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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Unplanned track inspections can be a direct consequence of any disruption to the operation of on-board track geometry monitoring activities. A novel response strategy to enhance the value of the information for supplementary track measurements is thus established to construct a data generation model. In this model, artificial (synthetic) data is assigned on each measurement point along the affected track segment over a short period of time. To effectively generate artificial track measurement data, this study proposes a NARX (nonlinear autoregressive with exogenous variables) model, which incorporates short-range memory dependencies in the dependent variable and integrates interdependent effects from external factors. Nonlinearities in the proposed model have been determined using an artificial neural network that allowed fast computation of a mapping function in line with the needs of effective disruption management. The risk of over fitting the data generation model, which reflected its generalisation ability, has been effectively managed through risk aversion concept. For the model evaluation, the deviation of track longitudinal level has been taken as a case study, predicted using its degradation rate and track alignment and gauge as exogenous variables. Simulation results on two datasets that are statistically different showed that the data generation model for disrupted track measurements is reliable, accurate, and easy-to-use. This novel model is an essential breakthrough in railway track integrity prediction and resilient operation management. [ABSTRACT FROM AUTHOR]

The increase in demand for railway transportation results in a significant need for higher train axle load and faster speed. Weak and sensitive trackforms such as railway switches and crossings (or called 'turnout') can suffer from such an increase in either axle loads or speeds. Moreover, railway turnout supports can deteriorate from other incidences due to extreme weather such as floods which undermine cohesion between ballast leading to ballast washaway or loss of support under turnout structures. In this study, new intelligent automation based on machine learning pattern recognition has been built to detect and predict the deterioration of railway turnouts exposed to flooding conditions which is the scope of this study. Since the turnout system is very complex by nature, different features and smart filtering are explored to find the potential features for deep learning. Nonlinear finite element models validated by actual field measurements are used to mimic the dynamic behaviors of turnout supports under flooding conditions. The study exhibits that the novel recognition model can achieve more than 98% accuracy, yielding the potential capability to recognize and classify turnout support deteriorations facing extreme weather conditions which will be beneficial for responsible parties to schedule and plan maintenance activities. [ABSTRACT FROM AUTHOR]

Railway turnouts are essential in the train traffic route management for modern railways. Despite significant devotion to railway turnout research, one of their most common failures has not been thoroughly investigated, which is a fatigue over the turnout crossing nose. At the crossings, wheel-rail discontinuity imparts high-frequency high-magnitude forces, which are the source of fatigue failure over the crossing nose. In this study, a novel approach built on "Peridynamics" (PD) has been developed to obtain new insights into the fatigue cracks. A recent approach using "crack on mid-plane" has also been employed in this study to enhance the limited capability of Peridynamics. This paper is the world's first to investigate fatigue failures over a crossing nose from fracture mechanics perspective. This paper also introduces a novel adaptive time-mapping method as an alternative to earlier time-mapping methods for fatigue models proposed in the open literature. The new model has been verified against both Finite Element Method and experimental data. It reveals that our new approach can simulate fatigue damage, particularly in mode I crack propagation. The study has provided important insights on the fatigue crack development, which is not possible before by existing Peridynamics fatigue model. The new approach on the basis of "adaptive time-mapping" and "crack on mid-plane" is demonstrated to be effective and efficient in PD simulations. [ABSTRACT FROM AUTHOR]

Railway sleepers are safety–critical components of a railway structure. They support ballasted track superstructure and are a critical factor in track geometry and track components' deterioration. Unsupported sleepers are a common issue incurred after tracks have been utilized. When unsupported sleepers are present, they cause differential settlements of track superstructures, additional dynamic loading, and excessive train-track vibrations which affect passenger comfort, safety, and maintenance cost. This study is the world's first to develop new machine learning models to prognose and better diagnose defect severities of unsupported sleepers aligned with practical track inspection guidelines. Data used to develop machine learning models are based on a verified finite element model with actual field measurements, enabling unbiased full data ranges that govern all defect conditions. Different conditions of unsupported sleepers can be explored by varying locations of unsupported sleepers and the number of unsupported sleepers. Also, other operational parameters can be addressed such as speeds of rolling stock, the roughness of rail surface, and vertical stiffness of wheel-rail contact. In total, 2016 data sets have been obtained. Axle box accelerations are adopted as key indicators for machine learning models. Machine learning techniques used in the study are the convolutional neural network, recurrent neural network, ResNet, and fully convolutional neural network. Data fusion and assimilation have been conducted since the data points are dependent on the train speeds. Our new results reveal a breakthrough essential for real-world applications that the convolutional neural network model provides the best accuracy in both unsupported sleeper prognostics and severity identification. The accuracies of detection and severity identification are 99.34% and 97.02% respectively. [ABSTRACT FROM AUTHOR]

