Comparisons of stakeholders' influences, inter-relationships, and obstacles for circular economy implementation on existing building sectors

Buildings are energy- and resource-hungry: their construction and use account for around 39% of global carbon dioxide emissions; they consume around 40% of all the energy produced; they are responsible for over 35% of the EU's total waste generation; and account for about 50% of all extracted (fossil) materials. Therefore, they present a significant challenge to meeting national and international Net Zero targets of reducing greenhouse emissions and fossil resource use. The CircularB Project, is at the heart of this issue, which will underpin synergies of multi-scale circular perspectives (from materials, to components, to assets and built environments), digital transformation solutions, data-driven and complexity science, stakeholder behavioral science, and interdisciplinary capabilities towards achievable, affordable and marketable circular solutions for both new and existing buildings, for sustainable urban design, and for circular built environments across Europe. This paper contributes to the project by deriving new insights into the stakeholders' influences, inter-relationships, and obstacles in the implementation of circular economy concepts on existing building stocks in Europe, which represent over 90% of whole building assets. In order to identify and derive the insights, our study is rigorously based on (i) a robust critical literature review of key documentations such as articles, standards, policy reports, strategic roadmaps and white papers; and (ii) interviews with relevant stakeholders and decision makers. Uniquely, our work spans across all scales of CE implementation from materials, to products and components, to existing building stocks, and to living built environments. The findings point out the current challenges and obstacles required to be tackled. Inadequacies of financial incentives and governmental enforcement (via policy, legislation, or directive) are commonly found to be the most critical obstacles found throughout Europe. Circular economy is the global challenge and not just a single country can resolve the climate issue without the cooperation of other countries. The insights thus highlight the essential need for harmonized actions and tactical/pragmatic policies promoted and regulated by the European Commission, national and local governments who can dominate the influence, promote inter-relationship, and overcome the barriers towards circular economy much more effectively.

Full-Scale Experimental and Field Investigations into Expansion Mechanism of Foamed Polyurethane and its Lifting Behaviors for Repair and Maintenance of Railway Slab Track Systems

Excessive settlement of the subgrade seriously reduces the service quality of slab tracks and threatens trains' running safety. While the utilization of foamed polyurethane is recognized as an effective solution, previous research on its expansion mechanism and its impact on track lifting requires further refinement. Accordingly, a series of full-scale tests, including expansion force tests on foamed polyurethane with diverse qualities and lifting tests of polyurethane grouting with varied qualities on the track structure, have been conducted. The expansion development process of foamed polyurethane is meticulously elucidated, and key expansion parameters are analyzed. Simultaneously, this research explores the lifting behavior of foamed polyurethane grouting under the slab tracks, yielding new insights into essential lifting parameters for track formation repair and maintenance. Based on the experimental data, this study proposes new empirical formulas to comprehensively describe both the expansion mechanism of foam polyurethane and its lifting behavior under the slab tracks. The outcomes of this research offer a new breakthrough for the design of lifting mechanism for maintaining slab track structures through the utilization of foam polyurethane slurry grouting, such as determining the optimal grouting quantity. In addition, these results are instrumental to the evaluation of lifting effects and service life, enhancing the circular economy of railway track systems.

Influences of Flood Conditions on Dynamic Characteristics of Novel 3D-Printed Porous Bridge Bearings

As the key safety-critical component of a bridge support system, bridge bearings are extensively used to accommodate, balance, and transfer differential displacements and loads between the superstructure and substructure of a bridge during operations. Several studies have been conducted to obtain dynamic modal parameters of traditional bridge bearings only in perfectly dry environments. However, in extreme weather conditions (e.g., heavy rain, flash floods, etc.), water can ingress and change the bearings' properties. In this study, novel 3D-printed porous bridge bearings (3DPPBBs) have been fabricated by Fused Deposition Modeling (FDM) with thermoplastic polyurethane (TPU) filaments. This study is the first to determine the influences of flood conditions on their dynamic properties, which has never been done before. An idealised single degree of freedom (ISDOF) for these novel bearings is considered for the non-destructive field-testing technique of the critical bridge component. A series of experimental tests have been performed under several conditions of flooding levels. The new results unprecedentedly indicate that relatively higher dynamic damping ratios can be found with the increasing flood levels. In contrast, the natural frequencies and dynamic stiffness decrease with the same conditions. Novel insights are essential for bridge engineers to assess and monitor bridge vibrations exposed to extreme weather conditions.

