Condition Index Research Articles

COMPARATIVE ANALYSIS OF EVALUATION CRITERIA FOR UNPAVED RURAL ROADS: A CASE STUDY

This study focused on the evaluation of the condition of the unpaved rural road: Sauces Norte-Imbana in the province of Loja, using two main indicators: Unsurfaced Road Condition Index (URCI) and the International Roughness Index (IRI). The IRI was measured using three different devices: MERLIN, Roughometer III, and a mobile application. The collected data were statistically analyzed to determine their distribution and the correlation between the variables. The results revealed a weak correlation between these variables, indicating that each methodology may be assessing different aspects of the road condition or due to the particular conditions of the road. The lack of a strong relationship may be related to the specific characteristics of the studied road, such as its sinuosity and the influence of environmental conditions on roughness measurements. This study underscores the importance of considering a variety of factors when evaluating the condition of unpaved roads and the need to develop and apply suitable evaluation techniques for the specific conditions of each road.

Evaluating Pavement Condition Index and Maintenance Management using Artificial Neural Networks

The pavement condition index (PCI) calculates pavement conditions based on current distresses. In traditional PCI calculation, a visual inspection method collects field data such distresses and stiffness. Data helps anticipate PCI values, a lengthy and difficult process. This research aims to create a simple, adaptable model that shows how PCIs, torments, and stiffness relate. Artificial neural networks (ANN) forecast PCI values for various parts, eliminating manual labour and specialized procedures. Based on distresses, the PCI estimates pavement conditions. For typical PCI intentions, a visual inspection device collects field data such distresses and stiffness. The data allows time-consuming and complicated PCI estimation. This study seeks to construct a simple, extensible model that links PCIs, torments, and rigidity. ANN prediction part PCI values, eliminating the need for manual labour and specialized technologies.

Multi-criteria decision support system using AHP method to determine the weight of transformer paper insulation index

The transformer has evolved into an electrical device that operates based on the principle of electromagnetic induction without altering the frequency, capable of transferring electric energy from one or more electrical circuits to others by means of magnets. Insulation material is utilized as equipment to safeguard electrical components, where the resilience of each insulation plays a significant role in the functioning of the system. In addition to providing mechanical strength and distance, paper insulation functions as a dielectric medium. The quality of paper insulation deteriorates due to excessive heat generated from oil oxidation, leading to a degradation process known as depolymerization. In addressing this issue, a Decision Support System is required to aid in calculating the condition index of the paper insulation based on given parameters. The chosen method is the Analytic Hierarchy Process (AHP) utilizing multiple criteria. The outcome of applying this method is that the entire system operates as intended, and the calculation process yields appropriate results. Testing results, which involve a comparison between manual calculations and the Confusion Matrix system, exhibit a 100% accuracy rate. Additionally, user testing yields an average satisfaction level of 90.97%. In conclusion, this system adequately fulfils its intended purpose and significantly helps to determine the quality level of paper insulation in transformers.

How do different modalities of land use practices impact the environmental features and macroinvertebrates? An assessment of mountain streams from Patagonia, Argentina