In 2011, the percentage of the American population affected by Noise Induced Hearing Loss (NIHL) was 15.3%. Unlike most forms of hearing loss, this can be prevented by limiting exposure to certain magnitudes of noises for varying amounts of time. The present study, using six calibrated smartphones, aims to assess the sound given off from dropping free weights in the gym, at the University of Birmingham, which can be contributing to NIHL. A relationship between drop weight and vibration is also constructed. For vibration, it is found that vibration level (m/s2) increases with the drop weight, whereas the average noise level for each drop weight only varies by a range of 4.4 dB between 102.7 and 98.3 dB. Note that all the sound levels recorded are over 85 dB, which is the range where NIHL can be contributed to. This study reminds us that measures need to be taken to reduce the sound level from the drops of loaded barbells in any sports and fitness centers. [ABSTRACT FROM AUTHOR]

In accordance with two different design methods including the technical specification for steel structures of tall buildings and the shear-bearing capacity method for infilled steel wall plates, two types of steel plate shear wall with unstiffened panels have been designed and constructed. All shear wall specimens are exposed to ultimate static monotonic and low horizontal cyclic loading conditions to determine their structural behaviors under an idealized severe earthquake event. The seismic performances of these two types of specimens are identified by the overall roof displacement angle, lateral stiffness, ductility, different distribution of horizontal force and overturning moment, and inclined angle of diagonal tension field. These two types of steel plate shear wall exhibit excellent seismic performance. However, the specimens with thin infill plate thickness of 1.1 mm perform better than the thicker specimens with plate thickness of 3.75 mm. In terms of serviceability performance, the experimental results exhibit that the thicker specimens designed by the technical specification tend to be more conservative. Their over-strength factor, strength assurance coefficient and drift angle are 4.98, 6.3 and 1/1335, respectively. However, the thinner specimens designed by the shear capacity method for shear panel yield the serviceability performance factors of 2.22, 2.71 and 1/407, respectively. It is important to note that design practice generally adopts the over-strength factor between 2 and 3, and the strength assurance coefficient is often designed for 3 and the maximum inter-story drift limit given by the design specification is 1/300. On this ground, it is apparent that the shear-bearing capacity method enables relatively more economical compared to the technical specification for steel structures. [ABSTRACT FROM AUTHOR]

Dynamic interaction between wheel and rail generates vibration and acoustic radiations transmitted through other media such as structural track systems, air, and rolling stock. Noteworthy, both wheel and rail irregularities often induce additional impact vibration and noise along the rail corridor. Rail squats and studs are typically classified as the development of any cracks that have grown longitudinally and laterally through the subsurface and become a laminated crack resulting in a depression of the rail surface. The rail defects are commonly referred to as ' squats' when they are initiated from rolling contact fatigue cracks, and as ' studs' when they are associated with a white etching layer due to wheel slips or excessive tractive effort. Interestingly, recent research shows that rail squats can be found almost everywhere, and every type of track structures, gradients and geometries in either light passenger or heavy freight traffics and for low, medium or high speed trains all over the world. A more pronounced issue appears when the crack grows and finally flakes off the rail. This paper highlights the monitoring of wheel-rail interaction over rail squat defects. Using the axle box acceleration data obtained from the calibrated track recording vehicle, the frequency spectra can be analysed. The results demonstrate an apparent influence of squats on the wheel-rail interaction over the defective sections and increased acoustic radiation at the high frequency range. The practical understanding will also enhance the development of acoustic- and vibration-based monitoring systems of rail surface defects. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Fitness culture has significantly grown since the nineteenth century. In the recent decade, the gym and fitness industry have thrived in many countries. The construction of gymnasiums has increased dramatically, and fitness centres have become one of the most common spaces in mix-used buildings around the world. There are a significant number of gyms located relatively close to residential areas, some of which have even proposed to operate 24 h a day. The noise and vibration generated by dropping free weights in the gym affect the user experience and the surrounding community to a certain extent. In addition, today’s gyms are frequently operated out of refurbished retail units. Most refurbished unit buildings’ structures were never designed to host a gym, which makes the mitigation of noise and vibration very difficult. Based on critical literature reviews, the use of gymnasiums flooring system is relatively straightforward to mitigate the noise and vibration, but its effectiveness is hardly monitored. This study mainly discusses the use of material for mitigating noise and vibration in the gymnasiums together with the crowd sensing evaluation of vibration effectiveness, uncertainty and material deterioration to manage appropriate level of noise and vibration. The gym at the University of Birmingham has been chosen as a case study. Over 10 h of field tests have been conducted to record data of the operating floor material in the gym and fitness centre using novel smartphone sensors. Considering the specific floor material, a strategy of reducing noise and vibration is proposed. In addition, health and safety assessments are also carried out to evaluate the public safety condition in the gym. The insight into novel crowdsourcing smartphone sensors can help end-users to monitor the environmental impacts around the gyms and surroundings in real time. [ABSTRACT FROM AUTHOR]

Several interesting papers have been well written by academics and acoustic practitioners in the area of rail freight noises in NSW Australia for the special issue of Acoustics Australia. Noteworthy, such the findings show that rail freight noises can be observed almost everywhere and every type of track structures. The most pronounced issue appears at sharply curved tracks where excessive lateral wheel/rail dynamics resonate with falling friction states, generating a tonal noise problem so-called 'squeal'. This letter to the editor promotes systems thinking approach and collaborative effort in resolving railway noise problems. It further enhances discussions on the systems-based lessons learnt in railway noise monitoring and management. [ABSTRACT FROM AUTHOR]