Natural hazard-induced disasters have caused catastrophic damage and loss to buildings, infrastructure, and the affected communities as a whole during the recent decades and their impact is expected to further escalate in the future. Thus, there is a huge demand for disaster risk management using digitalisation as a key enabler for effective and efficient disaster risk management systems. It is widely accepted that digital and intelligence technologies can help solve key aspects of disaster risk management such as disaster prevention and mitigation, and rescue and recovery. Digital Twin (DT) is one of the most promising technologies for multi-stage management which offers significant potential to advance disaster resilience. Smart Cities (SCs) use pervasive information and communications technology to monitor activities in the city. With increasingly large applications of DTs combined with big data generated from sensors in a SC, it is now possible to create Digital Twin Smart Cities (DTSCs). Despite the increasing prevalence of DTSC technologies and their profound impact on disaster risk management, a systematic and longitudinal view of the evolution to the current status of DTSC for disaster risk management does not exist. This review analyses 312 titles and abstracts and 72 full papers. To begin with, a scientific review of DT and SC is undertaken, where the evolution of DTSCs is reviewed. In addition, the intelligence technologies used in DTSCs for disaster risk management are assessed and their benefits are evaluated. Furthermore, the evolution and technical feasibility of DTSC-driven disaster risk management is evaluated by assessing current applications of DTSCs in disaster risk management. It was found that despite the significant potential benefits offered by DTSCs, they also add a new layer of complexities and challenges inherent to these technologies to the already complex web of complexities involved in disaster risk management. These challenges can be addressed by understanding how the process of utilising DTSCs in disaster risk reduction and sustainability is designed, which is essential for comprehending what DTSCs may offer, how it is implemented, and what it means to all involved stakeholders. This paper contributes to the knowledge by improving the understanding of the current status of DTSC technologies and their impact on disaster risk management, and articulating the challenges in implementing DTSC, which inspires the professional community to advance these technologies to address them in future research.

Generating an object-oriented, geometric digital twin of an existing railway from its point cloud data (PCD) is a laborious task, needing 10 times more labor hours than scanning the physical asset. The resulting modeling cost counteracts the expected benefits of the twin. This cost and effort can be reduced by automating the process of creating such models. The first perceived challenge to achieving such automation is detecting masts from airborne light detection and ranging (LiDAR) data because their position and function (separating substructure from superstructure) is critical to the subsequent detection of other elements. This paper presents a method that tackles the aforementioned challenge by leveraging the highly regulated and standardized nature of railways. In railway infrastructure, the geometric relations of a unit contain overhead line equipment (OLE) between two mast pairs are consistent and repetitive throughout the track. The proposed method starts with tools for cleaning the PCD and roughly detecting its positioning and orientation. The resulting data sets are then processed by restricting the search region of the masts considering its positions compared with the track centerline. Subsequently, the masts are detected using the Random Sample Consensus (RANSAC) algorithm. The final deliverables of the method include the coordinates of the mast positions, detected point clusters and three-dimensional (3D) models of the masts in Industry Foundation Classes (IFC) format. The method was implemented in a prototype and tested on three railway PCDs with a cumulative length of 18 km. The results indicated that the method achieves an overall detection rate of 94%. This is the first method in automatically detecting masts from airborne LiDAR data.

Purpose: Quantity surveying education in Sri Lanka (SL) presently does not appear to be catering to the industry needs indicating that it may not be up to the expected standard. Hence, the purpose of this study is to identify the gap between the competencies of graduate quantity surveyors (QSs) and the competencies that industry in SL expects from them.Design/methodology/approach: A hybrid approach was used, consisting of desk reviews, expert interviews and a questionnaire survey. A comparative analysis identifying differences between two competency levels was carried out.Findings: Analysis reveals that competencies of graduate QSs in areas of cost planning, strategic planning, risk management, value management, life cycle cost analysis, sustainability, surveying and levelling, research and development, building surveying and business management are at levels higher than industry needs. However, majority of competencies are at levels lower than industry expectations.Research limitations/implications: This research was focused only on competencies of QSs who have successfully completed a quantity surveying degree programme accredited by Institute of QSs SL, Royal Institution of Chartered Surveyors, Australian Institute of QSs and Pacific Association of QSs. It excluded non-graduates’ competencies as they gain competencies only through work experience.Practical implications: This study revealed the need for designing quantity surveying degree programmes to cater to industry needs to ensure that graduates from these programmes are acceptable to the industry.Originality/value: This study made an original contribution to knowledge by identifying the gap that currently exists between industry needs and programme outcomes of quantity surveying degree programmes, which could be invaluable when improving quantity surveying education in SL.

An Information Manager (IM) has an exclusive and unique role to play in a project. Yet, the competencies expected of an IM have not been adequately defined. The other professionals in the construction industry define this role in their own way ignoring the industry needs. Thus, this study aimed to define the competencies expected of an IM. To achieve this, the tasks expected of an IM were first determined through a desk study using BIM guidelines. Secondly, an informal public opinion study was carried out using online data sources. The collected data were then analyzed using NVIVO software to outline the competencies required of an IM for each task. Finally, an expert interview survey was carried out to validate the outcomes of the study. The findings revealed that an IM must be a construction professional well-versed in construction technology and contract administration and that the IM must be competent in using Building Information Modeling (BIM) workflows/software. The study contributes to knowledge by identifying the gap that currently exists between industry needs and the performance of professionals working as IMs, which would be invaluable when designing a curriculum for a course on information management within a BIM environment.

The journal retracts the article titled “Digital Twin Smart Cities for Disaster Risk Management: A Review of Evolving Concepts”