Bentley Systems (United Kingdom)

Maintenance has entered its artificial intelligence (AI) era. By processing and analysing huge volumes of trusted, reliable datasets, AI is unlocking insights that are significantly improving the way that our critical infrastructure is managed and maintained as well as informing reliability and demand-led investment decisions. In this article, ICE Fellow, David Philp, explains where and how AI is being put to work. He provides examples relating to highways, bridges and metros. He also explains why the need for a ‘superpower’ of this kind is so necessary due to factors including an ageing infrastructure asset base and the pressure being placed on our infrastructure from the impacts of climate change, increasing city populations and budget constraints. He concludes that, while AI will not eliminate infrastructure maintenance woes, it does provide the means for a huge leap forward in managing assets more intelligently, efficiently and proactively.

Abstract Global power generation from floating offshore wind turbines is expected to grow from 132 MW in 2022 to 289 GW by 2035. During the design phase these floating systems will require advanced numerical analysis tools to ensure strucutural integrity and reliability. A new method for the hydrodynamic analysis of these structures is presented. The long standing offshore operations simulation software MOSES has been enhanced with the addition of the AeroDyn aeroelastic wind turbine solver from OpenFAST. This coupled analysis tool is designed to compute the motions and loads on the floating system. An added benefit of using MOSES is that these loads can be readily mapped on to a structural model for assessing code compliance. In this paper the MOSES-AeroDyn solver has been validated against published numerical and model test data of the OC3-Hywind system in regular waves. The coupled MOSES-AeroDyn results were found to be in good agreement with both the experimental and numerical data.

Operations of water distribution networks (WDNs) are monitored daily via installed data loggers, where the collated hydraulic data can be leveraged to improve the system’s operations over time, and to minimize total economic losses due to non-revenue water (NRW). In collaboration with Public Utility Board (PUB), Singapore’s National Water Agency, a practically novel model calibration approach using 24/7 monitoring flow and pressure data has been developed to facilitate PUB’s Smart Water Grid (SWG). The approach is developed as a generic integrated solution process to conduct a series of systematic analyses for daily WDN model calibration, namely: (1) estimating the system’s daily NRW contributions; (2) performing flow calibration that involves net demand consumption calibration, adjusting pumps operational configurations and localizing NRW sources when the system’s daily estimated NRW volume exceeds its assumed background volume; (3) performing energy calibration by rectifying possible drifting in monitored pressure head data and calibrating other physical properties which include, but not limited to, pipe roughness and valve settings, especially during peak-demand hours. The effectiveness of our proposed approach is subsequently tested on three WDN zones in Singapore, having a total pipe length of >100km, that comprises of atypical water usage patterns. The results of model calibration for one of three zones is presented in this paper. The key outcomes derived from the study are: (a) localized a reported leakage event by PUB to less than 100m; (b) calibrated the system’s flow balance, to less than 1% average mean absolute percentage error (MAPE), by first identifying and addressing the system’s billing data uncertainties, followed by localizing anomaly events that account for the total NRW volume estimated; and (c) calibrated the system’s pipe roughness values to improve the total energy balance by achieving an average daily MAPE of 4.0%.

Acoustic sensors are widely used for monitoring urbanized water distribution networks (WDNs) to detect and localize pipe leakages. Since their inception, few research studies have focused on developing a generic, effective, and practical methodology to analyse complex acoustics signals for leakage detection and localization in large-scale WDNs. In collaboration with PUB, Singapore’s National Water Agency, a generic acoustic data analysis approach has been developed to facilitate PUB’s present Smart Water Grid (SWG) management. The proposed approach encompasses multi-stage systematic analyses, namely: (1) data quality assessment; (2) data pre-processing; (3) near real-time leakage event detection and classification; and finally (4) near real-time leakage localization. Our proposed approach is then tested in major WDNs in Singapore having more than 1100km of underground water pipelines and 82 permanently installed hydrophone acoustic sensors between 1 Aug 2019 and 31 Aug 2020, where multiple historical leakage events were reported to within 600m, or less, from neighbouring hydrophones across the large complex networks. By emulating the near real-time detection and localization analyses daily, our proposed methodology could localize reported leakage events to an error range of around 150m on average, while demonstrating significant and stable acoustic leakage power rate over the temporal size of the leakage event cluster(s).

BackgroundObesity is a risk factor for complications from SARS-CoV-2 infection, increasing the need for effective weight management measures in primary care. However, in the UK, COVID-19 restrictions have hampered primary care weight management referral and delivery, and COVID-19 related weight gain has been reported. The present study evaluated outcomes from a multicomponent weight loss and health promotion programme in UK primary care, delivered remotely due to COVID-19 restrictions.MethodPatients with obesity, type 2 diabetes or pre-diabetes attended six 90 min sessions over 10 weeks on Zoom. The dietary component comprised a low-carbohydrate ‘real food’ approach, augmented with education on physical activity, intermittent fasting, gut health, stress management, sleep and behaviour change. Anthropometric and cardiometabolic data were self-reported. Mental well-being was assessed with the Warwick Edinburgh Mental Wellbeing Scale. Subjective outcomes and participant feedback about the programme were collected with an anonymous online survey.ResultsTwenty participants completed the programme. Weight loss and improvements in body mass index, waist circumference, systolic and diastolic blood pressure and mental well-being achieved statistical and clinical significance. Mean weight loss (5.8 kg) represented a 6.5% weight loss. Participants’ subjective outcomes included weight loss without hunger (67%) and increased confidence in their ability to improve health (83%). All participants reported the usage of Zoom to access the programme as acceptable with 83% reporting it worked well.ConclusionA multicomponent weight loss and health promotion programme with a low-carbohydrate dietary component, clinically and statistically significantly improved health outcomes including weight status, blood pressure and mental well-being in a group of primary care patients when delivered remotely. Further research is warranted.

Abstract Crack detection has been an active research topic for civil infrastructure inspection. Over the last few years, many research efforts have focused on applying deep learning-based techniques...

Designers work by creating alternatives.Current design media restrict this practice through their near-universal adherence to a single-state document model.We describe the implementation of an online gallery system built as part of a research program to understand new media types for working with design alternatives in parametric modeling.The online gallery supports multiple commercially available parametric modelers.A user study shows a significant difference between two modes of gallery operation and a qualitative study describes user patterns in using the online gallery system.

Hydraulic calculations performance becomes a very significant factor when large urban drainage systems are modeled under dynamic conditions for many hours of simulation duration. These systems typically contain many thousands of partly full circular pipes of different sizes and the hydraulic calculations of normal depth, critical depth and critical slope are involved in every pipe at every time step. This paper presents a method in which the theoretically exact circular pipe equations are represented by very simple explicit polynomial equations. These explicit equations are shown to be fast and accurate. Significant performance improvement is achieved by replacing the iterative method with the explicit equations.