenging. Incorporating a morphological image-based crack detection algorithm can mitigate these limitations. This study proposes a parametric approach for concrete crack analysis using digital image processing techniques. The objective is to detect damage to the concrete surface of infrastructures, potentially exacerbating structural deterioration. The research justifies the utilization of digital parametric image processing techniques, including a weighted median filter, grayscale, Otsu filter, and other methods. These techniques were applied to evaluate different concrete images for crack detection. The findings were validated through Image Quality Assessment (IQA), determining the condition of the studied images and statistical properties using metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), Natural Image Quality Evaluator (NIQE), and Perceptual Image Quality Evaluator (PIQE). Additionally, uncontrollable factors such as variations in lighting effects under different conditions, testing setups, and different textures of the concrete were considered in this study. The comprehensive experimental results demonstrated that the Otsu filter outperforms other filtering techniques. Overall, the study achieved remarkable accuracy, approximately 95%, in detecting cracks on building infrastructure. The proposed method holds potential for integration with advanced techniques in practical applications for the maintenance and safety of infrastructure. This research not only contributes to technological advancements in concrete defect assessment but also carries significant implications for the future of automated and reliable structural health monitoring.

The use of copper as plant protection product contributes to environmental contamination and resulting impacts on terrestrial and aquatic biodiversity and ecosystem functions.

Copper-based plant protection products (PPPs) are widely used in both conventional and organic farming, and to a lesser extent for non-agricultural maintenance of gardens, greenspaces, and infrastructures. The use of copper PPPs adds to environmental contamination by this trace element. This paper aims to review the contribution of these PPPs to the contamination of soils and waters by copper in the context of France (which can be extrapolated to most of the European countries), and the resulting impacts on terrestrial and aquatic biodiversity, as well as on ecosystem functions. It was produced in the framework of a collective scientific assessment on the impacts of PPPs on biodiversity and ecosystem services in France. Current science shows that copper, which persists in soils, can partially transfer to adjacent aquatic environments (surface water and sediment) and ultimately to the marine environment. This widespread contamination impacts biodiversity and ecosystem functions, chiefly through its effects on phototrophic and heterotrophic microbial communities, and terrestrial and aquatic invertebrates. Its effects on other biological groups and biotic interactions remain relatively under-documented.

Lateral Track Buckling in Sweden: Insights from Operators and Infrastructure Managers

Rail transport is expected to become a key component in the development of a long-term sustainable transport system. The planning, construction, operation, and maintenance of railway infrastructure are crucial in this effort. Hence, it is essential to ascertain that the railway infrastructure withstands and is adapted to extreme weather conditions and climate change. This study focuses on evaluating climate adaptation measures for lateral track buckling in Sweden. Through a literature review and interview with an expert at Swedish Transport Administration, it is highlighted that the maintenance status of railway infrastructure plays a significant role in the occurrence of lateral track buckling. According to the expert, inadequate track maintenance is the primary cause of lateral track buckling rather than weather variables like air temperature. The interview also clarifies that the chain of events related to the handling of track buckling is mainly initiated by the observation of a discrete lateral irregularity by a train driver, whereupon the train dispatcher at the traffic management center stops traffic until the location in the track has been inspected by a track entrepreneur. During the inspection, up to half of the observed cases of track buckling turn out to be false.

MANAJEMEN PEMELIHARAAN SARANA DAN PRASARANA DALAM MENINGKATKAN MUTU PEMBELAJARAN DI SMAN 1 PURWAKARTA

This research aims to get an overview of the maintenance management of facilities and infrastructure in improving the quality of learning. This research was carried out at SMA Negeri 1 Purwakarta. This research refers to the management theory of G.R. Terry to see the implementation of facilities and infrastructure maintenance management at SMA Negeri 1 Purwakarta. This research uses a qualitative approach with descriptive methods because researchers want to get an in-depth picture of problems in the field. Data collection was carried out by observation, interviews, and documentation studies. The results of this research show that: 1). Planning for maintenance of facilities and infrastructure is programmed globally, according to repair needs, the budget plan is detailed and then proposed through RKAS. 2). Organizing the maintenance of facilities and infrastructure is carried out by all school personnel, both teachers and students. 3). Implementation of maintenance of facilities and infrastructure includes two activities, namely light repairs and heavy repairs. 4). Supervision of maintenance of facilities and infrastructure is carried out by reporting activities by teachers or deputy heads of facilities and infrastructure and evaluated at the end-of-year meeting.