Railway infrastructure maintenance efficiency improvement using deep reinforcement learning integrated with digital twin based on track geometry and component defects

Railway maintenance is a complex and complicated task in the railway industry due to the number of its components and relationships. Ineffective railway maintenance results in excess cost, defective railway structure and components, longer possession time, poorer safety, and lower passenger comfort. Of the three main maintenance approaches, predictive maintenance is the trendy one, and is proven that it provides the highest efficiency. However, the implementation of predictive maintenance for the railway industry cannot be done without an efficient tool. Normally, railway maintenance is corrective when some things fail or preventive when maintenance is routine. A novel approach using an integration between deep reinforcement learning and digital twin is proposed in this study to improve the efficiency of railway maintenance which other techniques such as supervised and unsupervised learning cannot provide. In the study, Advantage Actor Critic (A2C) is used to develop a reinforcement learning model and agent to fulfill the need of the study. Real-world field data over four years and 30 km. is obtained and applied for developing the reinforcement learning model. Track geometry parameters, railway component defects, and maintenance activities are used as parameters to develop the reinforcement learning model. Rewards (or penalties) are calculated based on maintenance costs and occurring defects. The new breakthrough exhibits that using reinforcement learning integrated with digital twin can reduce maintenance activities by 21% and reduce the occurring defects by 68%. Novelties of the study are the use of A2C which is faster and provides better results than other traditional techniques such as Deep Q-learning (DQN), each track geometry parameter is considered without combining into a track quality index, filed data are used to develop the reinforcement learning model, and seven independent actions are included in the reinforcement learning model. This study is the world's first to contribute a new guideline for applying reinforcement learning and digital twins to improve the efficiency of railway maintenance, reduce the number of defects, reduce the maintenance cost, reduce the possession time for railway maintenance, improve the overall safety of the railway operation, and improve the passenger comfort which can be seen from its results.

Multi-Hazard Effects of Crosswinds on Cascading Failures of Conventional and Interspersed Railway Tracks Exposed to Ballast Washaway and Moving Train Loads

The interspersed railway track is an enhanced timber railway track, spot-replacing damaged wooden sleepers with new concrete sleepers to improve the bearing capacity of existing railway lines. Although this interspersed solution is characterised by low cost and short maintenance time, the interspersed tracks have worse stability than concrete tracks and can deteriorate quickly when exposed to extreme weather conditions such as heavy rains and floods. In many cases, heavy rains and floods are accompanied by strong winds. Ballast washaway can often be observed under flood conditions while the mass of trains is unevenly distributed on two rails due to the effect of lateral wind load and rail irregularities. The current work is the first in the world to investigate the collective multi-hazard effects of ballast washway and uneven axle loads on the vulnerability of conventional and interspersed railway tracks using nonlinear FEM software, STRAND 7. The train bogie is modelled by two sets of point loads. The maximum displacement, bending moment and twists have been studied to evaluate the worst condition. The novel insights will help the railway industry develop proper operations of interspersed railway tracks against naturally hazardous conditions.

Engineering, Mechanical and Dynamic Properties of Basalt Fiber Reinforced Concrete

This study investigates the engineering and mechanical properties of basalt fiber-reinforced (FRF) concrete, giving special attention to residual flexural strength and dynamic modal parameters. These properties, which have not been thoroughly investigated elsewhere, are a precursor to structural design applications for dynamic compliant structures (i.e., bridges, offshore platforms, railways, and airport pavement). Accordingly, the standard notched flexural tests have been carried out to assess the basalt fiber-reinforced concrete's residual flexural strength with an additional 0.125%, 0.25%, 0.375%, and 0.5% of volume fraction of basalt fiber. In addition, dynamic modal tests were then conducted to determine the dynamic modulus of elasticity (MOE) and damping of the FRF concrete beams. The results indicate that concrete's toughness and crack resistance performance are significantly improved with added fiber in basalt fiber reinforced concrete, and the optimum fiber content is 0.25%. It also exhibits the highest increment of compressive strength of 4.48% and a dynamic MOE of 13.83%. New insights reveal that although the residual flexural performance gradually improved with the addition of basalt fiber, the damping ratio had an insignificant change.

Track Geometry Prediction Using Three-Dimensional Recurrent Neural Network-Based Models Cross-Functionally Co-Simulated with BIM

Railway track maintenance plays an important role in enabling safe, reliable, and seamless train operations and passenger comfort. Due to the increasing rail transportation, rolling stocks tend to run faster and the load tends to increase continuously. As a result, the track deteriorates quicker, and maintenance needs to be performed more frequently. However, more frequent maintenance activities do not guarantee a better overall performance of the railway system. It is crucial for rail infrastructure managers to optimize predictive and preventative maintenance. This study is the world's first to develop deep machine learning models using three-dimensional recurrent neural network-based co-simulation models to predict track geometry parameters in the next year. Different recurrent neural network-based techniques are used to develop predictive models. In addition, a building information modeling (BIM) model is developed to integrate and cross-functionally co-simulate the track geometry measurement with the prediction for predictive and preventative maintenance purposes. From the study, the developed BIM models can be used to exchange information for predictive maintenance. Machine learning models provide the average R² of 0.95 and the average mean absolute error of 0.56 mm. The insightful breakthrough demonstrates the potential of machine learning and BIM for predictive maintenance, which can promote the safety and cost effectiveness of railway maintenance.

