Condition Index Research Articles

Facility Condition Index 2.0 : A tool to (re)assess our portfolios in the light of the EU green deal?

Investments in capital expenditures (CapEx) for corporate real estate (CRE) assets, especially in a manufacturing environment, are always highly competing against investments in production and core business-related equipment and projects. The maximum amount of acceptable CapEx to the organisation seems to be only determined by the previous year’s amount of depreciation, completely independent from the actual need — and health — of the portfolio. This paper recaps the well-established concept of the Facility Condition Index (FCI)1 — a tool that shows the amount of needed building repairs in relation to the costs of replacing the building, to give a snapshot of deferred maintenance and general condition. It explains the concept through a multi-year case study of (light) industrial assets in freehold and triple net (NNN) lease environments. It also touches on new (reporting) requirements resulting from the European Union (EU) Green Deal2 with relevance to real estate and a potential revision of this indexing methodology.

SMART quality control analysis of pavement condition data for pavement management applications

Assuring the accuracy and reliability of pavement condition data is crucial for effective decision-making in pavement management. Despite existing data collection protocols, concerns persist regarding data quality. This paper introduces SMART, a systematic statistical method designed to analyze the quality of pavement condition data from field surveys for pavement management applications. SMART employs a structured procedure that expands traditional descriptive statistics by applying interrater reliability statistics combined with bootstrapping methods and Modified Blant Altman diagrams to evaluate data quality. A comparative analysis of interrater statistics, including Cohen’s Kappa (CK), Interclass Correlation (IC), Krippendorff’s Alpha (KA), Percent Agreement (PA), and Weighted Cohen’s Kappa (WCK), is conducted in the research study. As a result, the adoption of KA and Modified Bland-Altman diagrams for data analysis is recommended. KA demonstrates versatility across diverse data types, accommodating nominal, ordinal, interval, and ratio-level data, while Modified Bland-Altman diagrams facilitate data dispersion analysis to visualize possible bias trends for the condition ratings. A case study is presented to demonstrate the applicability of SMART to analyzing Pavement Condition Index (PCI) data provided by the Metropolitan Transportation Commission (MTC) in California. This methodological approach aims to enhance pavement management decisions by ensuring the reliability of condition field survey data through the implementation of robust analytical quality control procedures.

Evaluation of Municipal Solid Waste Management in Brazilian Cities According to the Updated Waste Management Condition Index –ICGRA

Evaluation of Municipal Solid Waste Management in Brazilian Cities According to the Updated Waste Management Condition Index –ICGRA

Automated Multi-Type Pavement Distress Segmentation and Quantification Using Transformer Networks for Pavement Condition Index Prediction

Automated Multi-Type Pavement Distress Segmentation and Quantification Using Transformer Networks for Pavement Condition Index Prediction

Marine aquaculture spatial planning on market orientation for Pacific oyster in Shandong, China

Accessing the production value is essential to the selection of suitable sites for aquaculture farms. This study employed a Dynamic Energy Budget (DEB) model and used market-oriented indicators, including shell length, flesh weight, condition index and minimum farming duration, to analyze the suitability for Pacific oyster aquaculture in offshore Shandong, China. Chlorophyll a (Chl-a) concentration, Total Suspended Sediment (TSS) concentration, and Sea Surface Temperature (SST) were extracted by remote sensing and used as forcing variables in the bioenergetic model. Four market-oriented indicators and five zones in the study area were chosen to develop a Suitable Market-Aquaculture site-selection model (SMASM) and estimate the oyster farming suitability. The results demonstrated that Sanggou Bay exhibits high potential for Pacific oyster year-round aquaculture. Eastern Laizhou Bay is suitable for the autumn-spring fattening culture of adult oysters. Laoshan Bay and Changshan Islands are suitable for promoting rapid growth in juvenile oysters. The results of our model were consistent with the oyster production value in Shandong, showing an overall increasing trend except for the anomalous value observed in 2019. Sudden drops in water temperature, red tides, and significant increases in turbidity can exert detrimental effects on oyster production, which has been evidenced to be influenced by Super Typhoon Lekima in 2019. Although this study concentrated on a specific geographical region, the model could be applied to other regions to develop market-oriented suitability management for species and environments worldwide.

