A computer-aided model for the simulation of railway ballast by random sequential adsorption process

This paper presents a computer-aided multi-stage methodology for the simulation of railway ballasts using the Random Sequential Adsorption (RSA – 2D domain) paradigm. The primary stage in this endeavour is the numerical generation of a synthetic sample by a "particle sizing and positioning" process followed by a "compaction" process. The synthetic samples of ballast are then visualised in the Computer-Aided Design (CAD) environment. The outcomes of the simulation are analysed by comparison with the results of an experimental investigation carried out using a methacrylate container in which real samples of railway ballast are formed. A test of model reliability is carried out between the aggregates number and the grading curves of the synthetic sample and the real one. A validation is therefore performed using the ground-penetrating radar (GPR) non-destructive testing (NDT) method and the finite-difference time-domain (FDTD) simulation developed in a computer-aided environment. The results prove the viability and the applicability of the proposed modelling for the assessment of railway ballast conditions

Transport infrastructure monitoring by data fusion of GPR and SAR imagery information

In order to maintain the highest operational safety standards, it is crucial that surface and structural deformation caused by geophysical natural hazards and human-related activities in linear transport networks (such as highways and railways) are monitored and evaluated. Today, Ground Penetrating Radar (GPR) is a well-established technology among the available non-destructive testing (NDT) methods for the collection of ground-based information. Concurrently, the space-borne Interferometric Synthetic Aperture Radar (InSAR) is another well-known viable methodology for large-scale investigations of road network surface deformations. However, it is fair to comment that the potential of this method in the area of transport infrastructure monitoring has not yet been sufficiently explored. Within this context, this research demonstrates the viability of integrating InSAR and GPR for monitoring transport assets at network level. The main theoretical and working principles of the two above-mentioned methodologies have been presented and discussed, and the advantage and drawbacks of each technique have then been analysed. The final section of the paper examines a recent experimental activity carried out on a real-life railway located in Puglia, Southern Italy. Test outcomes prove the viability of the proposed data fusion methodology for monitoring the health of transport assets at network level

Contribution to Railway Track Maintenance Planning from the Analysis of Dynamic Movements of Trains

Dynamic movements of trains in relation to the track have a significant impact on the displacement stability of rail vehicles, having effects inclusive of operational safety. Although there are numerous approaches to track maintenance planning, most of them are based solely on long-term geometric degradation assessments without taking into account any dynamic parameters in assessing operational safety or establishing means to predict future rolling stock accelerations relative to the track in order to develop safer maintenance plans. This paper introduces a method of track maintenance planning based on geometric degradation modeling and prediction of rolling stock vertical and horizontal acceleration. The goal is to establish how frequent geometric maintenance is necessary to ensure operational safety under geometric and dynamic criteria. This approach is based on regression models defined from geometric and dynamic inspection data. The method was applied in a passenger railway and obtained expressive results that corroborated the need of considering dynamic aspects on maintenance planning, as sections of the analyzed railway were identified with operation becoming unsafe, under the dynamic criterion, before the geometric safety tolerances are reached. This work is intended not only to propose a planning method but also to present to the scientific and technical communities a novel approach to be explored and developed in future research. The obtained results, therefore, do more than confirm quantitatively the relevance of this analysis; they also demonstrate qualitatively how promising the development of this thematic field is. In this regard, this work also presents in its conclusions some research opportunities to be explored for the development of this theme. Doi: 10.28991/CEJ-2023-09-02-02 Full Text: PD

Integration of InSAR and GPR techniques for monitoring transition areas in railway bridges

This paper reports the integration of the Ground Penetrating Radar (GPR) and the Interferometric Synthetic Aperture Radar (InSAR) techniques for the monitoring of the rail-abutment transition area in railway bridges. To this purpose, an experimental campaign was conducted on a rail truss bridge located in Puglia, Southern Italy. On one hand, GPR was used to obtain structural details of the subsurface (thickness of the ballasted layer, position of the sleepers, presence of clay/humidity spots) and to identify potential construction-related issues. Parallel to this, InSAR analyses were mainly addressed to monitor subsidence at the rail-abutment transition area. Outcomes of this investigation outlined presence of subsidence at both the areas of transition and have proven the proposed integrated approach as viable to achieve a more comprehensive assessment of the structural integrity of railway bridges

Numerical modelling of additive manufacturing process for stainless steel tension testing samples

