In response to construction challenges, the Liaison project is developing an off-siteconcept for optimized RC members derived from an initial version (RCB-2.0) thatincorporates complex formworks, designed using an original analytical optimizationprocess (SOM), and 3D-printed with clay. The improved concept (RCB-2.1) makesgreater use of digital technologies to save more concrete and increase industrialscalability. The digital twins of as-built steel reinforcement cages are created using alaser tracker, enabling the 3D printing of custom-made low-carbon cementitious mortarformworks. Four 7-meter-long beams, structurally optimized, incorporating a fiber-opticBragg grating and 35% recycled aggregates were manufactured and experimentallytested (four-point bending test). They demonstrated the expected behavior in terms ofstiffness, ductility, and strength, and are now a part of a footbridge in Spain. Newpossibilities for more material savings, up to 50%, particularly through quality controland design, are also being explored and discussed.
Ballasted railways are among the most commonly used forms of track infrastructure. However, their primary drawbacks include track stability issues due to ballast degradation over time, as well as increased noise levels and high maintenance costs. A novel low-noise and vibration railway ballast track design is needed to address these concerns. This paper presents an innovative solution developed within the European project LIAISON-HORIZON-CL5-2022-D6-02-06 framework that combines modified railway ballasted track systems with recycled tires, geosynthetics, low-height ballast walls, and noise barriers. The study investigates the use of recycled waste tire rubber chips to enhance shear modulus and mitigate vibrations within the ballast layer. Large-scale cyclic simple shear tests conducted on the rubber-mixed ballast material demonstrate improved shear modulus and damping ratio. Additionally, the paper briefly examines prototype testing and performance evaluations of the modified ballast wall arrangement in conjunction with geosynthetics. The inclusion of geosynthetic materials enhances lateral confinement by facilitating particle interlocking and providing better confinement through end-to-end connections with ballast walls.
This paper introduces new contributions for construction procedures designed to enhance the robustness and precision of stress control in active anchorage and short presetressing units for long-span bridges, particularly addressing potential technical risks. The primary focus is on optimizing stress management for bridge stays, suspension cables, and short prestressing units by emphasizing a unified parameter: stress. The contributions of this research encompass (1) the introduction of advanced load cells for stress control in active anchorages and (2) the implementation of a novel synchronized multi-strain gage load cell network for short prestressing units, crucial in situations where prestressing losses can attain significant magnitudes. To validate these advancements, the authors present (3) a practical experience and results obtained from applying these methodologies in monitoring the structural response during the construction of the Tajo Bridge using the cable-stayed cantilever technique.
This work proposes a novel supervised learning approach to identify damage in operating bridge structures. We propose a method to introduce the effect of environmental and operational conditions into the synthetic damage scenarios employed for training a Deep Neural Network, which is applicable to large-scale complex structures. We apply a clustering technique based on Gaussian Mixtures to effectively select representative measurements from a long-term monitoring dataset. We employ these measurements as the target response to solve various Finite Element Model Updating problems before generating different damage scenarios. The synthetic and experimental measurements feed two Deep Neural Networks that assess the structural health condition in terms of damage severity and location. We demonstrate the applicability of the proposed method with a real full-scale case study: the Infante Dom Henrique bridge in Porto. A comparative study reveals that neglecting different environmental and operational conditions during training detracts the damage identification task. By contrast, our method provides successful results during a synthetic validation.
Although many high-sampling sensor systems tend to be power-hungry, critical monitoring applications require reliable battery-powered sensor nodes that can retrieve and compute data on the edge for years. With the advent of Tiny Machine Learning (TinyML), it is becoming increasingly feasible to deploy always-on inference Machine Learning models on constrained battery-powered microcontrollerbased nodes. However, owing to unpredictable and dynamic energy harvesting availability conditions and the limitations of battery technology, long-term operation is still challenging. In this paper, we present a hardware and software solution for long term continuous solar operation of power-hungry wireless sensor nodes with Lithium titanate oxide (LTO) batteries.
To achieve climate neutrality, synergies between circular economy (CE) and carbon reduction need to be established in the context of transport infrastructure. Implementation of the circular economy and resource efficiency (RE) policies have the potential to facilitate decarbonization targets, while using fewer natural resources, maintaining or enhancing biodiversity and providing regenerative design for generations to come. This paper presents the interpretation of RE and CE within transport infrastructure in the context of the CERCOM and LIAISON projects. As part of CERCOM, a strategic review of current practice was carried out to develop a definition of CE within transport infrastructure, and provide the successes and barriers for further transition from a linear to a circular economy. A Risk Based Assessment Framework (RBAF) and associated software tool were developed to provide a means to evaluate the impacts of certain measures and prioritize areas that require further research or investigation. LIAISON will provide further progression in this regard, and develop a methodology, support tools and close to market technological solutions to transform EU Transport Infrastructure into a more sustainable and low carbon economic activity.