Agricultural intensification is a significant process, transforming the land and modifying stream water quality and biodiversity. Land-use practices are associated with substantial changes in vegetation structure, significant inputs of nutrients, erosion, and increased sedimentation, all of which profoundly impact aquatic biota and ecosystem functioning. The effect of different agricultural practices (horticulture, extensive and intensive livestock) on stream water quality was compared in terms of physicochemical features, bacteria, habitat condition, riparian ecosystem, and macroinvertebrate variables in a temperate rural landscape (Patagonia). The main objective was to identify which variables related to land use were determinants in the configuration of biotic communities. Significantly higher values in water temperature, conductivity, nutrients, and bacteria were found at intensively developed sites than at reference ones. The quality of the habitat and the riparian ecosystem had been profoundly modified. A multidimensional scaling analysis revealed that macroinvertebrate assemblages also differed among land use categories, with the variables: total phosphorous, total nitrogen, bacteria (E. coli, mesophilic and total coliforms), water temperature, and oxygen having the highest predictive power. Macroinvertebrate metrics (richness of EPT (Ephemeroptera+Plecoptera+Trichoptera), Plecoptera, shredders and scrapers, and the percentage of scrapers and collectors) proved appropriate bioindicators for discriminating intensive land use impacts and highlighted a trend of biodiversity loss. Plecoptera richness, a taxa group with a high level of endemism, responded significantly to habitat condition degradation. These results confirm that the intensification of land use leads to increased nutrient levels and suppressed aquatic-terrestrial interactions involved in the provision of allochthonous detrital input, shade, and habitat structure for larval stages and adults. The Habitat Condition Index appears to be a useful tool for rapid stream assessment and providing early warnings. Several ecological services are threatened in these Patagonian agroecosystems. Restoring riverbank heterogeneity and improving management practices that ameliorate nutrient and sediment inputs are crucial to enhance biodiversity.

Hazardous impacts of heavy metal pollution on biometric and biochemical composition of pearl oyster Pinctada radiata from five sites along Alexandria coast, with reference to its potential health risk assessment

This study investigated the effect of heavy metals on the pearl oyster Pinctada radiata from 5 sites along the coast of Alexandria, with focus on its ecological health and potential risks to human consumption. Pollution results showed that Abu-Qir had the highest Cu and Cd values. Montaza and Eastern Harbor had the highest Fe and Pb values, respectively. Statistically, differences in metal concentrations among study sites were significant (p < 0.05). Non-carcinogenic risk (TTHQ) of tested metals and carcinogenic ones of Cd and Pb showed “high risk” on human health by consuming pearl oysters. Morphometric measurements and condition indices were studied to assess growth patterns and health in relation to heavy metals exposure. Key findings showed detectable declines in size and condition index in Eastern Harbor, whereas Abu-Qir recorded the highest values. This condition index performance presented Abu-Qir, Mammora, and Miami as ideal locations for spat collection and oyster rearing, potentially enhancing Egyptian pearl farming. Average values of spatial proximate contents of pearl oyster showed that it was rich in proteins (33.07–58.52%) with low fat content (1.39–1.87%) and carbohydrates (9.72–17.63%). Biochemical composition of pearl oyster demonstrated its high nutritional value which supported its promotion as a functional food for human consumption. The calorie content of pearl oyster was less than 2 Kcal, making this species an alternative source of healthy food to reduce obesity. Regression analysis indicated that Cu, Cd, and Pb had significant effect on 2-diphenyl-1-picrylhydrazyl (DPPH) scavenging activity, calories, vitamins, and pigment content of the collected oysters.

EVALUATION OF ACUTE AND SEMI-CHRONIC TOXICITY OF THE REMEDY “BO PHE DINH SUYEN QY” ON EXPERIMENTAL ANIMALS

Purpose: To evaluate the acute toxicity and semi-chronic toxicity of the remedy “Bo phe dinh suyen QY” on experimental animals. Subjects and methods: The remedy “Bo phe dinh suyen QY” meeting basic standards was used to evaluate for acute toxicity in Swiss white mice, and for semi-chronic toxicity in Wistar white rats. Acute toxicity was assessed according to the Litchfield-Wilcoxon method. Sub-chronic toxicity was assessed according to the guidance of the Ministry of Health of Vietnam. Results: The remedy “Bo phe dinh suyen QY” at a dose of 375 g/kg mouse body weight/24 hours did not cause acute toxicity or death of experimental mice. For 28 consecutive days, rats administered “Bo phe dinh suyen QY” at doses of 18.2 g/kg/day and 54.6 g/kg/day without affecting their general condition and hematological indices. Conclusions: The remedy “Bo phe dinh suyen QY” was safe in the assessment of acute toxicity (on mice) and semi-chronic toxicity (on rats).