Identification of damages in a concrete beam: a modal analysis based method

Structural Health monitoring (SHM) strategies can play a pivotal role in the perspective of enhancing structures and infrastructures life cycle and maintenance operations. A plethora of sensors and technologies can be employed in this field; in a seismic context, vibrational tests are particularly relevant, being able to give an insight on the dynamic characteristics of the structure itself. In particular, modal parameters can be considered in order to detect damages. A comparison between a certain test time and 0 test time (i.e., undamaged structure) is commonly performed; to this aim numerical models result particularly useful to provide baseline data (often unavailable in pre-existing structures), but they need to be validated before use. Non-contact techniques, like scanning laser Doppler vibrometry, can be exploited to do this. In this paper a numerical model of a scaled concrete beam is realized and validated through LDV data, then it is used to design load tests for progressive damages generation. Modal analysis is conducted after different load trials to evaluate changes of modal parameters in relation to the damage occurred; also, damage-related indices are proposed. The results confirmed the suitability of LDV for dynamic analyses of cement-based structures and this can be particularly useful when big structures (e.g., bridges) have to be monitored in-field. The numerical model was validated with acceptable absolute errors in terms of natural frequencies (between 26 Hz and 131 Hz) and high Modal Assurance Criterion (MAC) values (0.85-0.93). Moreover, the proposed methodology allows to detect damages also in a concise way through synthetic indices (with changes >50% in damaged vs undamaged conditions) and early warnings could be generated according to their values, hence supporting decision-making procedures in the building management scenario.

AI in renewable energy: A review of predictive maintenance and energy optimization

In the dynamic landscape of the burgeoning renewable energy sector, optimizing energy output, ensuring robust infrastructure maintenance, and seamless integration into the grid present formidable challenges. This paper delves into the transformative potential of artificial intelligence (AI) as a solution to these critical issues. The focus of this study is on the current state of AI applications within the renewable energy domain, particularly honing in on its profound impact on predictive maintenance and energy optimization across diverse sources such as solar, wind, and hydro. By examining the underlying AI techniques employed in this context, the research seeks to unravel the intricacies of how AI contributes to enhancing the efficiency and sustainability of renewable energy systems. A critical component of this exploration involves the analysis of successful case studies, illustrating real-world applications where AI has made substantial strides in predictive maintenance and energy optimization. These cases provide tangible evidence of the practical implications of incorporating AI into renewable energy practices. The research explores AI’s role in renewable energy, focusing on emerging trends and future directions. It aims to understand AI’s transformative influence on optimization, sustainability, and energy efficiency, fostering a more resilient and efficient energy landscape. AI is revolutionizing the renewable energy sector, transforming infrastructure maintenance, energy generation optimization, and integrating renewable sources into the grid. Its advanced analytics, predictive capabilities, and optimization are crucial in achieving global renewable energy targets. As AI technology evolves, its impact on the renewable energy landscape will deepen, paving the way for a cleaner, more sustainable future. By harnessing AI’s power, we can accelerate the transition towards a renewable energy future, ensuring a thriving planet for future generations.

Assessment of physical condition and status of maintenance of public primary schools infrastructure in Port Harcourt metropolis

This study presents a comprehensive assessment of the physical condition and maintenance status of public primary school infrastructure in Port Harcourt Metropolis. The investigation delves into various aspects, including the establishment and age of schools, building components, furniture conditions, security infrastructure, basic amenities, and environmental elements. The majority of schools were established after 1960, with a notable concentration of building construction between 2010-2020, indicating recent infrastructural development. While essential components such as classrooms, administrative offices, auditoriums, libraries, and ICT facilities are universally present, variations in the conditions of roofs, ceilings, and electricity supply from the national grid emphasize the need for targeted maintenance efforts. The study identifies a commendable commitment to transparency and accountability, as evidenced by the reporting of infrastructure conditions to higher authorities. However, a significant percentage reporting no major maintenance in the past 5 years raises concerns about the longevity of infrastructure. Recommendations include prioritizing maintenance on critical components, strategic investments in security infrastructure, and fostering sustainability through ongoing maintenance practices. The findings underscore the importance of a multifaceted approach to infrastructure management, aiming for a conducive and sustainable learning environment in public primary schools. The commitment to transparency, coupled with proactive maintenance strategies, is essential for fostering an environment conducive to effective teaching and learning, ultimately contributing to the overall well-being of the educational system in Port Harcourt Metropolis.

