Automated Pavement Condition Index Assessment with Deep Learning and Image Analysis: An End-to-End Approach

The degradation of road pavements due to environmental factors is a pressing issue in infrastructure maintenance, necessitating precise identification of pavement distresses. The pavement condition index (PCI) serves as a critical metric for evaluating pavement conditions, essential for effective budget allocation and performance tracking. Traditional manual PCI assessment methods are limited by labor intensity, subjectivity, and susceptibility to human error. Addressing these challenges, this paper presents a novel, end-to-end automated method for PCI calculation, integrating deep learning and image processing technologies. The first stage employs a deep learning algorithm for accurate detection of pavement cracks, followed by the application of a segmentation-based skeleton algorithm in image processing to estimate crack width precisely. This integrated approach enhances the assessment process, providing a more comprehensive evaluation of pavement integrity. The validation results demonstrate a 95% accuracy in crack detection and 90% accuracy in crack width estimation. Leveraging these results, the automated PCI rating is achieved, aligned with standards, showcasing significant improvements in the efficiency and reliability of PCI evaluations. This method offers advancements in pavement maintenance strategies and potential applications in broader road infrastructure management.

Prediction Models for Evaluating Resilient Modulus of Stabilized Aggregate Bases in Wet and Dry Alternating Environments: ANN and GEP Approaches

Stabilized aggregate bases are vital for the long-term service life of pavements. Their stiffness is comparatively higher; therefore, the inclusion of stabilized materials in the construction of bases prevents the cracking of the asphalt layer. The effect of wet−dry cycles (WDCs) on the resilient modulus (Mr) of subgrade materials stabilized with CaO and cementitious materials, modelled using artificial neural network (ANN) and gene expression programming (GEP) has been studied here. For this purpose, a number of wet−dry cycles (WDC), calcium oxide to SAF (silica, alumina, and ferric oxide compounds in the cementitious materials) ratio (CSAFRs), ratio of maximum dry density to the optimum moisture content (DMR), confining pressure (σ3), and deviator stress (σ4) were considered input variables, and Mr was treated as the target variable. Different ANN and GEP prediction models were developed, validated, and tested using 30% of the experimental data. Additionally, they were evaluated using statistical indices, such as the slope of the regression line between experimental and predicted results and the relative error analysis. The slope of the regression line for the ANN and GEP models was observed as (0.96, 0.99, and 0.94) and (0.72, 0.72, and 0.76) for the training, validation, and test data, respectively. The parametric analysis of the ANN and GEP models showed that Mr increased with the DMR, σ3, and σ4. An increase in the number of WDCs reduced the Mr value. The sensitivity analysis showed the sequences of importance as: DMR > CSAFR > WDC > σ4 > σ3, (ANN model) and DMR > WDC > CSAFR > σ4 > σ3 (GEP model). Both the ANN and GEP models reflected close agreement between experimental and predicted results; however, the ANN model depicted superior accuracy in predicting the Mr value.

The role of concrete in life cycle greenhouse gas emissions of US buildings and pavements

Concrete is a critical component of deep decarbonization efforts because of both the scale of the industry and because of how its use impacts the building, transportation, and industrial sectors. We use a bottom-up model of current and future building and pavement stocks and construction in the United States to contextualize the role of concrete in greenhouse gas (GHG) reductions strategies under projected and ambitious scenarios, including embodied and use phases of the structures' life cycle. We show that projected improvements in the building sector result in a reduction of 49% of GHG emissions in 2050 relative to 2016 levels, whereas ambitious improvements result in a 57% reduction in 2050, which is 22.5 Gt cumulative saving. The pavements sector shows a larger difference between the two scenarios with a 14% reduction of GHG emissions for projected improvements and a 65% reduction under the ambitious scenario, which is ∼1.35 Gt. This reduction occurs despite the fact that concrete usage in 2050 in the ambitious scenario is over three times that of the projected scenario because of the ways in which concrete lowers use phase emissions. Over 70% of future emissions from new construction are from the use phase.

A Novel Concrete-Based Sensor for Detection of Ice and Water on Roads and Bridges

Hundreds of people are killed or injured annually in the United States in accidents related to ice formation on roadways and bridge decks. In this paper, a novel embedded sensor system is proposed for the detection of black ice as well as wet, dry, and frozen pavement conditions on roads, runways, and bridges. The proposed sensor works by detecting changes in electrical resistance between two sets of stainless steel poles embedded in the concrete sensor to assess surface and near-surface conditions. A preliminary decision algorithm is developed that utilizes sensor outputs indicating resistance changes and surface temperature. The sensor consists of a 102-mm-diameter, 38-mm-high, concrete cylinder. Laboratory results indicate that the proposed sensor can effectively detect surface ice and wet conditions even in the presence of deicing chlorides and rubber residue. This sensor can further distinguish black ice from ice that may exist within concrete pores.

Assessment of Environmental Determinants of Physical Activity: a Study of Built Environment Indicators in Brno, Czech Republic

BACKGROUND

Research on physical activity in relation to obesity gradually becomes more focused on environmental determinants, which can potentially influence people's health choices. The present article addresses the topic of physical activity from a wider sociological perspective. Our pilot study was designed with the objective of testing the applicability of a method included in the EC 6th Framework Programme EURO-PREVOB, in the Czech context. The method examines specific determinants of the built environment that can have an impact on physical activity at the population level. In addition, the study aims to analyze possible differences in built environment indicators and their relation to the physical activity of people living in neighbourhoods with areas of varying socioeconomic status.

METHODS

The field study was carried out in the city of Brno, Czech Republic, in 5 neighbourhood quintiles, i.e. areas divided according to the socioeconomic status of local residents. In each quintile, we evaluated the quality of the built environment according to the quality, aesthetics and safety of segregated cycle facilities, playgrounds/playing areas, public open spaces, marked road crossings and pavements as well as signs of incivilities and devastation.

RESULTS

Between the five quintiles, significant differences were found in the quality of parks and playgrounds/playing areas, pavements, marking of pedestrian crossings, and in general aesthetics, i.e. signs of incivilities and devastation of the built environment. No differences were found in the quality and use of cycle facilities.

CONCLUSIONS

The method we used for the evaluation of the built environment proved highly applicable in Czech populated areas. Monitoring of built environment indicators in the Czech Republic should provide a basis for health maps, showing potential associations between the prevalence of high-incidence, non-infectious diseases and various social determinants of physical activity. This information might help in achieving an improvement in these determinants at a community level and promoting an increase in physical activity at the population level.