Demographic insights for the endangered Apennine yellow-bellied toad in the Pollino National Park (Southern Italy). A five-year Capture-Mark-Recapture study

Investigating population structure, parameters, and growth over time is vital for conserving endangered taxa like the Apennine yellow-bellied toad (Bombina variegata pachypus) in Italy. The Apennine yellow-bellied toad faces habitat loss or degradation, disease, and climate change threats. Our study delves into the population dynamics of this endemic amphibian by monitoring populations in two breeding sites within the Pollino National Park (Southern Italy) using a five-year Capture-Mark-Recapture (CMR) approach. With a particular emphasis on demographic parameters such as population size, detectability, survival, temporary emigration probability, and body condition index, the research aims to contribute to understanding population dynamics and inform conservation efforts. We used two CMR models to estimate these parameters: Jolly-Seber with POPAN parametrisation and Pollock's robust design with Huggins' estimator. Results showed variation in body size between males and females, challenging previous findings in south-central Italy. For both populations, demographic data indicate high annual adult survival, fidelity to the breeding areas, support for no movement, and minimal trap response. Abundance over the years reveals a slight decline in one site and a gradual increase in the other. The estimated maximum population size indicated ∼ 92 animals in one population and ∼ 70 in the other one. Despite the high conservation concern for the Apennine yellow-bellied toad, there remains a dearth of quantitative information on demographic aspects in Southern Italy. The findings underscore the importance of protected areas in biodiversity conservation and contribute to understanding the B. v. pachypus conservation status, suggesting that small, isolated populations may be vulnerable to unpredictable fluctuations, inbreeding depression, and extinction risks. Additionally, the research emphasises the importance of long-term CMR studies to gain insights into population dynamics and develop effective conservation strategies and management practices, particularly in the face of climate changes that may impact both the endemic taxon and its habitat.

Trace Elements in Stenella coeruleoalba: Assessment of Marine Environmental Pollution and Dolphin Health Status.

Heavy metals are environmental contaminants and can easily accumulate and biomagnify in various marine species (fishes and mammalians) at the top of the aquatic food chain. Among marine mammalians, the striped dolphin (Stenella coeruleoalba) is the most abundant cetacean in the Mediterranean Sea and is considered to be a sentinel species to monitor the environmental marine pollution. In this study, the contents of toxic metals and metalloids (Cd, Pb, Hg, and As), micro-elements (Ni, Cr, Cu, Fe, Co, Mn, Se and Zn) and macro-elements (Na, Ca, K, Mg and P) were evaluated by ICP-MS analysis in several organs/tissues (lung, skin, muscle and liver) of Stenella coeruleoalba. The assessment of marine environmental pollution and dolphins health status was carried out through further analysis of the same specific parameters such as the metal pollution index (MPI) and coefficient of condition (K). Finally, the correlation between toxic metals and metalloids and essential micro-elements, expressed as molar ratios, was analyzed to evaluate the detoxifying ability (effectiveness) of Zn, Se and Cu. Data obtained showed the presence of toxic metals and metalloids analyzed in the Stenella coeruleoalba samples but the MPI values suggested a low environmental contamination of the Mediterranean Sea where dolphins lived. The content of micro- and macro-elements was found to be in a normal range for this species and predictive of dolphins good health status, as confirmed by the coefficient of condition K. However, the correlation between toxic and essential metals, expressed as molar ratios, showed that the following toxic metals cannot be detoxified by the essential metals: 66Zn/201Hg, 82Se/201Hg, 63Cu/201Hg and 66Zn/52Cr, 82Se/52Cr, 63Cu/52Cr. Therefore, this study highlights the key role of dolphin Stenella coeruleoalba to assess marine pollution and the importance of analyzing the complete mineral profile to evaluate the animal health status.