Solar power integration in Urban areas: A review of design innovations and efficiency enhancements

The increasing global emphasis on sustainable energy solutions has fueled a growing interest in integrating solar power systems into urban landscapes. This paper presents a comprehensive review of the current state of solar power integration in urban areas, with a focus on design innovations and efficiency enhancements. Urban environments pose unique challenges for solar power implementation, such as limited space, shading, and aesthetic considerations. This review explores a range of design innovations aimed at overcoming these challenges, including the integration of solar panels into building facades, windows, and urban infrastructure. The examination of these advancements provides insights into maximizing energy capture while seamlessly blending solar technologies into the urban fabric. Efficiency enhancements play a pivotal role in the viability of solar power integration. The paper analyzes emerging technologies and methodologies that boost the efficiency of solar energy systems in urban contexts. This includes advancements in photovoltaic cell technologies, energy storage solutions, and intelligent grid integration. The exploration of these efficiency-enhancing strategies sheds light on the potential for increased energy yield and grid reliability in urban solar installations. Moreover, the paper discusses the role of smart city concepts in optimizing solar power integration. The integration of data analytics, Internet of Things (IoT) devices, and artificial intelligence is explored as a means to enhance the monitoring, control, and maintenance of urban solar infrastructure. In conclusion, this review provides a nuanced examination of the evolving landscape of solar power integration in urban areas. By exploring design innovations and efficiency enhancements, the paper contributes valuable insights for researchers, policymakers, and practitioners seeking to promote sustainable energy solutions in the urban environment.

Infrastructure Management for Children with Special Needs in Inclusive Schools: A Literature Review

The importance of infrastructure management for children with special needs in inclusive schools is interesting to study more deeply in order to achieve educational goals. There are several articles on infrastructure management in inclusive schools that can be found. The review was conducted to find out how infrastructure management is conducted in inclusive schools. Based on the results of the literature review, it was found that there is a significant relationship between infrastructure management in inclusive schools in order to achieve learning objectives. The management of infrastructure facilities has six important points in its implementation, namely planning needs, procurement of infrastructure facilities, inventory, maintenance, use, and elimination. This is carried out in order to achieve educational goals in inclusive schools.

Administrasi Sarana dan Prasarana Pendidikan

In this article, the author will elaborate on the governance of facilities and infrastructure within the school environment. The management of facilities and infrastructure emerges as a crucial aspect in the realm of education. Every activity within the school necessitates facilities and infrastructure, directly or indirectly, to support the smooth progress of the learning process. The significance of managing facilities and infrastructure significantly contributes to the achievement of educational objectives. The educational environment can be influenced both positively and negatively by the maintenance of school facilities and infrastructure. Effective management of infrastructure serves as the key to success, although there are still some areas that have not been fully optimized. Therefore, the author compiles this article with the hope that it can serve as a crucial reference in the administration of facilities and infrastructure in the educational context.

Intelligent Electric Grid Maintenance via an Adaptive Predictive Maintenance Optimization Algorithm

This study presents a novel framework for the predictive maintenance of electric grid infrastructure, leveraging an Adaptive Predictive Maintenance Optimization (APMO) algorithm. Our comprehensive system architecture integrates IoT sensors and drone surveillance, harnessing grid computing for data processing and machine learning for analytics. The APMO algorithm, underpinned by reinforcement learning, dynamically refines maintenance schedules, enhancing operational efficiency. Results from simulated sensor data exhibit a negligible correlation, indicating a multi-faceted approach to predictive analytics. Specifically, the Health Index distribution showed a wide range of 0.7 to 1.0, while the electrical current maintained a stable mean at 10 A. Further, the APMO algorithm demonstrated a promising improvement in maintenance efficiency from 40% to 75% over a year. This research introduces a scalable, robust system for grid management, paving the way for smarter infrastructure maintenance.

Supervisory Roles of School Heads in Public Stand-Alone Senior High School: A Phenomenological Case Study

Supervision is inseparable with education. In school, the principal performs supervisory roles to guide personnel and implement activities. However, the school head in a stand-alone senior high school reported challenges that influenced her supervisory roles. Hence, this study sought to examine the challenges and supervisory roles of the school head in stand-alone senior high school. Three research questions guided the paper which employed phenomenological-case study. One school head and six teachers in the same school of Region XI were chosen using the purposive sampling technique. The researchers conducted in- depth interview using the Office Performance Commitment and Review Form (OPCRF). Employing Colaizzi’s technique and content analysis, the researchers analyzed and clustered responses into themes. Results revealed that the school head encountered challenges in their supervisory role such as limited resources, curriculum demands and changes, teacher retention, community and parental involvement, and maintenance and improvement of infrastructures and facilities. With the findings of the supervisory roles and supervisory roles insights, the school heads are encouraged to create a strategic plan that contains clear steps and measures to address the supervisory challenges to be guided on what roles to take. Department of Education leaders might also help them address these challenges by assigning senior high school focal persons to directly respond to the needs and capacitate the school heads to enhance their supervisory skills. Moreover, stakeholders are encouraged to strengthen their involvement in stand-alone senior high schools. Lastly, future researchers might be inspired conduct a related study with more participants in a single case to comprehensively investigate their supervisory roles.