Road surface condition is vitally important for road safety and transportation efficiency. Conventionally, road surface monitoring relies on specialised vehicles equipped with professional devices, but such dedicated large-scale road surveying is usually costly, time-consuming, and prohibitively difficult for frequent pavement condition monitoring—for example, on an hourly or daily basis. Current advances in technologies such as smartphones, machine learning, big data, and cloud analytics have enabled the collection and analysis of a great amount of field data from numerous users (e.g., drivers) whilst driving on roads. In this regard, we envisage that a smartphone equipped with an accelerometer and GPS sensors could be used to collect road surface condition information much more frequently than specialised equipment. In this study, accelerometer data were collected at low rate from a smartphone via an Android-based application over multiple test-runs on a local road in Ireland. These data were successfully processed using power spectral density analysis, and defects were later identified using a k-means unsupervised machine learning algorithm, resulting in an average accuracy of 84%. Results demonstrated the potential of collecting crowdsourced data from a large population of road users for road surface defect detection on a quasi-real-time basis. This frequent reporting on a daily/hourly basis can be used to inform the relevant stakeholders for timely road maintenance, aiming to ensure the road’s serviceability at a lower inspection and maintenance cost.

Embedded retaining walls equipped with ground heat exchangers is one kind of energy geostructures, harvesting the shallow ground energy for heating and cooling buildings. However, past investigations mainly focus on the thermal assessment of energy walls, but pay limited attention to mechanical wall behavior and associated ground response. At present, there is no standard preliminary code for assessing the long-term mechanical performance of energy walls. In this study, a comprehensive literature review is conducted to summarize the existing studies on the long-term mechanical behavior of both conventional retaining walls and thermo-active ones, respectively. The review identifies three most critical mechanical factors for energy walls (i.e., horizontal wall movement, ground settlement, and basement heave), while their permissible values can be referred to standard preliminary design criteria of conventional retaining walls in the short-term and limited long-term field measurements. For elaborating the application of design criteria, a parametric study is implemented to evaluate the long-term mechanical performance of energy walls in stiff clay under various thermal conditions, including the influence of thermal solicitations, station temperature and asymmetrical operation mode. The vivid assessing results show that the thermal-induced mechanical movements due to geothermal operation may cause unfavorable serviceability issues (e.g., cracks), but are unlikely to bring critical damages to the structural performance. The identified critical mechanical factors and evaluation of thermal influences in this study provide some guidance for the mechanical assessment of long-term energy wall behavior, particularly in the light of limited field measurements and demonstration cases.

The monitoring and assessment of ageing underground tunnels is critical to ensure their serviceability, stability, and safety as arteries for a transport network in the long term. This paper first comprehensively reviewed the long-term tunnel performance monitoring case studies, aimed at highlighting the limited field monitoring data and identifying research gaps. It was found that previous studies largely focused on the performance of single tunnel sections subject to short-term disturbances (e.g., adjacent excavation), whereas limited efforts concentrated on the long-term performance of twin tunnels, let alone those with cross passages, under the influence of deteriorations. To this end, a Wireless Sensor Network (WSN) was deployed at a critical vehicle cross passage (VCP) twin tunnel section of Dublin Port Tunnel to monitor its long-term ageing performance with time, in addition to the existing long-term water leakage and lining crack monitoring. The evolvement of lining crack and water leakage since 2010 indicated the progressive deteriorations of the monitoring section, and the deployed WSN monitoring of lining inclination demonstrated a robust sensor deployment layout and monitoring plan for (quasi) real-time monitoring for a confined underground cross passage twin tunnel network. An analytical solution was proposed to convert tunnel inclination to horizontal deformation, with the converted measurements suggesting that even more than one decade after construction, both twin tunnels are still moving horizontally towards the VCP centreline in the long term, primarily due to twin tunnel interaction. Along tunnel longitudinal direction, the closer to the VCP, the greater tunnel deformation rate is, revealing the effect of cross passage on tunnel differential longitudinal behaviour, in agreement with hypotheses and numerical results in previous studies. The field observations were believed to be attributed to the two mechanisms which are hydro-geological degradation of the surrounding ground and hydro-mechanical deterioration of the tunnel, where the correlation between tunnel deformation and deteriorations was detailed.

This paper presents the observed morphological evolution of a multilevel beach cusp system in Long Strand, Co. Cork, Ireland. The surveys were carried out with an Unmanned Aerial Vehicle (UAV) system between March and September 2019. From this site, three levels of beach cusps on the beachface (i.e., lower beach level, mid beach level and upper beach level), and critical cusp parameters are reported, including cusp spacing, cusp elevation, cusp depth, and cusp amplitude. Thus far, such an extensive dataset has not previously been reported in the literature from a single site. The evolution of the different cusp parameters is then linked with the hydrodynamics in the study area, and new prediction theories are proposed for the different cusp parameters. The Lower beach level cusps (1 < z < 2.5 m Irish Transverse Mercator (ITM)) changed with every tide and appeared when surf-similarity parameter-ξ₀ < 1.55. These cusps had a mean cusp spacing of λ_mean = 11.09 m, which are closely linked with the predictions of the self-organisation theory (p < 0.05). In contrast, the Mid beach level cusps (2.5 < z < 3.5 m ITM) are less dynamic compared to the Lower beach level cusps and can persist between spring tidal cycles. They had a mean cusp spacing of λ_mean = 18.17 m. The Upper beach level cusps (approximately z = 6 m ITM) are above astronomical tide levels and have a mean cusp spacing of λ_mean = 40.26 m. They did not change significantly over the survey period due to a lack of major storm events. These findings give a better understanding of the evolution of different cusp parameters for a multilevel beach cusp system and can be used to formulate a global theory regarding their change over time.