Liaison Horizon Europe Project provides knowledge and technical solutions to limit transport infrastructures (TI) emissions, both caused by transport infrastructure itself and to which transport infrastructure contributes. This project covers the whole life cycle of TI to which extent TI design can influence and limit the overall emissions from construction, maintenance, operation and decommissioning of the infrastructure in a digital environment for next future TI. Liaison adopts a holistic approach to tackle this challenge, because the development of particular technical solutions is not sufficient to achieve low environmental impact TI if they are not part of a broader strategy. The only effective way to ensure the implementation of paradigm-shifting technical solutions in the TI sector is to implement a governance framework (Dynamic Multi-Infrastructure Governance Framework -DMIGF) that activates, articulates, and monitors compliance with circular economy principles throughout the life of the infrastructure when developing and implementing these solutions. Liaison develops smart and sustainable beams, rigid road pavements and improved ballast; bio-asphalt and smart pavement inspection system; intelligent tunnel control system and photovoltaic guardrails.
This paper proposes a data-driven approach to detect damage using monitoring data from the Infante Dom Henrique bridge in Porto. The main contribution of this work lies in exploiting the combination of raw measurements from local (inclinations and stresses) and global (eigenfrequencies) variables in a full-scale structural health monitoring application. We exhaustively analyze and compare the advantages and drawbacks of employing each variable type and explore the potential of combining them. An autoencoder-based deep neural network is employed to properly reconstruct measurements under healthy conditions of the structure, which are influenced by environmental and operational variability. The damage-sensitive feature for outlier detection is the reconstruction error that measures the discrepancy between current and estimated measurements. Three autoencoder architectures are designed according to the input: local variables, global variables, and their combination. To test the performance of the methodology in detecting the presence of damage, we employ a finite element model to calculate the relative change in the structural response induced by damage at four locations. These relative variations between the healthy and damaged responses are employed to affect the experimental testing data, thus producing realistic time-domain damaged measurements. We analyze the receiver operating characteristic curves and investigate the latent feature representation of the data provided by the autoencoder in the presence of damage. Results reveal the existence of synergies between the different variable types, producing almost perfect classifiers throughout the performed tests when combining the two available data sources. When damage occurs far from the instrumented sections, the area under the curve in the combined approach increases compared to using local variables only. The classificatoin metrics also demonstrate the enhancement of combining both sources of data in the damage detection task, reaching close to precision values for the four considered test damage scenarios. Finally, we also investigate the capability of local variables to localize the damage, demonstrating the potential of including these variables in the damage detection task.
The present study evaluates the shear strength characteristics, deformation properties, and degradation behavior of limestone-based reused ballast (RB) material by mixing crumbs of waste tire-derived aggregates (TDA), focusing on its suitability for railway infrastructure. Conventional large-scale direct shear tests and novel large-scale cyclic simple shear tests were performed to investigate the effects of tire-derived aggregate (TDA) content, with particle sizes varying between 22.4 mm and 50 mm. The results indicate that adding 5 % by the mass of TDA slightly reduced the friction angle from 46.6° to 44.5°, which is not a significant change compared to RB. However, increasing the TDA content to 10 % led to a notable decrease in the friction angle to 41°, highlighting the significant impact of higher TDA content on the shear strength behavior. Further, incorporating 5 % TDA improved the shear modulus and damping ratio relative to RB, which is attributed mainly to the similar larger particle sizes (22.4–50 mm) of TDA. Conversely, at 10 % TDA content, reductions in both shear modulus and damping ratio were observed. The ballast breakage index (BBI), evaluated through cyclic simple shear tests, showed a significant decrease from 15 % for RB to 9.5 % for the ballast sample containing 5 % TDA. Additionally, increased TDA content enhanced material durability, reducing Los Angeles abrasion (LAA) losses from an initial 33.5 to under 30 % at 5 % TDA. These findings demonstrate that incorporating 5 % by mass of TDA into RB material is optimal for enhancing deformation characteristics and reducing ballast degradation while maintaining adequate shear strength. This sustainable approach facilitates the recycling of waste materials, promotes a circular economy, and helps maintain safe and stable railway track conditions.
Computer-aided deep learning has significantly advanced road crack segmentation. However, supervised models face challenges due to limited annotated images. There is also a lack of emphasis on deriving pavement condition indices from predicted masks. This article introduces a novel semantic diffusion synthesis model that creates synthetic crack images from segmentation masks. The model is optimized in terms of architectural complexity, noise schedules, and condition scaling. The optimal architecture outperforms state-of-the-art semantic synthesis models across multiple benchmark datasets, demonstrating superior image quality assessment metrics. The synthetic frames augment these datasets, resulting in segmentation models with significantly improved efficiency. This approach enhances results without extensive data collection or annotation, addressing a key challenge in engineering. Finally, a refined pavement condition index has been developed for automated end-to-end defect detection systems, promoting more effective maintenance planning.