Exploring the Potential of the Google Earth Engine (GEE) Platform for Analysing Forest Disturbance Patterns with Big Data

Climate change has led to various adverse consequences, with natural disasters being one of the most striking outcomes. Natural disasters negatively impact life, causing significant disruptions to the ecosystem. Prompt identification of affected areas and initiation of the rehabilitation process are imperative to address the disturbances in the ecosystem. Satellite imagery is employed for the rapid and cost-effective detection of damages caused by natural disasters. In this conducted study, the outputs of climate change wildfire, forest change detection, and drought analysis, have been examined, all of which worsens the impacts on the ecosystem. The analysis of drought involved using MODIS data, while Sentinel-2A satellite images were utilized to identify wildfire areas and changes in forested regions caused by windthrow. The research focused on Ganja, Azerbaijan, as the area for drought analysis. The driest June between 2005 and 2018 was assessed using the Vegetation Condition Index (VCI) in conjunction with data from the National Centers for Environmental Information (NOAA). At the Düzce Tatlıdere Forest Management Directorate, the Normalized Difference Red Edge Index (NDRE) was utilized between the years 2018 and 2019 to detect the changes occurring in forested areas due to windthrow. The NDRE synthetic band was added to satellite images for the years 2018 and 2019, and a Random Forest (RF) algorithm was employed to classify the data. The classification results were evaluated using Total Accuracy and Kappa statistics. The study includes the detection of the Normalized Burn Ratio (NBR) applied to determine the extent of the wildfire that occurred in the Solquca village of the Qabala region in Azerbaijan in 2021. According to the analysis of the VCI and NOAA, June 2014 was identified as the driest month in Ganja. In the Tatlıdere region, the analysis indicated that 4.22 hectares experienced reforestation, while 24 hectares experienced deforestation. The NBR analysis has revealed that ~1007 hectares of land were burned in the Solquca village of Qabala. The analyses conducted provide information regarding the use of satellite imagery in relation to changes in forest areas due to drought, wildfire, and windthrow.

ROAD DAMAGE EVALUATION ON RIGID PAVEMENT WITH PAVEMENT CONDITION INDEX METHOD IN KLAKAH REJO-BENOWO ROAD, SURABAYA CITY, EAST JAVA

The research objective was to analyze the level and type of damage from every kind of damage on the Klakah Rejo Benowo road by calculating the rigid pavement condition index value and providing solutions. This study took the location of the Klakah Rejo-Benowo road section, Surabaya City—data analysis by calculating the Deduct Value, m, TDV, CDV, and PCI. The research results state it was concluded that five types of damage occurred in Jalan Klakah Rejo-Benowo, Surabaya City. The types of damage are Grooves, Patches, Alligator Cracks, Weathering and Loose Granules, and Pits. Damage caused by high rainfall conditions (&gt; 900mm/year) &amp; Daily Traffic Volume exceeds the provisions of the Binamarga. After analysis, it shows that the road pavement is in Moderate/Fair condition; field analysis results and values calculated using the PCI (Pavement Index Condition) method for damaged road conditions can provide recommendations for road repairs using the Asphalt Institute MS-17 method, namely patching and resurfacing. Based on the analysis of the road condition values obtained, the solution must be patched and re-coated so that water does not quickly seep into the road layer, which causes more severe road damage.

Shorter telomeres are associated with shell anomalies in a long‐lived tortoise

AbstractAge‐related telomere length (TL) variation is relatively well‐described for mammals, birds and other model organisms. Nevertheless, it remains largely unknown in ectotherms, especially turtles and tortoises, which are extremely long‐lived species with slow or negligible senescence. In this study, we described TL dynamics in wild spur‐thighed tortoises (Testudo graeca), one of the chelonian species with the lowest aging rates. By combining cross‐sectional (single) and longitudinal (capture–recapture) samplings, we assessed the relationship between TL and individual characteristics (sex, age, individual growth rate, body condition index, presence of shell anomalies). We did not find any association between TL and sex, individual growth rate, or body condition. However, the relationship with age remains uncertain, likely due to the complex dynamics of TL over time. Interestingly, shorter telomeres correlated significantly with shell anomalies, which are usually assumed as a fitness proxy for reptiles. Overall, our results suggest TL as a potential indicator for ontogenetic studies on tortoises, while its utility as a marker of biological age appears limited.