Digital Twins for Managing Railway Bridge Maintenance, Resilience, and Climate Change Adaptation

Innovative digital twins (DTs) that allow engineers to visualise, share information, and monitor the condition during operation is necessary to optimise railway construction and maintenance. Building Information Modelling (BIM) is an approach for creating and managing an inventive 3D model simulating digital information that is useful to project management, monitoring and operation of a specific asset during the whole life cycle assessment (LCA). BIM application can help to provide an efficient cost management and time schedule and reduce the project delivery time throughout the whole life cycle of the project. In this study, an innovative DT has been developed using BIM integration through a life cycle analysis. Minnamurra Railway Bridge (MRB), Australia, has been chosen as a real-world use case to demonstrate the extended application of BIM (i.e., the DT) to enhance the operation, maintenance and asset management to improve the sustainability and resilience of the railway bridge. Moreover, the DT has been exploited to determine GHG emissions and cost consumption through the integration of BIM. This study demonstrates the feasibility of DT technology for railway maintenance and resilience optimisation. It also generates a virtual collaboration for co-simulations and co-creation of values across stakeholders participating in construction, operation and maintenance, and enhancing a reduction in costs and GHG emission.

Digital twins for managing railway maintenance and resilience

Background: To improve railway construction and maintenance, a novel digital twin that helps stakeholders visualize, share data, and monitor the progress and the condition during services is required. Building Information Modelling (BIM) is a digitalization tool, which adopts an interoperable concept that benefits the whole life-cycle assessment (LCA) of the project. BIM's applications create higher performance on cost efficiency and optimal time schedule, helping to reduce any unexpected consumption and waste over the life cycle of the infrastructure. Methods: The digital twin will be developed using BIM embedded by the lifecycle analysis method. A case study based on Taipei Metro (TM) has been conducted to enhance the performance in operation and maintenance. Life cycles of TM will be assessed and complied with ISO14064. Operation and maintenance activities will be determined from official records provided by TM. Material flows, stocks, and potential risks in the LCA are analyzed using BIM quantification embedded by risk data layer obtained from TM. Greenhouse emission, cost consumption and expenditure will be considered for integration into the BIM. Results: BIM demonstrated strong potential to enable a digital twin for managing railway maintenance and resilience. Based on the case study, a key challenge for BIM in Taiwan is the lack of insights, essential data, and construction standards, and thus the practical adoption of BIM for railway maintenance and resilience management is still in the design phase. Conclusions: This study exhibits a practical paradigm of the digital twin for railway maintenance and resilience improvement. It will assist all stakeholders to engage in the design, construction, and maintenance enhancing the reduction in life cycle cost, energy consumption and carbon footprint. New insight based on the Taipei Mass Rapid Transit system is highly valuable for railway industry globally by increasing the lifecycle sustainability and improving resilience of railway systems.

Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning

Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions; and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world’s first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R²) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R²_GSA-GBR = 0.958) and stronger robustness (RMSE_GSA-GBR = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved.

Viaduct maintenance for future traffic demands and earthquakes

The Tokyo Metropolitan Expressway in Japan has been in operation for more than half a century. Recently, serious damages in the network have been reported in many of the old routes. However, the Metropolitan Expressway has little-to-no of experience in managing such a situation so far. Thus, developing an appropriate management strategy focusing on maintenance is critical to the expressway. The emphasis of this study is placed on the maintenance improvement of steel viaducts, and the project aims to evaluate their marginal maintenance cost and identify the influences on the cost from uncertainties such as earthquakes and future traffic demand from an econometric perspective. The study reveals that the traffic volume of passenger cars can be identified as a significant factor, while the effect of earthquake events on the cost is not clearly pronounced. Based on the analyses, it is found that the current maintenance approach is capable of dealing with fluctuations in traffic volume, which is the most influential factor. Furthermore, an increase in the amount of maintenance works could possibly lower the maintenance costs due to economies of scale. The results also indicate that the efficiency of the current maintenance method could be improved.

