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A study was conducted on the interference factors during on-site inspection of infrared testing for composite insulators. The temperature curve of the composite insulator core rod in service status was detected online using infrared equipment. The fluctuation characteristics of the temperature curve of normal composite insulators and their error range on temperature difference were analyzed in detail. Furthermore, the effects of multi angle testing, occlusion, image clarity, testing distance, and lens focal length on the core rod temperature curve were further studied, and corresponding countermeasures were proposed. The experimental results indicate that the fluctuation characteristics of the temperature curve are closely related to environmental factors and testing equipment, and the testing accuracy can be improved by optimizing the testing methods and equipment parameters. In addition, for the measured infrared data, after removing high-frequency fluctuation parameters using preprocessing methods, defective insulators can be selected based on the temperature gradient and the two-dimensional graph of the umbrella skirt unit temperature difference. This work establishes a practical framework for standardizing infrared inspections of critical grid infrastructure.

In recent years, citizens’ attention towards air quality and pollution has increased significantly, and nowadays, odor pollution related to different industrial activities is recognized as a well-known environmental issue. For this reason, odors are subjected to control and regulation in many countries, and specific methods for odor measurement have been developed and standardized over the years. This paper, conceived within the H2020 D-NOSES project, summarizes odor measurement techniques, highlighting their applicability, advantages, and limits, with the aim of providing experienced as well as non-experienced users a useful tool that can be consulted in the management of specific odor problems for evaluating and identifying the most suitable approach. The paper also presents relevant examples of the application of the different methods discussed, thereby mainly referring to scientific articles published over the last 10 years.

In today’s photovoltaic (PV) power plants, traditional crystalline PV modules are the prevalent technology, which is highly susceptible to partial shading due to the risk of irreversible damage. Therefore, it is advisable to explore potential construction sites for objects that might cause shading, including high-voltage transmission towers, whose shading effects can be significant due to their height. By means of innovative simulation, using a model, validated with actual data, this study endeavored to deliver novel information related to the problems of shading by high-voltage transmission lines. In the context of Hungary, it examined the risk factors, technical and economic aspects, and possible solutions important for PV projects. It provides new insight, much needed also at the international level, considering the fact that the extent of the shadows cast by conductors on the surface at low Sun elevations is not known at present and neither are the shading characteristics of conductors between two transmission towers, depending on their height, in winter, when the Sun is low. An added practical benefit of the study is that its technical and economic approaches and the software solutions are all based on the practice of PV system design and construction. Related to the investigated issues, this can facilitate the formulation of the technical and economic aspects of suitable PV power plant building strategies in Hungary.

This study aims to establish an effective approach for evaluating the safety performance of road infrastructure. Road safety levels are typically quantified using safety performance indicators. However, due to the stochastic nature of accidents, many safety performance indicators cannot adequately and completely describe reality. Therefore, predictive methods based on regression models are widely used. This approach also allows for the identification of latent risk conditions in the infrastructure, even in the absence of accidents. Among available approaches, the Highway Safety Manual (HSM) methodology is chosen for its synthesis of validated highway research and best practices for incorporating safety into both new design and rehabilitation. For this study, a preliminary new version of HSM is used. The application of this method, which combines a predictive model with observed accidents through an empirical Bayesian approach, requires a calibration process that is crucial to tailoring this method to the specific study context. In this research, the predictive model is calibrated for single carriageway roads with one lane per direction across the Italian national network. Following calibration, the safety indicators are evaluated. The results obtained according to different indicators are compared to show the importance of adopting this method to counteract the regression to the mean of observed crashes. In fact, the method, supported by empirical Bayesian analysis, enables the identification of high-risk sections of the road network, selecting more sections that would be neglected by traditional indicators based solely on observed crashes. Finally, a possible approach to prioritizing sites for inspection based both on the excess of crashes and the Safety Potential (SAPO) is proposed. In addition, SAPO is adjusted to local conditions to account for the specific context and the decreasing trend of accidents over the years.

Prolonged exposure to odour emissions causes annoyance which leads to nuisance and consequently to complaints. Different methodologies exist in the literature to evaluate odour impacts, but not all are suitable to assess environmental odour nuisance. Information about their applicability criteria and comparison, is scarce and referred to short time analysis. The research presents and discusses the application of different methods to characterize and assess odour nuisance around an industrial plant localized in a sensitive area. Experimental activities are carried out through a long-time analysis programme. Field inspections and predictive methods are investigated and compared. A modification of the traditional dispersion modelling approach is proposed in order to adapt its application for the prediction of the odour nuisance. The offensiveness and location factors are identified as key parameters in the quantification of the perceived nuisance. The integrated dispersion modelling multi-level approach is highlighted as the most suitable for defining the plant strategies. The paper provides useful information to characterize environmental odour problems and identify appropriate solutions for an effective management of odorous sources, with the aim of reducing complaints, restoring the proper relationship between odorous plants and the surrounding communities and increasing the overall quality of the environment.

Traditional methods for assessing the stability of rubble mound breakwaters (RMBs) often rely on 2.5D data, which may fall short in capturing intricate changes in the armor units, such as tilting and lateral shifts. Achieving a detailed analysis of RMB geometry typically requires fully 3D methods, but these often hinge on expensive acquisition technologies like terrestrial laser scanning (TLS) or airborne light detection and ranging (LiDAR). This article introduces an innovative approach to evaluate the structural stability of RMBs by integrating UAV-based photogrammetry and the random sample consensus (RANSAC) algorithm. The RANSAC algorithm proves to be an efficient and scalable tool for extracting primitives from point clouds (PCs), effectively addressing challenges presented by outliers and data noise in photogrammetric PCs. Photogrammetric PCs of the RMB, generated using Structure-from-Motion and MultiView Stereo (SfM-MVS) from both pre- and post-storm flights, were subjected to the RANSAC algorithm for plane extraction and segmentation. Subsequently, a spatial proximity criterion was employed to match cuboids between the two time periods. The methodology was validated on the detached breakwater of Cabedelo do Douro in Porto, Portugal, with a specific focus on potential rotations or tilting of Antifer cubes within the protective layer. The results, assessing the effects of the Leslie storm in 2018, demonstrate the potential of our approach in identifying and quantifying structural changes in RMBs.

