Behaviour of precast reinforced concrete slabs in steel-concrete composite bridge decks with bolted shear connectors

University of Technology, Sydney. Faculty of Engineering and Information Technology.Due to ease of fabrication and maintenance and speed of construction, precast prefabricated composite deck slabs have gained huge popularity all around the globe. The precast prefabricated structural systems do not require the costly in-situ formworks. Accordingly, the precast prefabricated structural systems can reduce the cost of labour and improve the safety and speed of construction. In addition, the prefabricated composite structures can significantly facilitate application of external reinforcement in lieu of conventional internal steel bars. The reinforced concrete (RC) structures, in general, suffer maintenance and repair difficulties, as internal reinforcements in reinforced concrete (RC) structures are susceptible to corrosion that can be typically accelerated by chloride and other corrosive material ingress. Once the corrosion occurs, reinforcement starts to expand inside the concrete and that in turn causes concrete cracking and spalling. Accordingly, the reinforced concrete member cannot perform its structural role properly. Second generation bridge deck slabs, namely steel-free deck slabs, in which conventional embedded reinforcements are replaced by external reinforcements have proved to be efficient in mitigating the problems associated with corrosion of reinforcing steel bars. The steel-free deck slabs rely on development of arching action to withstand the load. The inherent arching action in longitudinally restrained reinforced concrete members was realised about fifty years ago, however, the beneficial effects of arching action has not been recognised by most of the existing reinforced concrete design standards yet. So far only Northern Island Standard, DRD, NI (1990), and Canadian code, OHBD (1992) takes account of the enhancing effect of arching action in design practice. This intrinsic capacity of laterally restrained RC structures helps the flexural reinforced concrete members to show loading capacity far in excess of flexural resistance predicted by the conventional formulas. Apart from corrosion of reinforcing steel bars, the existing steel-concrete composite deck slabs cannot be repaired and rehabilitated conveniently and without the interruption to the traffic. Although many studies have been conducted examining a wide range of composite deck systems, lack of a practical precast prefabricated steel-concrete deck slab that allow for easy replacement of concrete slabs in case of deterioration is apparent. The restrained steel-free concrete deck provides a practical solution to the corrosion of reinforcement by removing the internal steel bars and replacing them with external steel straps. However, in the meshless slabs proposed by them, the future repair and replacement of concrete slab cannot be conducted easily without a major interruption to the traffic. To take advantage of the intrinsic characteristic of precast prefabricated deck slabs and to overcome the issues associated with corrosion of internal steel bars in RC bridge decks subject to corrosive environment, a novel steel-concrete deck with precast prefabricated concrete slabs is proposed and examined in this study. The results of experimental tests on precast prefabricated slabs with high strength bolts are presented and FE numerical simulation are carried out using ATENA 2D. The novelty of this research project lies in the application of high strength steel bolts for connecting the concrete slabs to steel girders. The high strength bolts are pre-tensioned with a special amount of tensile force induced in them by a torque meter wrench. This new steel-concrete composite deck has two main advantages; firstly, there is no requirement as to design and assemble formworks for constructing cast-in-situ concrete slabs and hence the construction of deck is much faster. Secondly, the high strength bolts can be opened and the precast slab can be easily released and replaced if required. This advantage allows for easy repair and maintenance of the concrete deck slab without causing significant interruption to the traffic during repair and rehabilitation

Single residential load forecasting using deep learning and image encoding techniques

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The integration of more renewable energy resources into distribution networks makes the operation of these systems more challenging compared to the traditional passive networks. This is mainly due to the intermittent behavior of most renewable resources such as solar and wind generation. There are many different solutions being developed to make systems flexible such as energy storage or demand response. In the context of demand response, a key factor is to estimate the amount of load over time properly to better manage the demand side. There are many different forecasting methods, but the most accurate solutions are mainly found for the prediction of aggregated loads at the substation or building levels. However, more effective demand response from the residential side requires prediction of energy consumption at every single household level. The accuracy of forecasting loads at this level is often lower with the existing methods as the volatility of single residential loads is very high. In this paper, we present a hybrid method based on time series image encoding techniques and a convolutional neural network. The results of the forecasting of a real residential customer using different encoding techniques are compared with some other existing forecasting methods including SVM, ANN, and CNN. Without CNN, the lowest mean absolute percentage of error (MAPE) for a 15 min forecast is above 20%, while with existing CNN, directly applied to time series, an MAPE of around 18% could be achieved. We find the best image encoding technique for time series, which could result in higher accuracy of forecasting using CNN, an MAPE of around 12%