Nowadays additive manufacturing (AM) technologies including 3D printing grow rapidly and they are expected to replace conventional subtractive manufacturing technologies to some extents. During a selective laser melting (SLM) process as one of popular AM technologies for metals, large amount of heats is required to melt metal powders, and this leads to distortions and/or shrinkages of additively manufactured parts. It is useful to predict the 3D printed parts to control unwanted distortions and shrinkages before their 3D printing. This study develops a two-phase numerical modelling and simulation process of AM process for 17-4PH stainless steel and it considers the importance of post-processing and the need for calibration to achieve a high-quality printing at the end. By using this proposed AM modelling and simulation process, optimal process parameters, material properties, and topology can be obtained to ensure a part 3D printed successfully

The movement of pesticides within a mixed land use catchment

Although the application of UK non-agricultural pesticides (mainly herbicides) comprises only 3% of the total amount used, similar concentrations of agriculturally and non-agriculturallyderived pesticides are routinely detected in surface waters. This has led to concern regarding the contamination of drinking water resources at concentrations above the statutory limits of the EC Drinking Water Directive (ECDWD), and the consequent risk to human health. Before the risks to drinking water resources can be fully assessed, it is important to understand and subsequently predict the chronic and transient levels of herbicide occurrence in receiving surface waters as a result of their normal application. The factors which influence herbicide transport to the aquatic environment from sites of application, particularly from the wide variety of application substrates, are not fully understood. This project addresses this lack of knowledge through an eighteen-month programme (January 1992-March 1993) of storm event herbicide monitoring on a mixed land use catchment at North Weald (Essex) which periodically received applications of common agricultural and non-agricultural herbicides including chlorotoluron, isoproturon, diuron, simazine and atrazine. To support the field monitoring programme a robust multi-residue pesticide method was developed for the simultaneous determination of the previously mentioned compounds from storm water. This was based on liquid-liquid extraction into dichloromethane and high performance liquid chromatography using photo diode array detection. The pesticide runoff data from agricultural land agreed with similar experiments carried out in the UK. The ECDWD was frequently exceeded in baseflow conditions and more frequently during storm event periods. The extent of the exceedance was found to be related to the period which had elapsed between the herbicide application and the timing of the surface water sampling. The range of application losses for the agricultural data-set was 4.0xlO-4-O.204% (median; 4.6x10-2%). The range of peak storm event concentrations was 0.03-10.0jJg/1 (median; 0.34pg/I). Similar exceedances of the ECDWD were observed during storm and non-storm conditions for discharged waters from the urban land area of the catchment. For the urban runoff data-set, the range of application losses was 0.01-45.1% (median; 0.28%) and the range of peak storm event concentrations was 0.2-238.4pg/1 (median; 0.7pg/l). The results of the monitoring programme show that the underlying factor that differentiated between the fates of herbicides applied to the North Weald catchment was the difference in the application substrate properties. Specifically, the hard surfaces, where low infiltration capacity promotes the generation of relatively high volumes of surface runoff and where poor retention behaviour exists, allow applied herbicides to be readily transported in storm event runoff to receiving surface waters. The simazine, isoproturon, chlorotoluron and diuron runoff data produced during the monitoring programme were successfully modelled using the fugacity-based Soilfug model. In the case of chlorotoluron, this model s performance was compared with a statistical model produced using multiple linear regression analysis, which showed the former approach to be superior since it required less input data and was not site specific

Bridge monitoring and assessment by high-resolution satellite remote sensing technologies

Satellite Remote-Sensing has been successfully applied for detection of natural-hazards, (e.g. seismic events, landslides and subsidence) and transport infrastructure monitoring over the last few years. Persistent Scatterer SAR Interferometry (PSI), is a satellite remote sensing technique able to measure ground displacements over the time. More specifically, the PSI technique is an evolution of the DInSAR technique and it is based on a statistical multi-temporal differential interferogram analysis. This allows to determine coherent stable-pixels over a data-stack of SAR images, in order to identify potential ground displacements. This study aims at demonstrating the potential of the PSI technique as an innovative health-monitoring methodology for the structural integrity of bridges. For this purpose, X‐Band COSMO‐SkyMed images provided by the Italian Space Agency (ASI) were acquired and processed in order to detect structural displacements of the Rochester Bridge in Rochester, UK. Outcomes of this investigation outlined the presence of various PSs over the inspected bridge, which were proven useful to achieve a more comprehensive monitoring methodology and to assess the structural integrity of the bridge. This research paves the way for the development of a novel interpretation approach relying on the integration between remote-sensing technologies and on-site surveys to improve upon current maintenance strategies for bridges and transport assets

