Ramboll (Denmark)

BackgroundAssessment of subtle hip fractures on radiographs can be difficult, especially among less experienced emergency physicians, which may prolong the diagnosis and ultimately time to surgery. Clinical artificial intelligence (AI) decision support tools have shown great potential in assisting the detection of fractures on radiographs.PurposeTo investigate how a CE-marked AI fracture detection tool affects junior doctors' diagnostic accuracy in detecting hip fractures on radiographs.Material and MethodsEight junior doctors with affiliation to the Accident and Emergency (A&E) department read 246 hip radiographic examinations with and without AI support. The reference standard was determined by two musculoskeletal radiologists, to measure sensitivity and specificity for readers without and with support from the AI tool as well as the AI tool's standalone performance.ResultsMean sensitivity in detecting hip fractures increased significantly from 0.89 (95% confidence interval [CI] = 0.85-0.93) without AI support to 0.94 (95% CI = 0.92-0.97) (χ2 = 9.27; P = 0.002) with AI support and the false-negative cases was thereby reduced by 49%. There was no significant change in mean specificity 0.90 (95% CI = 0.86-0.93) to 0.91 (95% CI = 0.88-0.94) (χ2 = 0.34; P = 0.56). The AI standalone performance was 0.99 (95% CI = 0.99-1.00) and 0.73 (95% CI = 0.67-0.80) in sensitivity and specificity, respectively.ConclusionOut of eight junior doctors, seven detected more fractures with AI assistance than without. The applied performance gain for readers highlights the value of the product.

Massive open online courses (MOOCs) have emerged as powerful educational tools for disseminating complex scientific knowledge. This study examines the RECONECT MOOC, an innovative online learning platform designed to educate stakeholders about nature-based solutions (NbS) for hydrometeorological risk reduction. Developed by a multidisciplinary team of international experts, the course drew on diverse professional and geographical perspectives, across a range of national and sectoral contexts. Its primary aim to bridge the knowledge gap in climate adaptation strategies across diverse professional sectors. The research investigated the effectiveness of interactive, multimedia-based learning approaches in communicating advanced NbS concepts. Using the Sharable Content Object Reference Model (SCORM) framework, the course integrated multiple learning methods, including reading, listening, inquiry-based learning, discussion forums and practical application exercises. The MOOC was divided into six modules and targeted local authorities, policymakers, academics, postgraduate students and other stakeholders interested in climate resilience. Findings revealed that 90% of participants gained additional NbS knowledge, 69% reported a shift in perception and 62% felt confident applying what they learned. Participants valued the interactive resources and flexible, self-paced format. Overall, the study demonstrates that MOOCs can effectively support scientific knowledge dissemination and strengthen understanding of complex environmental solutions.

In the process of designing long span bridges, runability analysis plays an important role. In fact, it deals with the assessment of the impact on the structure’s response and train running safety of external factors such as environmental actions, like wind and earthquake, or catastrophic events like ship impact. Runability analysis involves a combination of local aspects (e.g. wheel–rail interaction) with global and local bridge dynamic response. In this respect, runability analysis represents a complex task to be tackled and requires adequate modelling. The present work proposes a runability study to be performed on the New Storstrøm Bridge, which is currently under construction in Denmark connecting Zealand with Falster. In this numerical analysis. two scenarios have been considered separately: ship impact on the bridge’s superstructure and wind action on a rail vehicle, using both the “Chinese hat” and stochastic turbulent wind formulations. The outcomes of this study clearly show that, even when threshold values were locally reached in terms of train running safety, a close examination of the local wheel–rail contact condition helps in defining the expected evolution of the dynamics of the train and thus documenting a higher safety level than indicated by the basic requirements.

This paper presents an experimental investigation of an innovative Timber-Concrete Composite (TCC) deck with a dry-dry demountable connection system. The connections were made as notches, and the composite deck was assembled from a CNC-cut timber element and two prefabricated concrete panels joined at mid-span. The longitudinal joint between the concrete elements facilitated pre-activation of the deck, which has several advantages, including potential for pre-camber and a diminution of displacements induced by early-age shrinkage. All connections in the new deck were designed for disassembly to ease reuse of the materials at end-of-service-life and the environmental impact was reduced by adopting a carbon-minimized concrete mix design which resulted in semi-low strengths. The new deck system has been experimentally investigated through 14 four-point bending tests, comprising seven specimens designs with varying notch width, number of fasteners, and intended imperfections at selected notch mouths. The performance of the deck, in terms of connection efficiency and displacement capacity (ductility), was assessed and compared with 35 tests from the literature on existing wet-dry solutions. The findings revealed that the behavior of the new dry-dry deck system is comparable with existing wet-dry solutions. Most of the connections failed from concrete failures because of the low concrete strength, and when combined with a fastener, these failures exhibited a remarkable ductile local behavior, leading to elevated levels of global displacement capacity. The experimental study demonstrated a promising potential for improved structural behavior despite being more climate friendly.

The Bogibeel Bridge in India, spanning the Brahmaputra River, is a 4.94 km rail-cum-road bridge pivotal for Northeast India’s infrastructure. Its design incorporates 41 spans with steel truss girders and a concrete slab. This paper focuses on the bridge’s advanced Structural Health Monitoring (SHM) system, which employs around 920 sensors for continuous monitoring of key structural aspects like strain, temperature, vibration, and displacement. A critical feature of this SHM system is its integration with the bridge’s asset management, allowing automated reporting and alarms based on data analysis for proactive maintenance. This innovative approach combines sensor-based data, cloud computing, and automated analytics to move maintenance from reactive to predictive, enhancing operational efficiency, safety, and cost-effectiveness. By early detection of structural issues, it ensures timely intervention, minimizing downtime. The adopted approach to the SHMS on the bridge, significantly improves safety, efficiency, and cost-effectiveness, demonstrating a pioneering model for smart infrastructure management.

Nature-Based Solutions (NBS) are growing in popularity as approaches for, among others, the reduction of hydro-meteorological risks. However, their uptake is still slow despite the recognition of their role in enabling a smarter, more systemic, and faster adaptation to climate change. Uncertainties regarding the valuation and subsequent economic feasibility of NBS are deemed to be a substantial barrier to their uptake. The monetary valuation of the non-tangible benefits of NBS (e.g., increased recreation and enhanced biodiversity) is seen as a plausible strategy to get closer to providing a holistic assessment. However, the quantification of these non-tangible benefits is often not integrated into economic assessments of NBS, partly as methods that can be replicated across sites have been lacking. This situation risks leading to inconsistent and/or biased valuations, which may negatively influence or delay the prioritization of NBS. Thus, our study aims to collect primary data on the non-tangible benefits of NBS across different European study sites and to identify patterns in how the different types and contexts of NBS influence people's valuation. We applied a contingent valuation survey to six different peri‑urban NBS study sites, where the focus is on the reduction of hydro-meteorological risks. The target of the survey was the general public, and we used willingness-to-pay questions to quantify the value given by the respondents to the NBS. Our results highlighted that people value the multiple benefits of NBS, and positively react to implementations improving nature across all sites. Moreover, similar variables seem to influence the value attributed to NBS across sites, with income and personal preferences ranking highly among them. These insights indicate that decision-makers and NBS-planners can learn more broadly from past experiences from a variety of (cultural) contexts, which can assist in delivering more targeted and multi-functional NBS.