Large amplitude vibrations of imperfect spider web structures

Due to the high-efficiency energy absorption and high-tension strength material properties of spider silk, many researchers have studied the mechanical properties and microstructure of the spider web. The concept of spider web structure has been recognized to be adopted for structural engineering aspect. The structure of spider web and its material properties have been studied for decades. However, the fundamental free vibration mode shapes and their corresponding frequencies have never been fully investigated. This study investigates the nonlinear characteristics in the large-amplitude free vibration of imperfect spider web structures using finite element analysis. The spider web applies the concept of elastic cables taking only axial deformation into account. The finite element models of a spider web considering geometric nonlinearities are employed. It should be noted that spider web could experience large deformation when the spider uses its silk to catch prey. This research aims at analyzing the linear and geometric nonlinear behaviour of imperfect spider web structure. Four different types of imperfect spider web: spiral imperfect spider web, radial imperfect spider web, central imperfect spider web, and circular rings imperfect spider web, are considered. It is found that pretension in spider silk plays a significant role in nonlinear vibration characteristics of the spider web. Moreover, the radial thread damaged tends to have a greater effect on structural free vibration of spider web in comparison with other imperfections. The outcome will help a structural engineer to adapt the concept of spider web, its properties, and damage patterns for any larger structures.

The Self-Sealing Capacity of Environmentally Friendly, Highly Damped, Fibre-Reinforced Concrete

Cracks could attenuate the service life of concrete structures because of the intrusion of hazardous substances such as water. In this study, different proportions of Duras S500 fibre were employed to investigate the self-sealing capacity of environmentally friendly, highly damped, fibre-reinforced concrete (EFHDFRC) containing 5% crumb rubber. The workability of EFHDFRC with different proportions of the fibre was investigated by mechanical properties test. The self-sealing capacity was first measured by introducing the ultrasonic pulse velocity (UPV) test combined with the damage degree in a time-dependent manner. In addition, the regained compressive strength test and visual inspection were applied as additional measures of the self-sealing capacity. The experimental results show that EFHDFRC with different proportions of fibre showed the maximum sealing degree between the 42nd and 51st days after casting the concrete. EFHDFRC with 0.1% fibre had the best performance and the maximum self-sealing degree (2.82%). In summary, it has been proven that 0.1% fibre could stimulate the self-sealing capacity of EFHDFRC by bridging cracked concrete. Moreover, it is noted that sufficient space in cracks is essential for precipitation formation, which could seal the cracks. The new insights of this innovative self-healing, high-damping material are essential for industrial applications exposed to dynamic load conditions such as railway turnout bearers and sleepers, highspeed rail track slabs, blast-resistant walls and columns, and so on.

The effect of ground borne vibrations from high speed train on overhead line equipment (OHLE) structure considering soil-structure interaction

At present, railway infrastructure experiences harsh environments and aggressive loading conditions from increased traffic and load demands. Ground borne vibration has become one of these environmental challenges. Overhead line equipment (OHLE) provides electric power to the train and is, for one or two tracks, normally supported by cantilever masts. A cantilever mast, which is made of H-section steel, is slender and has a poor dynamic behaviour by nature. It can be seen from the literature that ground borne vibrations cause annoyance to people in surrounding areas especially in buildings. Nonetheless, mast structures, which are located nearest and alongside the railway track, have not been fully studied in terms of their dynamic behaviour. This paper presents the effects of ground borne vibrations generated by high speed trains on cantilever masts and contact wire located alongside railway tracks. Ground borne vibration velocities at various train speeds, from 100 km/h to 300 km/h, are considered based on the consideration of semi-empirical models for predicting low frequency vibration on ground. A three-dimensional mast structure with varying soil stiffness is made using a finite element model. The displacement measured is located at the end of cantilever mast which is the position of contact wire. The construction tolerance of contact stagger is used as an allowable movement of contact wire in transverse direction. The results show that the effect of vibration velocity from train on the transverse direction of mast structure is greater than that on the longitudinal direction. Moreover, the results obtained indicate that the ground bourn vibrations caused by high speed train are not strong enough to cause damage to the contact wire. The outcome of this study will help engineers improve the design standard of cantilever mast considering the effect of ground borne vibration as preliminary parameter for construction tolerances.

Life cycle analysis of mitigation methodologies for railway rolling noise and groundbourne vibration

Negative outcomes such as noise and vibration generated by railways have become a challenge for both industry and academia in order to guarantee that the railway system can accomplish its purposes and at the same time provide comfort for users and people living in the neighbourhood along the railway corridor. The research interest on this field has been increasing and the advancement in noise and vibration mitigation methodologies can be observed using various engineering techniques that are constantly put into test to solve such effects. In contrast, the life cycle analysis of the mitigation measures has not been thoroughly carried out. There is also a lack of detailed evaluation in the efficiency of various mechanisms for controlling rolling noise and ground-borne vibration. This research is thus focussed on the evaluation of materials used, the total cost associated with the maintenance of such the measures and the carbon footprint left for each type of mechanism. The insight into carbon footprint together with life cycle cost will benefit decision making process for the industry in the selection of optimal and suitable mechanism since the environmental impact is a growing concern around the world.