Sensing Advancement and Health Monitoring of Transport Structures.

Planning, design, construction, maintenance and management of transport infrastructure demand new methods and approaches to optimise utilisation of materials, energy and workforce [...].

Testing Sentinel-1 SAR Interferometry Data for Airport Runway Monitoring: A Geostatistical Analysis.

Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques are gaining momentum in the assessment and health monitoring of infrastructure assets. Amongst others, the Persistent Scatterers Interferometry (PSI) technique has proven to be viable for the long-term evaluation of ground scatterers. However, its effectiveness as a routine tool for certain critical application areas, such as the assessment of millimetre-scale differential displacements in airport runways, is still debated. This research aims to demonstrate the viability of using medium-resolution Copernicus ESA Sentinel-1A (C-Band) SAR products and their contribution to improve current maintenance strategies in case of localised foundation settlements in airport runways. To this purpose, "Runway n.3" of the "Leonardo Da Vinci International Airport" in Fiumicino, Rome, Italy was investigated as an explanatory case study, in view of historical geotechnical settlements affecting the runway area. In this context, a geostatistical study is developed for the exploratory spatial data analysis and the interpolation of the Sentinel-1A SAR data. The geostatistical analysis provided ample information on the spatial continuity of the Sentinel 1 data in comparison with the high-resolution COSMO-SkyMed data and the ground-based topographic levelling data. Furthermore, a comparison between the PSI outcomes from the Sentinel-1A SAR data-interpolated through Ordinary Kriging-and the ground-truth topographic levelling data demonstrated the high accuracy of the Sentinel 1 data. This is proven by the high values of the correlation coefficient (r = 0.94), the multiple R-squared coefficient (R2 = 0.88) and the Slope value (0.96). The results of this study clearly support the effectiveness of using Sentinel-1A SAR data as a continuous and long-term routine monitoring tool for millimetre-scale displacements in airport runways, paving the way for the development of more efficient and sustainable maintenance strategies for inclusion in next generation Airport Pavement Management Systems (APMSs).

Can smart rainwater harvesting schemes result in the improved performance of integrated urban water systems?

Although rainwater harvesting (RWH) schemes have gradually gained more credibility and popularity in recent times, efficient utilisation and larger scale implementation of multi-purpose RWH are still a challenging task. This paper aims to explore the potential of using smart RWH schemes and their impact on the efficiency improvement in integrated urban water systems (UWS). The smart RWH scheme analysed here is capable of proactively controlling the tank water level to ensure sufficient spare storage is maintained at all times that accommodates the runoff from storm events. The multi-purpose RWH tank can mitigate local floods during rainfall events and supply harvested rainwater to non-potable residential water consumption. Optimal design parameters of the smart RWH scheme are also identified to achieve the best operational performance of the UWS. WaterMet2 model is used to assess the performance of the UWS with smart RWH schemes. The efficiency of the proposed methodology is demonstrated through modelling a real case of integrated UWS. The results obtained indicate that utilisation of smart RWH with an optimally sized tank, compared to the corresponding conventional RWH, is able to significantly improve the UWS efficiency in terms of mitigation of local flooding and reliability of water supply from harvested rainwater.

Predicting the Probability of Failure of Cementitious Sewer Pipes Using Stochastic Finite Element Method.

In this paper, a stochastic finite element method (SFEM) is employed to investigate the probability of failure of cementitious buried sewer pipes subjected to combined effect of corrosion and stresses. A non-linear time-dependant model is used to determine the extent of concrete corrosion. Using the SFEM, the effects of different random variables, including loads, pipe material, and corrosion on the remaining safe life of the cementitious sewer pipes are explored. A numerical example is presented to demonstrate the merit of the proposed SFEM in evaluating the effects of the contributing parameters upon the probability of failure of cementitious sewer pipes. The developed SFEM offers many advantages over traditional probabilistic techniques since it does not use any empirical equations in order to determine failure of pipes. The results of the SFEM can help the concerning industry (e.g., water companies) to better plan their resources by providing accurate prediction for the remaining safe life of cementitious sewer pipes.

Prediction of sulphide build-up in filled sewer pipes.

Millions of dollars are being spent worldwide on the repair and maintenance of sewer networks and wastewater treatment plants. The production and emission of hydrogen sulphide has been identified as a major cause of corrosion and odour problems in sewer networks. Accurate prediction of sulphide build-up in a sewer system helps engineers and asset managers to appropriately formulate strategies for optimal sewer management and reliability analysis. This paper presents a novel methodology to model and predict the sulphide build-up for steady state condition in filled sewer pipes. The proposed model is developed using a novel data-driven technique called evolutionary polynomial regression (EPR) and it involves the most effective parameters in the sulphide build-up problem. EPR is a hybrid technique, combining genetic algorithm and least square. It is shown that the proposed model can provide a better prediction for the sulphide build-up as compared with conventional models.