Annual Reproductive Phenology and Condition Index of Blood Cockle Tegillarca granosa (L., 1758) Collected from the West Coast of Moheshkhali Island, Cox’s Bazar, Bangladesh

Annual Reproductive Phenology and Condition Index of Blood Cockle Tegillarca granosa (L., 1758) Collected from the West Coast of Moheshkhali Island, Cox’s Bazar, Bangladesh

Ensemble Machine Learning Classification Models for Predicting Pavement Condition

Forecasting pavement performance condition is essential within the pavement management system to optimize decisions with regard to planning maintenance and rehabilitation projects. Accurate forecasts facilitate timely interventions and assist in formulating cost-effective asset management plans. Data-driven machine learning models that utilize historical data to improve forecasting precision have gained attention in the field of asset management. Although numerous studies have employed regression-based models to forecast pavement condition, transportation asset management often operates according to condition index ranges rather than exact values. Therefore, classification models are suitable for predicting pavement condition grades and determining the appropriate maintenance type for pavement assets. This research focuses on developing five machine learning classification models to predict pavement condition: random forest; gradient boost; support vector machine; k-nearest neighbors; and artificial neural network. To enhance prediction performance, these models are integrated using ensemble methods, including voting and stacking. The classification models are developed using a dataset from the Georgia Department of Transportation that documented the condition of asphalt pavements for predefined maintenance sections between 2017 and 2021. A voting ensemble model constructed with the two best-performing individual classification models reached the highest accuracy rate at 83%. Although the performance of individual models fluctuates, ensemble models consistently produce a top-tier performance regardless of the variations in data sampling. Therefore, ensemble methods are recommended for developing pavement condition prediction models to improve accuracy and achieve a more consistent quality of predictions. The findings of this research will provide transportation agencies with information to help them strengthen their forecasting practices in relation to pavement condition, thereby improving their maintenance planning and cost savings.

Redistribution of songbirds within a migratory stopover site as a response to sylviid warbler song playback

Assessing habitat quality is critical for migrating birds, with implications for fuel loading, predation risk and timing of departure. To efficiently distribute within stopover sites, migrants rely on various cues that facilitate habitat quality assessment. In this study, we conducted a playback experiment at a mid‐route stopover site to test the effects of vocal cues, specifically bird songs, on habitat redistribution during spring migration. To estimate the effect of bird songs on bird abundance we mist‐netted and ringed birds for 60 days on alternating playback treatment (playing songs of three sylviid warbler species) and no playback (control) days. A nearby site was used to control for fluctuations caused by migration waves. Overall, total bird abundance was significantly increased by the playback treatment. This increase resulted from the attraction of conspecifics (species whose songs were played) rather than of other species – sylviids or others. Correspondingly, species richness was seemingly unaffected. To investigate the relationship between internal‐state factors and the attraction to playback in the most abundantly attracted species, Eurasian Blackcap Sylvia atricapilla, we tested associations with age, sex and body condition index but found no significant effects, possibly because of the limited sample size. Our findings challenge established ideas regarding heterospecific attraction and contradict previous studies. Attraction to bird song is discussed in the context of geographical location, timing and internal drivers. Vocal cues, specifically bird songs, may affect micro‐habitat selection by migrating songbirds during stopovers. Consequently, we call for consideration of the influence of the acoustic environment on birds during migratory stopover in future studies, habitat management and conservation efforts.

