A number of bridge infrastructures are rising significantly due to economic expansion and growing numbers of railway and road infrastructures. Owing to the complexity of bridge design, traditional design methods always create tedious and time-consuming construction processes. In recent years, Building Information Modelling (BIM) has been developed rapidly to provide a faster solution to generate and process the integration of information in a shared environment. This paper aims to highlight an innovative 6D BIM approach for the lifecycle asset management of a bridge infrastructure by using Donggou Bridge as a case study. This paper adopts 6D modelling, incorporating 3D model information with time schedule, cost estimation, and carbon footprint analysis across the lifecycle of the bridge project. The results of this paper reveal that raw materials contribute the most embodied carbon emissions, and as the 6D BIM model was developed in the early stage of the lifecycle, stakeholders can collaborate within the BIM environment to enhance a more sustainable and cost-effective outcome in advance. This study also demonstrates the possibility of BIM applications to bridge infrastructure projects throughout the whole lifecycle. The 6D BIM can save time by transforming 2D information to 3D information and reducing errors during the pre-construction and construction stages through better visualisation for staff training. Moreover, 6D BIM can promote efficient asset and project management since it can be applied for various purposes simultaneously, such as sustainability, lifecycle asset management and maintenance, condition monitoring and real-time structural simulations. In addition, BIM can promote cooperation among working parties and improve visualisation of the project for various stakeholders.

Over the past centuries, millions of bridge infrastructures have been constructed globally. Many of those bridges are ageing and exhibit significant potential risks. Frequent risk-based inspection and maintenance management of highway bridges is particularly essential for public safety. At present, most bridges rely on manual inspection methods for management. The efficiency is extremely low, causing the risk of bridge deterioration and defects to increase day by day, reducing the load-bearing capacity of bridges, and restricting the normal and safe use of them. At present, the applications of digital twins in the construction industry have gained significant momentum and the industry has gradually entered the information age. In order to obtain and share relevant information, engineers and decision makers have adopted digital twins over the entire life cycle of a project, but their applications are still limited to data sharing and visualization. This study has further demonstrated the unprecedented applications of digital twins to sustainability and vulnerability assessments, which can enable the next generation risk-based inspection and maintenance framework. This study adopts the data obtained from a constructor of Zhongcheng Village Bridge in Zhejiang Province, China as a case study. The applications of digital twins to bridge model establishment, information collection and sharing, data processing, inspection and maintenance planning have been highlighted. Then, the integration of “digital twins (or Building Information Modelling, BIM) + bridge risk inspection model” has been established, which will become a more effective information platform for all stakeholders to mitigate risks and uncertainties of exposure to extreme weather conditions over the entire life cycle.

The Beijing-Shanghai High-Speed Railway (HSR) is one of the most important railways in China, but it also has impacts on the economy and the environment while creating social benefits. This paper uses a life cycle assessment (LCA) method and a life cycle cost (LCC) analysis method to summarize the energy consumption, carbon emissions and costs of the Beijing-Shanghai HSR from the perspective of life cycle, and proposes some corresponding suggestions based on the results. The research objective of this paper is to analyse the carbon emissions, energy consumption, and costs of the rail system which includes the structure of the track and earthwork of the Beijing-Shanghai HSR during four stages: conception stage, construction stage, operation and maintenance stage, and disposal stage. It is concluded that the majority of the carbon emissions and energy consumption of the entire rail system are from the construction stage, accounting for 64.86% and 54.31% respectively. It is followed by the operation and maintenance stage with 31.60% and 35.32% respectively. In contrast, the amount of carbon emissions and energy consumption from the conception stage is too small to be considered. Furthermore, cement is the major contributor to the carbon emissions and energy consumption during the construction stage. As for the cost, the construction stage spends the largest amount of money (US$4614.00 million), followed by the operation and maintenance stage (US$910.61 million). Improving production technologies and choosing construction machinery are proposed to reduce the cost and protect the environment.

Various techniques have been developed to detect railway defects. One of the popular techniques is machine learning. This unprecedented study applies deep learning, which is a branch of machine learning techniques, to detect and evaluate the severity of rail combined defects. The combined defects in the study are settlement and dipped joint. Features used to detect and evaluate the severity of combined defects are axle box accelerations simulated using a verified rolling stock dynamic behavior simulation called D-Track. A total of 1650 simulations are run to generate numerical data. Deep learning techniques used in the study are deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Simulated data are used in two ways: simplified data and raw data. Simplified data are used to develop the DNN model, while raw data are used to develop the CNN and RNN model. For simplified data, features are extracted from raw data, which are the weight of rolling stock, the speed of rolling stock, and three peak and bottom accelerations from two wheels of rolling stock. In total, there are 14 features used as simplified data for developing the DNN model. For raw data, time-domain accelerations are used directly to develop the CNN and RNN models without processing and data extraction. Hyperparameter tuning is performed to ensure that the performance of each model is optimized. Grid search is used for performing hyperparameter tuning. To detect the combined defects, the study proposes two approaches. The first approach uses one model to detect settlement and dipped joint, and the second approach uses two models to detect settlement and dipped joint separately. The results show that the CNN models of both approaches provide the same accuracy of 99%, so one model is good enough to detect settlement and dipped joint. To evaluate the severity of the combined defects, the study applies classification and regression concepts. Classification is used to evaluate the severity by categorizing defects into light, medium, and severe classes, and regression is used to estimate the size of defects. From the study, the CNN model is suitable for evaluating dipped joint severity with an accuracy of 84% and mean absolute error (MAE) of 1.25 mm, and the RNN model is suitable for evaluating settlement severity with an accuracy of 99% and mean absolute error (MAE) of 1.58 mm.

The concept of the Net Zero Energy Building (NZEB) has received more interest from researchers due to global warming concerns. This paper proposes to illustrate optional solutions to allow existing buildings to achieve NZEB goals. The aim of this study is to investigate factors that can improve existing building performance to be in line with the NZEB concept and be more sustainable. An existing townhouse in Washington, DC was chosen as the research target to study how to retrofit or reconstruct the design of a building according to the NZEB concept. The methodology of this research is modeling an existing townhouse to assess the current situation and creating optional models for improving energy efficiency of the townhouse in Revit and utilising renewable energy technology for energy supply. This residential building was modeled in three versions to compare changes in energy performance including improving thermal efficiency of building envelope, increasing thickness of the wall, and installing smart windows (switchable windows). These solutions can reduce energy and cost by approximately 8.16%, 10.16%, and 14.65%, respectively, compared to the original townhouse. Two renewable energy technologies that were considered in this research were photovoltaic and wind systems. The methods can be applied to reconstruct other existing buildings in the future.