Kyriacos Neocleous

Integrated use of GIS and remote sensing for monitoring landslides in transportation pavements: the case study of Paphos area in Cyprus

This study considers the impact of landslides on transportation pavements in rural road network of Cyprus using remote sensing and geographical information system (GIS) techniques. Landslides are considered to be one of the most extreme natural hazards worldwide, causing both human losses and severe damages to the transportation network. Risk assessment for monitoring a road network is based on the combination of the probability of landslides occurrence and the extent and severity of the resultant consequences should the disasters (landslides) occur. Factors that can trigger landslide episodes include proximity to active faults, geological formations, fracture zones, degree and high curvature of slopes, water conditions, etc. In this study, the reliability and vulnerability of a rural network are examined. Initially, landslide locations were identified from the interpretation of satellite images. Different geomorphological factors such as aspect, slope, distance from the watershed, lithology, distance from lineaments, topographic curvature, land use and vegetation regime derived from satellite images were selected and incorporated in GIS environment in order to develop a decision support and continuous landslide monitoring system of the area. These parameters were then used in the final landslide hazard assessment model based on the analytic hierarchy process method. The results indicated good correlation between classified high-hazard areas and field-confirmed slope failures. The CA Markov model was also used to predict the landslide hazard zonation map for 2020 and the possible future hazards for transportation pavements. The proposed methodology can be used for areas with similar physiographic conditions all over the Eastern Mediterranean region.

Damage assessment using advanced non-intrusive inspection methods: integration of space, UAV, GPR, and field spectroscopy

The predominant approach for conducting road condition surveys and analyses is still largely based on extensive field observations. However, visual assessment alone cannot identify the actual extent and severity of damage. New non-invasive and cost-effective non-destructive (NDT) remote sensing technologies can be used to monitor road pavements across their life cycle, including remotely sensed aerial and satellite visual and thermal image (AI) data, Unmanned Aerial Vehicles (UAVs), Spectroscopy and Ground Penetrating Radar (GRP). These non-contact techniques can be used to obtain surface and sub-surface information about damage in road pavements, including the crack depth, and in-depth structural failure. Thus, a smart and cost-effective methodology is required that integrates several of these non-destructive/ no-contact techniques for the damage assessment and monitoring at different levels. This paper presents an overview of how an integration of the above technologies can be used to conduct detailed road condition surveys. The proposed approach can also be used to predict the future needs for road maintenance; this information is proven to be valuable to a strategic decision making tools that optimizes maintenance based on resources and environmental issues.

Predicting the Shear Strength of RC Beams Without Stirrups Using Bayesian Neural Network

This paper presents the application of Bayesian learning to train a multi layer perceptron network on experimental test on Reinforced Concrete (RC) beams without stirrups failing in shear. The trained network was found to provide good estimate of shear strength when the input variables (i.e. shear parameters) are within the range in the experimental database used for training. Within the Bayesian framework, a process known as the Automatic Relevance Determination is employed to assess the relative importance of different input variables on the output (i.e. shear strength). Finally the network is utilised to simulate typical RC beams failing in shear. Advances in neural computing have shown that a neural learning approach that uses Bayesian inference can essentially eliminate the problem of over fitting, which is common with conventional back propagation Neural Networks. In addition, Bayesian Neural Network can provide the confidence (error) associated with its prediction.

Seismic fragility assessment of existing sub-standard low strength reinforced concrete structures

An analytical seismic fragility assessment framework is presented for the existing low strength reinforced concrete structures more common in the building stock of the developing countries. For realistic modelling of such substandard structures, low strength concrete stress-strain and bond-slip capacity models are included in calibrating material models. Key capacity parameters are generated stochastically to produce building population and cyclic pushover analysis is carried out to capture inelastic behaviour. Secant period values are evaluated corresponding to each displacement step on the capacity curves and used as seismic demand. A modified capacity demand diagram method is adopted for the degrading structures, which is further used to evaluate peak ground acceleration from back analysis considering each point on the capacity curve as performance point. For developing fragility curves, the mean values of peak ground acceleration are evaluated corresponding to each performance point on the series of capacity curves. A suitable probability distribution function is adopted for the secant period scatter at different mean peak ground acceleration values and probability of exceedance of limit states is evaluated. A suitable regression function is used for developing fragility curves and regression coefficients are proposed for different confidence levels. Fragility curves are presented for a low rise pre-seismic code reinforced concrete structure typical of developing countries.

Risk provision using field spectroscopy to identify spectral regions for the detection of defects in flexible pavements

Natural and physical hazards accelerate the deterioration of asphalted surfaces. Climatic factors are unavoidable and can affect the properties of asphalt mixtures, making them weaker and less durable. Thus, continuous monitoring of bituminous surfaces is something that can reduce the risks of public health. Remote sensing techniques have become an effective, noninvasive method for early detection of damaged asphalt pavements. This paper outlines a range of different remote sensing methodologies that can be used to monitor asphalt road pavements. This is complemented by the use of field spectroscopy for the examination of asphalt pavements of varying age and conditions. The results of the study found spectral differences regarding asphalt defects, such as physical cracking, patched cracking and polishing. These spectral changes were examined through “in-band” simulation analysis of the Landsat 7 ETM+ sensor, using appropriate relative spectral response filters, concluding that the ratio band 5/band 1 can be used to distinguish asphalt pavements of different date of construction and condition.