Selection indices for high seed yield in desi chickpea (Cicer arietinum L.) genotypes under terminal heat stress

Seventy-one genotypes of desi chickpea including released varieties were evaluated over two years under normal and late conditions. The selection index based on discriminant function analysis was used to determine the phenotypic value of different characters in selection. Seed yield and its five contributing traits were used to construct the index under normal and late-sown conditions. The results indicated that the efficiency of the index increased with the inclusion of a greater number of traits. The best selection index for the normal sown condition was observed for three characters including date of maturity, reproductive phase duration, and number of pods per plant in E1 as well as with days to 50% flowering, days to maturity, and number of pods per plant in E3 . On the other hand, under late sown conditions (E2 and E4 ), four characters seed yield per plant, reproductive phase duration, number of pods per plant, and 100-seed weight had the best selection index. This study showed that the selection of these characters may be considered for better genetic gain for higher seed yield under any of these conditions.

EVALUATION OF THE LEVEL OF ROAD DAMAGE IN JELIDRO II-KUWUKAN-SAMBIKEREP ROADS USING THE PAVEMENT CONDITION INDEX (PCI) AND BINA MARGA METHOD

The road is a facility for land transportation that serves regional development. The deterioration of road quality can pose constraints on economic and social activities and endanger road users. Small damages can escalate if not promptly addressed, leading to losses. This reasearch aims to determine the type and level of damage on Jelidro II – Kuwukan - Sambikerep Roads STA 0+000 to STA 1+600. There are two methods for obtaining road condition values in this research, the methods are Pavement Condition Index method and the Bina Marga method. On the section of Jelidro II – Kuwukan - Sambikerep Roads, 8 types of damage were found, those are crocodile skin cracks, grade depression, edge cracks, potholes, patches, longitudinal cracks, slip cracks, and ravelling. The results of calculating the road condition value using the PCI method is 63,6 where the road is included in the good category. Calculations using the Bina Marga method get a priority order of 9. The two methods show that the road has the same type of treatment, that is a routine maintenance program with repair methods that can be carried out, namely surface layers such as asphalt resurfacing and patching holes. Keywords: Road Damage; PCI method; Bina Marga method.

Enhancing a machine learning model for predicting agricultural drought through feature selection techniques

This study aims to determine the crucial variables for predicting agricultural drought in various climates of Iran by employing feature selection methods. To achieve this, two databases were used, one consisting of ground-based measurements and the other containing six reanalysis products for temperature (T), root zone soil moisture (SM), potential evapotranspiration (PET), and precipitation (P) variables during the 1987–2019 period. The accuracy of the global database data was assessed using statistical criteria in both single- and multi-product approaches for the aforementioned four variables. In addition, five different feature selection methods were employed to select the best single condition indices (SCIs) as input for the support vector regression (SVR) model. The superior multi-products based on time series (SMT) showed increased accuracy for P, T, PET, and SM variables, with an average 47%, 41%, 42%, and 52% reduction in mean absolute error compared to SSP. In hyperarid climate regions, PET condition index was found to have high relative importance with 40% and 36% contributions to SPEI-3 and SPEI-6, respectively. This suggests that PET plays a key role in agricultural drought in hyperarid regions because of very low precipitation. Additionally, the accuracy results of different feature selection methods show that ReliefF outperformed other feature selection methods in agricultural drought modeling. The characteristics of agricultural drought indicate the occurrence of drought in 2017 and 2018 in various climates in Iran, particularly arid and semi-arid climates, with five instances and an average duration of 12 months of drought in humid climates.

Isotropic compact stars admitting Heintzmann solution in Rastall gravity

This paper is devoted to observe the physical attributes of static spherically symmetric isotropic compact stellar candidates in the context of Rastall theory of gravity. In order to inspect the structural composition of compact objects, the Heintzmann ansatz is taken into account. The unknown parameters associated with Heintzmann ansatz are evaluated through matching conditions with derived values of radii and masses of some specific star models. The consistency of the adopted ansatz is analyzed via graphical interpretation of matter variables, equation of state parameter, mass components, energy constraints, causality condition, stellar equation and adiabatic index for several choices of Rastall parameter. It is observed that the stars under consideration manifest stable structures corresponding to Heintzmann ansatz in this framework. Moreover, it is shown that for vanishing Rastall coupling parameter, the standard outcomes of general relativity can be retrieved.