Modified Asphalt with Graphene-Enhanced Polymeric Compound: A Case Study

In recent years, the increased use of heavy commercial vehicles with higher axle weights has required the development of innovative technologies to improve the mechanical properties of asphalt concrete conglomerates, such as fatigue resistance and rutting. This study offers a comprehensive comparative analysis of different types of asphalt concrete tested in four trial sections (S1, S2, S3, S4) of the SP3 Ardeatina rural road in Rome, under actual traffic and operational conditions. More precisely, the pavement technologies applied include modified asphalt concrete with graphene and recycled hard plastics for S1, asphalt concrete modified with styrene–butadiene–styrene (SBS) for S2, asphalt concrete with a standard polymeric compound for S3, and traditional asphalt concrete for S4. The evaluation approach involved visual inspections in order to calculate the pavement condition index (PCI) and falling weight deflectometer (FWD) tests. In addition, back-calculation analyses were performed using ELMOD software to assess the mechanical properties. The laboratory tests revealed superior properties of M1 in terms of its resistance to permanent deformations (+13%, +15%, and +19.5% compared to M2, M3, and M4, respectively) and stiffness (10,758 MPa for M1 vs. 9259 MPa, 7643 MPa, and 7289 MPa for M2, M3, and M4, respectively). These findings were further corroborated by the PCI values (PCIS1 = 65; PCIS2 = 17; PCIS3 = 28; PCIS4 = 29) as well as the FWD test results after 5 years of investigation, which suggests greater durability and resistance than the other sections.

Special Issue on Fatigue, Performance, and Damage Assessments of Concrete

This Special Issue covers the latest research on fatigue, performance, and damage assessments of concrete. Concrete fatigue is known as a phenomenon characterized by the repetition of loads. The fatigue-based performance of concrete may be affected by a combination of diverse loads, mechanical strength properties, and environmental changes. These factors eventually lead to poor concrete performance. In this Special Issue, nine papers were accepted, divided into three subgroups: two papers on fatigue, two papers on performance, and five papers on damage assessment. Unlike articles published in past Special Issues, our Special Issue contains papers that address concrete performance in concrete pavement applications. In particular, six of the articles contained in this issue concentrate on pavement performance and damage assessments, especially in terms of the Pavement Condition Index. Together with a brief introduction, the keywords, research significance and potential impacts of each article are summarized in this Editorial.

Configuring Taiwan’s highway bridge performance indicator: a semi-quantitative risk assessment approach

The Taiwan Bridge Management System (TBMS) calculates the condition index (CI) of every highway bridge in its inventory using the latest findings from biennial routine inspections. However, the computational approach for CI can yield skewed results and make a bridge appear healthier than it actually is. To address this issue, a highway bridge performance indicator called the bridge risk index (RI) is presented. The RI is determined by combining the risks associated with the failure of each bridge component, which are evaluated using a semi-quantitative risk assessment approach. This evaluation considers the probability and consequence of a bridge component’s failure as a function of a component's deterioration state and criticality to the bridge’s overall structural safety and serviceability, respectively. To validate the effectiveness of the RI, inspection data from a sample of seven highway bridges were extracted from the TBMS and used to compare the RI with the CI. The results showed that the RI and its associated parameters can mitigate the inherent issues observed with the CI. Hence, the RI has the potential to be a valuable decision-support tool for transport agencies that rely on similar routine inspection processes to enhance their bridge management systems.