Deep learning based forecasting of individual residential loads using recurrence plots

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. High penetration of renewable energy resources in distribution systems brings more uncertainty for system control and management due their intermittent behaviour. In this context, besides generation side, demand side should be also controlled and managed. Since demand side has variant flexibility over time, in order to timely facilitate Demand Response (DR), distribution system operators (DSO) should be aware of DR potential in advance to see whether it is sufficient for different services, and how much and when to send DR signals. This indeed requires accurate short-term or medium-term load forecasting. There are many methods for predicting aggregated loads, but more effective DR schemes should involve individual residential households which would require load forecasting of single residential loads. This is much more challenging due to high volatility in load curves of single customers. In this paper, we present a novel method of forecasting individual household power consumption using recurrence plots and deep learning. We use Convolutional Neural Network (CNN) for such a two-dimensional deep learning approach, and compare it with one-dimensional CNN, as well as Support Vector Machine (SVM) and Artificial Neural Network (ANN). Demonstrating some experimental tests on a real case proved that our approach outperforms the other existing solutions

Time-of-Use Tariff with Local Wind Generation

Renewable energy, such as wind power, is known to significantly reduce system costs and carbon emissions. However, traditional Time of Use (ToU) tariffs fail to account for local energy generation. To overcome this limitation, we propose a mechanism for calculating new ToU tariffs that incorporates Agile ToU and local energy resources, such as a wind farm. By partially supplying local consumption, wind energy can reduce electricity costs for consumers and encourage load shifting towards peak renewable energy production periods. We demonstrate the effectiveness of the proposed mechanism by testing it on a case study of a residential area in Wales, UK, where electricity would be partially supplied by a nearby wind farm with 5 turbines through a Power Purchase Agreement (PPA). The results show that the new tariff significantly reduces electricity bills

Mechanical Intelligence (MI): A Bioinspired Concept for Transforming Engineering Design

Despite significant scientific advances in the past decades, most structures around us are static and ironically outdated from a technological perspective. Static structures have limited efficiency and durability and typically perform only a single task. Adaptive structures, in contrast, adjust to different conditions, tasks, and functions. They not only offer multi-functionality but also enhanced efficiency and durability. Despite their obvious advantages over conventional structures, adaptive structures have only been limitedly used in everyday life applications. This is because adaptive structures often require sophisticated sensing, feedback, and controls, which make them costly, heavy, and complicated. To overcome this problem, here the concept of Mechanical Intelligence (MI) is introduced to promote the development of engineering systems that adapt to circumstances in a passive-automatic way. MI will offer a new paradigm for designing structural components with superior capabilities. As adaptability has been rewarded throughout evolution, nature provides one of the richest sources of inspiration for developing adaptive structures. MI explores nature-inspired mechanisms for automatic adaptability and translates them into a new generation of mechanically intelligent components. MI structures, presenting widely accessible bioinspired solutions for adaptability, will facilitate more inclusive and sustainable industrial development, reflective of Goal 9 of the 2030 Agenda for Sustainable Development

Nonintrusive Load Monitoring (NILM) Using a Deep Learning Model with a Transformer-Based Attention Mechanism and Temporal Pooling

Nonintrusive load monitoring (NILM) is an important technique for energy management and conservation. In this paper, a deep learning model based on an attention mechanism, temporal pooling, residual connections, and transformers is proposed. This article presents a novel approach for NILM to accurately discern energy consumption patterns of individual household appliances. The proposed method entails a sequence of layers, including encoders, transformers, attention, temporal pooling, and residual connections, offering a comprehensive solution for NILM while effectively capturing appliance-specific energy usage in a household. The proposed model was evaluated using UK-DALE, REDD, and REFIT datasets in both seen and unseen cases. It shows that the proposed model in this paper performs better than other methods stated in other papers in terms of F1-score and total error of the results (in terms of SAE). This model achieved an F1-score equal to 92.96 as well as a total SAE equal to −0.036, which shows its effectiveness in accurately diagnosing and estimating the energy consumption of individual home appliances. The findings of this research show that the proposed model can be a tool for energy management in residential and commercial buildings