This study proposes a probability-based carbonation prediction approach for successful monitoring of deteriorating concrete structures. Over the last several decades, a number of researchers have studied the concrete carbonation prediction to estimate the long-term performance of carbonated concrete structures. Recently, probability-based durability analyses have been introduced to precisely estimate the carbonation of concrete structures. Since the carbonation of concrete structures, however, can be affected by material compositions as well as various environmental conditions, it is still a challenge to predict concrete carbonation in the field. In this study, the Fick’s first law and a Bayes’ theorem-based carbonation prediction approach is newly proposed using on-site data, which were obtained over 19 years. In particular, the effects of design parameters such as diffusion coefficient, concentration, absorption quantity of CO2, and the degree of hydration have been thoroughly considered in this study. The proposed probabilistic approach has shown a reliable prediction of concrete carbonation and remaining service life.

The yield and quality of rice are closely related to field management. The automatic identification of field abnormalities, such as diseases and pests, based on computer vision currently mainly relies on high spatial resolution (HSR) images obtained through manual field inspection. In order to achieve automatic and efficient acquisition of HSR images, based on the capability of high-throughput field inspection of UAV remote sensing and combining the advantages of high-flying efficiency and low-flying resolution, this paper proposes a method of “far-view and close-look” autonomous field inspection by unmanned aerial vehicle (UAV) to acquire HSR images of abnormal areas in the rice canopy. First, the UAV equipped with a multispectral camera flies high to scan the whole field efficiently and obtain multispectral images. Secondly, abnormal areas (namely areas with poor growth) are identified from the multispectral images, and then the geographical locations of identified areas are positioned with a single-image method instead of the most used method of reconstruction, sacrificing part of positioning accuracy for efficiency. Finally, the optimal path for traversing abnormal areas is planned through the nearest-neighbor algorithm, and then the UAV equipped with a visible light camera flies low to capture HSR images of abnormal areas along the planned path, thereby acquiring the “close-look” features of the rice canopy. The experimental results demonstrate that the proposed method can identify abnormal areas, including diseases and pests, lack of seedlings, lodging, etc. The average absolute error (AAE) of single-image positioning is 13.2 cm, which can meet the accuracy requirements of the application in this paper. Additionally, the efficiency is greatly improved compared to reconstruction positioning. The ground sampling distance (GSD) of the acquired HSR image can reach 0.027 cm/pixel, or even smaller, which can meet the resolution requirements of even leaf-scale deep-learning classification. The HSR image can provide high-quality data for subsequent automatic identification of field abnormalities such as diseases and pests, thereby offering technical support for the realization of the UAV-based automatic rice field inspection system. The proposed method can also provide references for the automatic field management of other crops, such as wheat.

The level of degradation of reinforced concrete bridges was evaluated based on the in-situ measurements performed on five reinforced concrete bridges under service located in the Czech Republic. The combined effect of carbonation and chlorides with respect to the corrosion of steel reinforcement, namely the pH and the amount of water-soluble chlorides, were evaluated on drilled core samples of concrete. Based on these parameters, the ratio between the concentrations of Cl– and OH, which indicates the ability of concrete to protect reinforcement, was calculated. All the data were statistically summarized and the relationships among them were provided. The main goal of this study is to evaluate the non-proportional effect of the amount of chlorides per mass of concrete on the risk of corrosion initiation and to localize the “critical” locations in the bridges that are the most affected by the degradation effects.

The Agricultural Experimental Station «Zarechnoye» LLP (AES «Zarechnoye» LLP, Kostanay region, Kazakhstan) has always been the initiator of introduction and dissemination of advanced innovative technologies; in this regard, the agriculture has been identified as the basis for the introduction of digitalization elements, i.e. precision agriculture. The use of elements of the precision agriculture system by the AES «Zarechnoye» LLP, the use of modern technology equipped with an automatic driving system, a differential fertilizer application system and plant protection products ensured a guaranteed yield with high technological indicators in an extremely arid year. The obtained yield level of grain crops exceeded the average regional level by 79.7%. An economic model for the introduction of precision agriculture elements for the farm «Agrofirma Karkyn» LLP with medium technical equipment, indicating the costs of applying the developed technologies per 1 ha, was built. The analysis of economic return was carried out as a result of increased productivity with indicating the payback period. The production and economic indicators of the surveyed farm in the Kostanay region, obtained as a result of constructing financial and economic models taking into account the introduction of precision agriculture elements, are presented. Based on the constructed financial and economic models, the calculations showed that the introduction of precision agriculture elements will reduce production costs for wheat growing to 1.6 thousand tenge per 1 ha of cultivated area, or 4% of all production costs. The payback period for simulated improvements in the implementation of precision agriculture elements for the farm, while maintaining the current processing area, will be 4.4 years. The data obtained during the study can be used by agricultural producers to develop measures to increase productivity and reduce the cost of production, as well as by government agencies to improve measures of state support and regulation in the field of agriculture, aimed at digitalization, the introduction of precision agriculture elements and increasing the competitiveness of agricultural production.