Innovative Methods and Materials in Structural Health Monitoring of Civil Infrastructures

In the past, when elements in sructures were composed of perishable materials, such as wood, the maintenance of houses, bridges, etc., was considered of vital importance for their safe use and to preserve their efficiency. With the advent of materials such as reinforced concrete and steel, given their relatively long useful life, periodic and constant maintenance has often been considered a secondary concern. When it was realized that even for structures fabricated with these materials that the useful life has an end and that it was being approached, planning maintenance became an important and non-negligible aspect. Thus, the concept of structural health monitoring (SHM) was introduced, designed, and implemented as a multidisciplinary method. Computational mechanics, static and dynamic analysis of structures, electronics, sensors, and, recently, the Internet of Things (IoT) and artificial intelligence (AI) are required, but it is also important to consider new materials, especially those with intrinsic self-diagnosis characteristics, and to use measurement and survey methods typical of modern geomatics, such as satellite surveys and highly sophisticated laser tools

Multiscale Machine Learning and Numerical Investigation of Ageing in Infrastructures

Infrastructure is a critical component of a country’s economic growth. Interaction with extreme service environments can adversely affect the long-term performance of infrastructure and accelerate ageing. This research focuses on using machine learning to improve the efficiency of analysing the multiscale ageing impact on infrastructure. First, a data-driven campaign is developed to analyse the condition of an ageing infrastructure. A machine learning-based framework is proposed to predict the state of various assets across a railway system. The ageing of the bond in fibre-reinforced polymer (FRP)-strengthened concrete elements is investigated using machine learning. Different machine learning models are developed to characterise the long-term performance of the bond. The environmental ageing of composite materials is investigated by a micromechanics-based machine learning model. A mathematical framework is developed to automatically generate microstructures. The microstructures are analysed by the finite element (FE) method. The generated data is used to develop a machine learning model to study the degradation of the transverse performance of composites under humid conditions. Finally, a multiscale FE and machine learning framework is developed to expand the understanding of composite material ageing. A moisture diffusion analysis is performed to simulate the water uptake of composites under water immersion conditions. The results are downscaled to obtain micromodel stress fields. Numerical homogenisation is used to obtain the composite transverse behaviour. A machine learning model is developed based on the multiscale simulation results to model the ageing process of composites under water immersion. The frameworks developed in this thesis demonstrate how machine learning improves the analysis of ageing across multiple scales of infrastructure. The resulting understanding can help develop more efficient strategies for the rehabilitation of ageing infrastructure

Moisture Content and In-place Density of Cold-Recycling Treatments

Cold-recycling treatments are gaining popularity in the United States because of their economic and environmental benefits. Curing is the most critical phase for these treatments. Curing is the process where emulsion breaks and water evaporates, leaving residual binder in the treated material. In this process, the cold-recycled mix gains strength. Sufficient strength is required before opening the cold-treated layer to traffic or placing an overlay. Otherwise, premature failure, related to insufficient strength and trapped moisture, would be expected. However, some challenges arise from the lack of relevant information and specifications to monitor treatment curing. This report presents the outcomes of a research project funded by the Illinois Department for Transportation to investigate the feasibility of using the nondestructive ground-penetrating radar (GPR) for density and moisture content estimation of cold-recycled treatments. Monitoring moisture content is an indicator of curing level; treated layers must meet a threshold of maximum allowable moisture content (2% in Illinois) to be considered sufficiently cured. The methodology followed in this report included GPR numerical simulations and GPR indoor and field tests for data sources. The data were used to correlate moisture content to dielectric properties calculated from GPR measurements. Two models were developed for moisture content estimation: the first is based on numerical simulations and the second is based on electromagnetic mixing theory and called the Al-Qadi-Cao-Abufares (ACA) model. The simulation model had an average error of 0.33% for moisture prediction for five different field projects. The ACA model had an average error of 2% for density prediction and an average root-mean-square error of less than 0.5% for moisture content prediction for both indoor and field tests. The ACA model is presented as part of a developed user-friendly tool that could be used in the future to continuously monitor curing of cold-recycled treatments.IDOT-R27-227Ope