Vikram Pakrashi

Perception of safety of cyclists in Dublin City

In recent years, cycling has been recognized and is being promoted as a sustainable mode of travel. The perception of cycling as an unsafe mode of travel is a significant obstacle in increasing the mode share of bicycles in a city. Hence, it is important to identify and analyze the factors which influence the safety experiences of the cyclists in an urban signalized multi-modal transportation network. Previous researches in the area of perceived safety of cyclists primarily considered the influence of network infrastructure and operation specific variables and are often limited to specific locations within the network. This study explores the factors that are expected to be important in influencing the perception of safety among cyclists but were never studied in the past. These factors include the safety behavior of existing cyclists, the users of other travel modes and their attitude toward cyclists, facilities and network infrastructures applicable to cycling as well as to other modes in all parts of an urban transportation network. A survey of existing cyclists in Dublin City was conducted to gain an insight into the different aspects related to the safety experience of cyclists. Ordered Logistic Regression (OLR) and Principal Component Analysis (PCA) were used in the analysis of survey responses. This study has revealed that respondents perceive cycling as less safe than driving in Dublin City. The new findings have shown that the compliance of cyclists with the rules of the road increase their safety experience, while the reckless and careless attitudes of drivers are exceptionally detrimental to their perceived safety. The policy implications of the results of analysis are discussed with the intention of building on the reputation of cycling as a viable mode of transportation among all network users.

A study on the effects of damage models and wavelet bases for damage identification and calibration in beams

Structural damage detection and calibration in beams by wavelet analysis involve some key factors such as the damage model, the choice of the wavelet function, the effects of windowing, and the effects of masking due to the presence of noise during measurement. In this research, a numerical study was performed to address these issues for single and multispan beams with an open crack. The first natural modeshapes of single and multispan beams with an open crack have been simulated taking into account damage models of different levels of complexity and analyzed for different crack depth ratios and crack positions. Gaussian white noise has been synthetically introduced to the simulated modeshape and the effects of varying signal-to-noise ratio are studied. A wavelet-based damage identification technique was found to be simple, efficient, and independent of damage models and wavelet basis functions, once certain conditions regarding the modeshape and the wavelet bases are satisfied. The wavelet-based damage calibration is found to be dependent on a number of factors including damage models and the basis function used in the analysis. A curvature-based calibration is more sensitive than a modeshape-based calibration of the extent of damage.

Texture Analysis Based Damage Detection of Ageing Infrastructural Elements

:  To make visual data a part of quantitative assessment for infrastructure maintenance management, it is important to develop computer-aided methods that demonstrate efficient performance in the presence of variability in damage forms, lighting conditions, viewing angles, and image resolutions taking into account the luminous and chromatic complexities of visual data. This article presents a semi-automatic, enhanced texture segmentation approach to detect and classify surface damage on infrastructure elements and successfully applies them to a range of images of surface damage. The approach involves statistical analysis of spatially neighboring pixels in various color spaces by defining a feature vector that includes measures related to pixel intensity values over a specified color range and statistics derived from the Grey Level Co-occurrence Matrix calculated on a quantized grey-level scale. Parameter optimized non-linear Support Vector Machines are used to classify the feature vector. A Custom-Weighted Iterative model and a 4-Dimensional Input Space model are introduced. Receiver Operating Characteristics are employed to assess and enhance the detection efficiency under various damage conditions.

Energy Harvesting from Train-Induced Response in Bridges

The integration of large infrastructure with energy-harvesting systems is a growing field with potentially new and important applications. The possibility of energy harvesting from ambient vibratio ...

A Bridge-Vehicle Interaction Based Experimental Investigation of Damage Evolution

This article presents an experimental monitoring of the evolution of a crack in a beam using beam-vehicle interaction response signals for identification of progressively increasing crack-depth ratios. The beam is traversed by a two-axle model vehicle providing excitation in the time domain for the various extents of damage. The response of the beam in the time domain during the period of forced vibration is measured using strain gages. A consistent evolution of damage has been demonstrated in terms of the maxima values of the measured responses. The corresponding distortions of wavelet coefficients of the measured strain data due to the presence of various levels of damage have been identified. The evolution of the phase space and the wavelet transformed phase spaces have been evaluated with damage evolution. The wavelet transformed phase spaces for the undamaged and the damaged cases are observed to be distinctly different at high scales. The importance of denoising of the acquired data and the importance of vehicle configuration has been illustrated. This study presents a basis for a general model free damage assessment and structural health monitoring framework. The study presented is particularly useful in the context of continuous online bridge health monitoring, since the data necessary for analysis can be obtained from the operating condition of the bridge and the structure does not need be closed down.

Quantifying the Health Impacts of Active Travel: Assessment of Methodologies

Abstract In the past several years, active travel (walking and cycling) has increasingly been recognized as an effective means of improving public health by increasing physical activity and by avoiding the negative externalities of motorized transport. The impacts of increased active travel on mortality and morbidity rates have been quantified through a range of methodologies. In this study, the existing publications in this field of research have been reviewed to compare and contrast the methodologies adapted and to identify the key considerations and the best practices. The publications were classified in terms of the health summary outcomes and exposure variables considered, the model structures used in the studies and the impact of these choices on the results. Increased physical activity was identified as the most important determinant of the health impacts of active travel but different ways of quantifying these health impacts can lead to substantial differences in the scale of the impact. Further research is required into the relationship between increased physical activity and health effects in order to reach consensus on the most reliable modelling approach for this important determinant of benefits. Critical discussions on other exposure variables have also been provided to ascertain best practices. Additionally, a logical flow of the modelling processes (and their variations) has also been illustrated which can be followed for developing future studies into the health impacts of active travel.