Use of GIS for the development of digital structural integrity maps of high risk areas in Cyprus due to corrosion of steel reinforcement

Concrete reinforced with steel rebars remains one of the most widely used construction materials. Despite its good mechanical performance and expected service life of at least 50 years, reinforced concrete is subjected to corrosion of the steel rebars which normally leads to concrete spalling, deterioration of the reinforced concrete’s (RC) mechanical properties and eventual reduction of the structural load capacity. In Cyprus, especially in coastal regions where almost 60% of the population resides, many structural problems have been identified in RC structures, which are caused by the severe corrosion of steel rebars. Most RC, located in coastal areas, show signs of corrosion within the first 15-20 years of their service life and this affects their structural integrity and reliability, especially against seismic loading. This paper presents the research undertaken as part of the ‘STEELCOR’ project which aims to extensively evaluate the steel corrosion of RC buildings in coastal areas of Cyprus and conduct a risk assessment relating to steel corrosion. GIS are utilized in conjunction with non-destructive testing of corroded RC structures to develop a digital structural integrity map of Cyprus which shows the areas with high risk of steel corrosion of RC buildings.

Predicting the shear resistance of RC beams without shear reinforcement using a Bayesian neural network

Advances in neural computing have shown that a neural learning approach that uses Bayesian inference can essentially eliminate the problem of over fitting, which is common with conventional back-propagation neural networks. In addition, Bayesian neural network can provide the confidence (error) associated with its prediction. This paper presents the application of Bayesian learning to train a multilayer perceptron network to predict the shear resistance of reinforced concrete beams without shear reinforcement. The automatic relevance determination technique was employed to assess the relative importance of the different input variables considered in this study on the shear resistance of reinforced concrete beams. The performance of the Bayesian neural network is examined and discussed along with that of current shear design provisions.

Capitalize on the Experience of the ATHENA Project for Cultural Heritage for the Eratosthenes Centre of Excellence for the Benefit of the East Med Region

The “ATHENA” H2020 Twinning project seeks to establish a Center of Excellence in the field of Remote Sensing for Cultural Heritage through the development of an enhanced knowledge base and innovative methods in the areas of Archaeology and Cultural Heritage. This paper presents an overview of the ATHENA twinning project as well a review of the remote sensing in archaeology. The ATHENA stakeholder hub is presented through a WEBGIS platform. The importance of capitalizing on the experience of running the ATHENA project for the benefit of the ERATOSTHENES Centre of Excellence (ECoE) is explained. In recent years, Earth Observation (EO) techniques have been used extensively for archaeological and cultural heritage applications, which makes the ECoE a key player in EO activities in the Eastern Meditteranean region. The different areas that are under the umbrella of the remote sensing in archaeology sector are categorized based on the review findings. Finally, how Earth observation and remote sensing is spread out through research activities in the Eastern Meditteranean region from 1998 to 2018 is presented based on the Scopus engine.

On the Pathway to Success: Becoming a Leading Earth Observation Centre Through the EXCELSIOR Project

This paper presents the pathway towards the establishment of the ERATOSTHENES Centre of Excellence (ECoE), through the upgrade of the existing Remote Sensing & Geo-Environment Group - ERATOSTHENES Research Centre (ERC), within the Cyprus University of Technology (CUT). The ECoE aspires to become a sustainable, viable and autonomous Centre of Excellence for Earth Surveillance and Space-Based Monitoring of the Environment. The ECoE will provide the highest quality of related services in the National, European, Eastern Mediterranean and Middle East and Northern Africa areas (EMMENA). Therefore, drawing on the capitalization of experience and knowledge from previous projects and the research areas and international networks of the ERC, this papers highlights the importance of the establishment of the ECoE in the EMMENA area.

ERATOSTHENES: excellence research Centre for Earth surveillance and space-based monitoring of the environment, the EXCELSIOR Horizon 2020 teaming project

The aim of this paper is to present the strategy and vision to upgrade the existing ERATOSTHENES Research Centre (ERC) established within the Cyprus University of Technology (CUT) into a sustainable, viable and autonomous Centre of Excellence (CoE) for Earth Surveillance and Space-Based Monitoring of the Environment, which will provide the highest quality of related services on the National, European and International levels. EXCELSIOR is a Horizon 2020 Teaming project which addresses a specific challenge defined by the work program, namely, the reduction of substantial disparities in the European Union by supporting research and innovation activities and systems in low performing countries. It also aims at establishing long-term and strategic partnerships between the Teaming partners, thus reducing internal research and innovation disparities within European Research and Innovation landscape. The proposed CoE envisions the upgrading of the existing ERC into an inspiring environment for conducting basic and applied research and innovation in the areas of the integrated use of remote sensing and space-based techniques for monitoring the environment. Environment has been recognized by the Smart Specialization Strategy of Cyprus as the first horizontal priority for future growth of the island. The foreseen upgrade will regard the expansion of this vision to systematic monitoring of the environment using Earth Observation, space and ground based integrated technologies. Such an approach will lead to the systematic monitoring of all three domains of the Environment (Air, Land, Water). Five partners have united to upgrade the existing ERC into a CoE, with the common vision to become a world-class innovation, research and education centre, actively contributing to the European Research Area (ERA). More specifically, the Teaming project is a team effort between the Cyprus University of Technology (CUT, acting as the coordinator), the German Aerospace Centre (DLR), the National Observatory of Athens (NOA), the German Leibniz Institute for Tropospheric Research (TROPOS) and the Cyprus Department of Electronic Communications of the Ministry of Transport, Communications and Works (DEC-MTCW).