Nation-wide surveillance of ticks (Acari: Argasidae) on bats (Chiroptera) in Singapore

Bats and ticks are important sources of zoonotic pathogens. Therefore, understanding the diversity, distribution, and ecology of both groups is crucial for public health preparedness. Soft ticks (Argasidae) are a major group of ectoparasites commonly associated with bats. The multi-host life cycle of many argasids make them important vectors of pathogens. Over nine years (2011–2020), surveillance was undertaken to identify the ticks associated with common bats in Singapore. During this period, the bat tick Ornithodoros batuensis was detected within populations of two cave roosting bat species: Eonycteris spelaea and Penthetor lucasi. We examined the relationship between bat species, roosting behaviour, and probability of O. batuensis infestation. We also estimated the relationship between bat life history variables (body condition index, sex, and age) on the probability of infestation and tick count. This represents the first detection of O. batuensis and the genus Ornithodoros within Singapore. We also provide evidence of the continued persistence of Argas pusillus in Singapore with the second local record.

Assessment of dental and periodontal indices and Streptococcus mutans virulence in fragile X syndrome patients.

Fragile X syndrome (FXS) is the most common cause of hereditary genetic disorder in a single gene characterised by intellectual disability. Behavioural features such as autism, hyperactivity and anxiety disorder may be present. Biofilm development and pathogenicity of Streptococcus mutans may be altered because FXS renders the dental approach and oral hygiene more complex. The purpose of this study was to compare the levels of transcripts for VicRK and CovR of S. mutans isolated from FXS patients with the levels of transcripts for VicRK and CovR of standard strain ATCC, using a quantitative polymerase chain reaction (qPCR). The caries experience index was assessed by the International Caries Detection and Assessment System (ICDAS), Periodontal Condition Index (PCI) and Invasive Dental Treatment Need Index (INI). The clinical index findings revealed a high rate of caries cavities and bleeding on probing of FXS patients. When VicRK and CovR transcript levels were compared with the reference strain, Fragile X patients were found to have significantly higher values. The present study demonstrated that FXS patients have more adverse clinical conditions, with increased biofilm accumulation and virulence. When combined with behavioural abnormalities, these patients become even more vulnerable to dental caries.

Using marine mussels to assess the potential ecotoxicological effects of two different commercial microplastics

Microplastics (MPs) in the aquatic environment pose a serious threat to biota, by being confounded with food. These effects occur in mussels which are filter-feeding organisms. Mussels from the genus Mytilus sp. were used to evaluate the ecotoxicological effects of two MPs, polypropylene (PP) and polyethylene terephthalate (PET), after 4 and 28-days. Measured individual endpoints were condition index and feeding rate; and sub-individual parameters, metabolism of phase I (CYP1A1, CYP1A2 and CYP3A4) and II (glutathione S-transferases - GSTs), and antioxidant defense (catalase - CAT). MPs decreased both condition index (CI) and feeding rate (FR). No alterations occurred in metabolic enzymes, suggesting that these MPs are not metabolized by these pathways. Furthermore, lack of alterations in GSTs and CAT activities suggests the absence of conjugation and oxidative stress. Overall, biochemical markers were not responsive, but non-enzymatic responses showed deleterious effects caused by these MPs, which may be of high ecological importance.

Spatial-Temporal Assessment of Drought in Hoa Vang district, Da Nang City, Vietnam Using Remote Sensing and Google Earth Engine

The objective of this research was to map the drought risk in Hoa Vang district, Da Nang city, and analyze the variability of drought events from 1991 to 2020 using remote sensing imagery. To achieve this, remote sensing and GIS methods were utilized to gather information on land surface temperature and the Normalized Difference Vegetation Index (NDVI). The study effectively employed the Google Earth Engine which is a cloud-based computing platform to analyze image sequences from Landsat 5 and OLI sensors spanning the period from 1991 to 2020. This analysis aimed to extract the remote sensing indices and applied them for drought assessment and monitoring. The study employed land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) to derive the Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI). These indices were utilized to assess drought conditions in Hoa Vang district, Da Nang city, over the period from 1991 to 2020. The research findings reveal that the drought values in Hoa Vang district exhibited variations throughout the studied period. Notably, regions at risk of drought during the later periods exhibited an increasing trend compared to the initial periods. This suggests a potential escalation in drought severity in the study area over time. The outcomes of this study provide essential background for evaluating the impacts of drought on agricultural production in Hoa Vang district, Da Nang city.

