Virtual replicas of infrastructure can be used to run simulations and optimize the construction, management, and maintenance of such assets throughout their entire lifecycle. These digital twins (defined as integrated multi-physics, multiscale, and probabilistic simulations of a complex product) mirror the behavior and environmental responses of its corresponding twin. Digital reconstruction techniques using optical sensor technologies and mobile sensor platforms are providing viable, low-cost alternatives to develop digital twins of physical infrastructure. In previous work, the digital twinning of asphalt pavement surfacings using visual simultaneous localization and mapping and the initiation of a digital twin of a local road network were investigated and successfully demonstrated. In this article, the further development of the concept, incorporating road surface temperatures collected over a 1-month period, as well as potential inferences based on these data, in the micro- and macro-twinning of a local road, are discussed. Light detection and ranging, unmanned aerial vehicles, and traffic counting artificial intelligence allows for quantification of the road geometry and infrastructure utilization over large areas (macro-twinning), whereas the photogrammetric reconstruction technique based on a neural network, a proprietary environmental condition sensor (SNOET, or SNiffing Omgewing / Environmental Tester) and commercial temperature sensors were used to acquire the surface texture and environmental conditions respectively (micro-twinning), as well as surface temperatures at four locations and different surfacing materials. The combination of advanced environmental monitoring data, physical data, and surface temperature data provide management data that can assist in the maintenance of such roads. This article expands (with the permission of the conference organizers) on a GeoChina 2021 article through the addition of further temperature data collected on the discussed digital twin, with substantial additional data analysis and discussion.

• Successful realisation of a low-cost RTK-grade geolocation service. • Digital twinning of both modern and historically significant assets. • Mobile acquisition unit incorporating high level customizability. • Low cost and wide serviceability area for multidisciplinary research.

Structural Health Monitoring (SHM) has emerged as a viable alternative to traditional visual and non-destructive assessment methods for civil infrastructure. The integration of sensor systems, the Internet of Things (IoT), and advanced data processing has further digitised SHM, leading to the development of Digital Twin (DT) technology, enabling dynamic, real-time simulations for proactive risk prediction and asset management. However, many existing DT-based SHM systems are costly, complex, and resource-intensive, limiting their practicality for small-scale implementations. This study investigates the feasibility of a practical and cost-effective SHM framework enhanced by DT technology for civil infrastructure. The hardware system explored two low-cost displacement sensors: a potentiometer contact sensor and an infrared non-contact sensor. During the static load testing, the potentiometer demonstrated high accuracy and stability, whilst the infrared sensor, despite higher noise, was effective for submillimetre measurements. These sensors were integrated with an IoT-enabled Arduino Nano 33 microcontroller for remote access via the cloud platform. The software system, “ReConTwin”, developed using open-source resources, provides near real-time updates, analysis, and damage diagnosis through an automated post-processing system. The calibrated DT replicated the force–displacement response, accurately estimated the applied load, and closely predicted mid-span strain and crack formations of a Reinforced Concrete (RC) beam specimen subjected to short-term static three-point bending loads in a controlled laboratory setting. The user-friendly design and compatibility with standard commercial computers enhance the accessibility and feasibility of the proposed DT-SHM framework, making it a promising scalable solution for widespread adoption in real-world civil infrastructure applications.

A Perfectly Accurate, Synthetic dataset for Multi-View Stereopsis (PASMVS) is presented, consisting of 400 scenes and 18,000 model renderings together with ground truth depth maps, camera intrinsic and extrinsic parameters, and binary segmentation masks. Every scene is rendered from 45 different camera views in a circular pattern, using Blender's path-tracing rendering engine. Every scene is composed from a unique combination of two camera focal lengths, four 3D models of varying geometrical complexity, five high definition, high dynamic range (HDR) environmental textures to replicate photorealistic lighting conditions and ten materials. The material properties are primarily specular, with a selection of more diffuse materials for reference. The combination of highly specular and diffuse material properties increases the reconstruction ambiguity and complexity for MVS reconstruction algorithms and pipelines, and more recently, state-of-the-art architectures based on neural network implementations. PASMVS serves as an addition to the wide spectrum of available image datasets employed in computer vision research, improving the precision required for novel research applications.

• Successful realisation of a low-cost data acquisition system for geotechnical applications. • Negative pore pressure, soil temperature and dielectric permittivity measurements. • Integrated battery power and SigFox communications for remote operation. • High level of customizability for adding additional sensors.