ROC dependent event isolation method for image processing based assessment of corroded harbour structures

The localisation and calibration of damage in a structure are often difficult, time consuming, subjective and error prone. The importance of a simple, fast and relatively inexpensive non-destructive technique (NDT) with reliable measurements is thus greatly felt. The usefulness and the efficiency of any such technique are often affected by environmental conditions. The definition of damage and the subsequent interpretation of the possible consequences due to the damage introduce subjectivity into an NDT technique and affect its performance. It is of great importance in terms of practical application to find out the efficiency of an NDT technique in a probabilistic way for various damage definitions and environmental conditions through the use of receiver operating characteristic (ROC) curves. Such variations of performance of an NDT tool can be predicted through simulation processes, and the test conditions conducive to good detections can be isolated and ranked according to their relative efficiency. This paper considers a camera based image analysis technique to identify, quantify and classify damage in structures at various levels of scale. The general method has been applied to identify the affected areas on aluminium due to pitting corrosion. The method depends on the optical contrast of the corroded region with respect to its surroundings, performs intelligent edge detection through image processing techniques and computes each affected and closed region to predict the total area of the affected part, together with its spatial distribution on a two-dimensional plane. The effects of various environmental factors on the quality of such images are simulated from an original photograph. The objectivity and the amount of available information, quantification and localisation and the extent of pitting corrosion are observed, together with the various constructed ROC curves. The method provides the engineer, the owner of the structure and the end-user of the NDT technique with a tool to assess the performance of the structure in an as-built condition and decide on the appropriateness of a certain NDT, under a given environmental condition and a certain definition of damage. Moreover, it allows the findings of the NDT results to be introduced in the decision chain and risk analysis.

Regionally Enhanced Multiphase Segmentation Technique for Damaged Surfaces

Imaging-based damage detection techniques are increasingly being utilized alongside traditional visual inspection methods to provide owners/operators of infrastructure with an efficient source of quantitative information for ensuring their continued safe and economic operation. However, there exists scope for significant development of improved damage detection algorithms that can characterize features of interest in challenging scenes with credibility. This article presents a new regionally enhanced multiphase segmentation (REMPS) technique that is designed to detect a broad range of damage forms on the surface of civil infrastructure. The technique is successfully applied to a corroding infrastructure component in a harbour facility. REMPS integrates spatial and pixel relationships to identify, classify, and quantify the area of damaged regions to a high degree of accuracy. The image of interest is preprocessed through a contrast enhancement and color reduction scheme. Features in the image are then identified using a Sobel edge detector, followed by subsequent classification using a clustering-based filtering technique. Finally, support vector machines are used to classify pixels which are locally supplemented onto damaged regions to improve their size and shape characteristics. The performance of REMPS in different color spaces is investigated for best detection on the basis of receiver operating characteristics curves. The superiority of REMPS over existing segmentation approaches is demonstrated, in particular when considering high dynamic range imagery. It is shown that REMPS easily extends beyond the application presented and may be considered an effective and versatile standalone segmentation technique.

Dynamic response mitigation of floating wind turbine platforms using tuned liquid column dampers

In this paper, we experimentally study and compare the effects of three combinations of multiple tuned liquid column dampers (MTLCDs) on the dynamic performance of a model floating tension-leg platform (TLP) structure in a wave basin. The structural stability and safety of the floating structure during operation and maintenance is of concern for the performance of a renewable energy device that it might be supporting. The dynamic responses of the structure should thus be limited for these renewable energy devices to perform as intended. This issue is particularly important during the operation of a TLP in extreme weather conditions. Tuned liquid column dampers (TLCDs) can use the power of sloshing water to reduce surge motions of a floating TLP exposed to wind and waves. This paper demonstrates the potential of MTLCDs in reducing dynamic responses of a scaled TLP model through an experimental study. The potential of using output-only statistical markers for monitoring changes in structural conditions is also investigated through the application of a delay vector variance (DVV) marker for different conditions of control for the experiments.

An energy aware adaptive sampling algorithm for energy harvesting WSN with energy hungry sensors

Wireless sensor nodes have a limited power budget, though they are often expected to be functional in the field once deployed for extended periods of time. Therefore, minimization of energy consumption and energy harvesting technology in Wireless Sensor Networks (WSN) are key tools for maximizing network lifetime, and achieving self-sustainability. This paper proposes an energy aware Adaptive Sampling Algorithm (ASA) for WSN with power hungry sensors and harvesting capabilities, an energy management technique that can be implemented on any WSN platform with enough processing power to execute the proposed algorithm. An existing state-of-the-art ASA developed for wireless sensor networks with power hungry sensors is optimized and enhanced to adapt the sampling frequency according to the available energy of the node. The proposed algorithm is evaluated using two in-field testbeds that are supplied by two different energy harvesting sources (solar and wind). Simulation and comparison between the state-of-the-art ASA and the proposed energy aware ASA (EASA) in terms of energy durability are carried out using in-field measured harvested energy (using both wind and solar sources) and power hungry sensors (ultrasonic wind sensor and gas sensors). The simulation results demonstrate that using ASA in combination with an energy aware function on the nodes can drastically increase the lifetime of a WSN node and enable self-sustainability. In fact, the proposed EASA in conjunction with energy harvesting capability can lead towards perpetual WSN operation and significantly outperform the state-of-the-art ASA.