Spatial Assessment of Groundwater Salinity and Its Impact on Vegetation Cover Conditions in the Agricultural Lands of Al-Ahsa Oasis, Saudi Arabia

This study investigated the complex relationship among groundwater salinity, soil texture, and vegetation health in Al-Ahsa Oasis, Saudi Arabia. Utilizing vegetation condition index data from 5 years of satellite imagery, alongside ground measurements of groundwater salinity and soil analysis, the study unveiled significant spatial heterogeneity. The vegetation health and groundwater salinity in the study area ranged from 27 to 70% and from 1.7 to 9.6 dS m−1, respectively, and exhibited contrasting patterns, with a high vegetation condition index (healthy vegetation) in the north and east versus high salinity in the southwest. The applied cross-tabulation statistics revealed a strong negative correlation between the vegetation condition index and salinity, with “good” and “normal” vegetation health primarily coinciding with “slightly saline” groundwater. Soil texture further modulated the vegetation response, as “Silt loam” soils corresponded with “good” VCI, while “silty clay loam” only supported “dry” and “normal” conditions. The applied spatial autocorrelation analysis confirmed this, showing low vegetation clustering with “silt” texture and high salinity in the mid-west of the study area (p &lt; 0.05). Outlier districts hint at local factors beyond simple salinity–vegetation health relationships. Finally, a conditional plot, applied as a spatial statistical approach, revealed that low vegetation health spatially correlates with high groundwater salinity (&gt;5 dS m−1) on “silt loam” in the mid-west, while a higher vegetation condition index aligned with lower groundwater salinity in the east and north. These findings highlight the critical role of spatial variability and the need for advanced modeling and data integration to understand and predict the responses of vegetation to environmental challenges, and will be crucial for optimizing water management to ensure the long-term sustainability of Al-Ahsa Oasis.

Multi-collinearity in Research and Wayforward

In multi-linear regression, there will be two or more independent variables also known as predictor variables. The main task in multi-linear regression is to find how the predictor variable impacts the dependent variable when there is a change in predictor variables by one unit. However, without testing multi-co linearity between these predictor variables, such a model will create difficulties in terms of defining the real impact of the predictor variable (independent) on the predicted variable (dependent). This situation makes us aware in terms of variable selection while conducting multi-linear regression to find the exact impact. To explain this issue, 14 students were selected and asked to fill up the form with their marks obtained in the recent examination, the number of study hours at home, the number of hours hanging out with friends, and the number of copies added during an exam. The research aimed to observe if there is a multi-co linearity issue between predictor variables (number of study hours, number of hours hanging out with friends, and number of copies added during an exam). SPSS-version 25 is used to find a correlation matrix between predictor variables, matrix scatter plot, the value of Tolerance, Variance Inflation Factor (VIF) value, condition index, and variation proportion. Multi-co linearity issue observed in two predictor variables thus further solution explained.

Nano-scale and micron-scale plastics amplify the bioaccumulation of benzophenone-3 and ciprofloxacin, as well as their co-exposure effect on disturbing the antioxidant defense system in mussels, Perna viridis

Plastics ranging from nano-scale to micron-scale are frequently ingested by many marine animals. These particles exhibit biotoxicity and additionally perform as vectors that convey and amass adsorbed chemicals within organisms. Meanwhile, the frequency of detection of the benzophenone-3 and ciprofloxacin can be adsorbed on plastic particles, then accumulated in bivalves, causing biotoxicity. To understand their unknown accumulative kinetics in vivo affected by different plastic sizes and toxic effect from co-exposure, several scenarios were set up in which the mode organism were exposed to 0.6 mg/L of polystyrene carrying benzophenone-3 and ciprofloxacin in three sizes (300 nm, 38 μm, and 0.6 mm). The live Asian green mussels were chosen as mode organism for exposure experiments, in which they were exposed to environments with plastics of different sizes laden with benzophenone-3 and ciprofloxacin, then depurated for 7 days. The bioaccumulation and depuration kinetics of benzophenone-3 and ciprofloxacin were measured using HPLC-MS/MS after one week of exposure and depuration. Meanwhile, their toxic effect were investigated by measuring the changes in six biomarkers (condition index, reactive oxygen species, catalase, glutathione, lipid peroxidation, cytochrome P450 and DNA damage). The bioconcentration factors in mussels under different exposure conditions were 41.48–111.75 for benzophenone-3 and 6.45 to 12.35 for ciprofloxacin. The results suggested that microplastics and nanoplastics can act as carriers to increase bioaccumulation and toxicity of adsorbates in mussels in a size-dependent manner. Overproduction of reactive oxygen species caused by microplastics and nanoplastics led to increased DNA damage, lipid peroxidation, and changes in antioxidant enzymes and non-enzymatic antioxidants during exposure. Marked disruption of antioxidant defenses and genotoxic effects in mussels during depuration indicated impaired recovery. Compared to micron-scale plastic with sizes over a hundred micrometers that had little effect on bivalve bioaccumulation and toxicity, nano-scale plastic greatly enhanced the biotoxicity effect.