Preventive Maintenance Decision-Making Optimization Method for Airport Runway Composite Pavements

Long-term preventive maintenance planning using finite annual budgets is vital for maintaining the service performance of airport runway composite pavements. Using the pavement condition index (PCI) to quantify composite pavement performance, this study investigated the PCI deterioration tendencies of middle runways, terminal runways, and taxiways and developed prediction models related to structural thickness and air traffic. Performance jump (PJ) and deterioration rate reduction (DRR) were used to measure maintenance benefits. Based on 112 composite pavement sections in the Long-term Pavement Performance Program, this study analyzed the influences of five typical preventive maintenance technologies on PJ, DRR, and PCI deterioration rates. The logarithmic regression relationship between PJ and PCI was obtained. For sections treated with crack sealing and crack filling, the DRR was nearly 0. For sections treated with fog seal, thin HMA overlay, and hot-mix recycled AC, the DRR was 0.2, 0.7, and 0.8, respectively. To solve the multi-objective maintenance problem, this study proposed a decision-making optimization method based on dynamic programming, and the solution algorithm was optimized, which was applied in a five-year maintenance plan. Considering different PCI deterioration tendencies of airport regions, as well as PJ, DRR, and costs of maintenance technologies, the preventive maintenance decision-making optimization method meets performance and financial requirements sufficiently.

The Use of Multi-Criteria Parameters for Bridge Maintenance Prioritization and Maintenance Categorization Options in Tanzania

A bridge is a structure across physical obstacles like valleys, rivers, seas, oceans, lakes, roads, and railways. Its components get damaged when exposed to harsh conditions resulting from various causes, which reduces its lifetime. Maintenance is necessary to prolong its remaining service life once it gets damaged. Successful maintenance depends on various factors, including the availability of funds, the effectiveness of condition assessment, and other damaging factors. Many developing countries face a scarcity of financial resources to restore the functions of bridges at network levels. In order to have a better plan for financial resources, it is important to devise a methodology for prioritising bridge maintenance at the network level. This research used parameter ratings to determine the Bridge Prioritisation Index (BPI) derived from selected parameters affecting bridge performance. The parameters considered for this study are bridge defects, earthquakes, traffic actions, and scouring potential. The prioritisation indices were determined from four bridges, which are Kikwete, Mvomero, Unkuku, and Nyahua. The bridge ratings from bridge condition indices were determined to be 2 for Unkuku and Mvomero bridges and 1 for Kikwete and Nyahua bridges. Scour ratings from scour indices were determined to be 4 for the Unkuku, Nyahua, and Mvomero bridges and 3 for the Kikwete bridge. Seismic ratings from seismic indices were determined to be 1 for all four bridges, and traffic ratings from traffic indices were determined to be 3 for Unkuku and Nyahua bridges and 2 for Mvomero and Kikwete bridges. Bridge Prioritisation Indices (BPI) determined indicate that Unkuku Bridge has higher priorities in maintenance than other bridges, with a BPI of 2.19, followed by Mvomero Bridge with a BPI of 2.08, Nyahua Bridge with a BPI of 1.67, and lastly, Kikwete Bridge with a BPI of 1.41. Based on the obtained BPI, the maintenance action required was proposed to be preventive maintenance for the Unkuku, Nyahua, and Mvomero bridges and routine maintenance for the Kikwete bridge. Further studies are proposed to include the earthquake amplitude in bridge maintenance prioritisation.