An Integrated Grassland Fire-Danger-Assessment System for a Mountainous National Park Using Geospatial Modelling Techniques

Grasslands are key to the Earth’s system and provide crucial ecosystem services. The degradation of the grassland ecosystem in South Africa is increasing alarmingly, and fire is regarded as one of the major culprits. Globally, anthropogenic climate changes have altered fire regimes in the grassland biome. Integrated fire-risk assessment systems provide an integral approach to fire prevention and mitigate the negative impacts of fire. However, fire risk-assessment is extremely challenging, owing to the myriad of factors that influence fire ignition and behaviour. Most fire danger systems do not consider fire causes; therefore, they are inadequate in validating the estimation of fire danger. Thus, fire danger assessment models should comprise the potential causes of fire. Understanding the key drivers of fire occurrence is key to the sustainable management of South Africa’s grassland ecosystems. Therefore, this study explored six statistical and machine learning models—the frequency ratio (FR), weight of evidence (WoE), logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) in Google Earth Engine (GEE) to assess fire danger in an Afromontane grassland protected area (PA). The area under the receiver operating characteristic curve results (ROC/AUC) revealed that DT showed the highest precision on model fit and success rate, while the WoE was used to record the highest prediction rate (AUC = 0.74). The WoE model showed that 53% of the study area is susceptible to fire. The land surface temperature (LST) and vegetation condition index (VCI) were the most influential factors. Corresponding analysis suggested that the fire regime of the study area is fuel-dominated. Thus, fire danger management strategies within the Golden Gate Highlands National Park (GGHNP) should include fuel management aiming at correctly weighing the effects of fuel in fire ignition and spread.

The effects of diet-shifting from invertebrates towards fruit on the condition of autumn-migrant Catharus thrushes.

Migration is an energetically challenging and risky life history stage for many animals, but could be supported by dietary choices en route, which may create opportunities to improve body and physiological condition. However, proposed benefits of diet shifts, such as between seasonally available invertebrates and fruits, have received limited investigation in free-living animals. We quantified diet composition and magnitude of autumn diet shifts over two time periods in two closely-related species of migratory songbirds on stopover in the northeastern U.S. (Swainson's thrush [Catharus ustulatus], long-distance migrant, N = 83; hermit thrush [C. guttatus], short-distance migrant, N = 79) and used piecewise structural equation models to evaluate the relationships among (1) migration timing, (2) dietary behavior, and (3) morphometric and physiological condition indices. Tissue isotope composition indicated that both species shifted towards greater fruit consumption. Larger shifts in recent weeks corresponded to higher body condition in Swainson's, but not hermit thrushes, and condition was more heavily influenced by capture date in Swainson's thrushes. Presence of "high-antioxidant" fruits in fecal samples was unrelated to condition in Swainson's thrushes and negatively related to multiple condition indices in hermit thrushes, possibly indicating the value of fruits during migration is related more to their energy and/or macronutrient content than antioxidant content. Our results suggest that increased frugivory during autumn migration can support condition, but those benefits might depend on migration strategy: a longer-distance, more capital-dependent migration strategy could require stricter regulation of body condition aided by increased fruit consumption.

Monitoring Drought Tolerance Mechanisms of Sorghum and Maize Under Unevenly Distributed Precipitation

The study examines the morpho-physiological differences in leaf characteristics between two species of C4 plants: sorghum and maize. The research was conducted in field conditions where plants were rainfed. Both species different varieties were cultivated at two distinct sites that exhibited variations in soil texture and drought stress incidence according to the vegetation condition index (VCI). Samples were collected during various growth stages to analyze the relative water content (RWC), proline levels, and stomatal density. Sorghum plants displayed higher RWC, proline levels, and stomatal density than maize plants. In sorghum, the biochemical traits, such as the proline content, may play a more critical role in withstanding water-limited conditions than in maize in our experiment. Under the same water restriction period, sorghum showed higher RWC levels. Sorghum plants reduced stomatal density under more water-limited conditions, which proves its plasticity. Additionally, early maturation played a crucial role in both species. The early sorghum variety KWS Kallisto and maize variety Walterinio KWS had a more stable yield at both sites. Nevertheless, the highest yields were found in the later varieties, KWS Hannibal and KWS Inteligens. The higher proline levels and the relative water content are drought-tolerant mechanisms and may be used to indicate drought intensity in field conditions. Our findings spotlight the influence of genetic diversity and genotype-environment interactions in determining crop responses to drought stress, providing valuable information for future breeding programs to enhance drought tolerance in crops.

THE BENEFIT EVALUATION FRAMEWORK OF USING PERFORMANCE-BASED CONTRACTING IN URBAN ROAD MAINTENANCE

Performance-based contracts (PBC) have been adopted increasingly in various countries. However, establishing an objective and convincing benefit evaluation framework to facilitate promotion remains a significant challenge for those who wish to use PBCs. A recent study introduced PBC in the context of daily inspection and repair of urban roads. The pilot program in that study successfully addressed the needs of road users, road maintenance authorities, and contractors. Consequently, this study develops the benefit evaluation framework for this type of PBC for broader applications. The proposed benefit evaluation framework, consisting of one qualitative and eight quantitative performanceevaluation items, calculates the effectiveness of introducing PBC to verify its practicality. The validation results show that qualitative benefit evaluation using the Pavement Condition Index achieves a “satisfactory” rating. For the quantitative benefit evaluation, an overall benefit-cost ratio (BCR) of 5.35 suggests that it is economically viable. The developed benefit evaluation framework can be used as a simple and easy-to-use evaluation tool for other organizations to assess whether to introduce PBC. This study fills this research gap by addressing the use of PBCs in the maintenance of all categories of toll-free roads, and contributes to expanding the application of PBCs in road maintenance services.

Examination of the Effects of Climate Change on Türkiye through the Google Earth Engine Platform

The objective of this research is to assess the effects of climate change on Türkiye by utilizing data catalogues provided by the Google Earth Engine (GEE) cloud-computing platform. The utilized data catalogues encompassed precipitation, Land Surface Temperature (LST), EvapoTranspiration (ET), Potential EvapoTranspiration (PET), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Vegetation Condition Index (VCI), and Forest Area Loss (FAL). Data spanning the years 2001-2022 were collected, and analyses were conducted separately for seven geographical regions of Türkiye on both an annual and seasonal basis. Initially, trend analysis techniques were applied to the individual data sets, followed by an examination of correlations among them. Notably, significant decreasing and increasing trends were observed in annual precipitation and LST data in the Eastern Anatolia region, respectively. Furthermore, a significant increasing trend was identified in annual ET data across all regions except Eastern Anatolia. Conversely, significant increasing trends were noted in annual PET data in Eastern Anatolia and the Aegean regions. Additionally, significant increasing trends were discerned in annual NDVI, EVI, and VCI data across all regions. Experiments revealed that the ET exhibited robust correlations with the NDVI (0.77), EVI (0.79) and VCI (0.81). Furthermore, the NDVI demonstrated strong correlations with EVI (0.99) and VCI (0.96).