Janaka Y. Ruwanpura

This paper compares national hydrogen (H₂) infrastructure plans in Canada, the United States (the USA), Singapore, and Sri Lanka, four countries with varying geographic and economic outlooks but shared targets for reaching net-zero emissions by 2050. It examines how each country approaches hydrogen production, pipeline infrastructure, policy incentives, and international collaboration. Canada focuses on large-scale hydrogen production utilizing natural resources and retrofitted natural gas pipelines supplemented by carbon capture technology. The USA promotes regional hydrogen hubs with federal investment and intersectoral collaboration. Singapore suggests an innovation-based, import-dominant strategy featuring hydrogen-compatible infrastructure in a land-constrained region. Sri Lanka maintains an import-facilitated, pilot-scale model facilitated by donor funding and foreign collaboration. This study identifies common challenges such as hydrogen embrittlement, leakages, and infrastructure scalability, as well as fundamental differences based on local conditions. Based on these findings, strategic frameworks are proposed, including scalability, adaptability, partnership, policy architecture, digitalization, and equity. The findings highlight the importance of localized hydrogen solutions, supported by strong international cooperation and international partnerships.

Purpose: Based on the existing provisions/operations of tsunami warning in the Indian Ocean, authors observed that detection as well as arrival time estimations of regional tsunami service providers (RTSPs) could be improved. In particular, the detection mechanisms have been eccentrically focussed on Sunda and Makran tsunamis, although tsunamis from Carlsberg ridge and Chagos archipelago could generate devastating tsunamis for which inadequate provisions exist for detection and arrival time/wave height estimation. RTSPs resort to assess estimated arrival time/wave heights from a scenario-based, pre-simulated database. These estimations in terms of Sri Lanka have been found inconsistent. In addition, current warning mechanism poorly manages non-seismic tsunamis. Thus, the purpose of this study is to investigate these drawbacks and attempt to carve out a series of suggestions to improve them.Design/methodology/approach: The work initiated with data retrieved from global earthquake and tsunami databases, followed by an estimation of probabilities of tsunamis in the Indian Ocean with particular emphasis on Carlsberg and Chagos tsunamis. Second, probabilities of tsunami detection in each sub-region have been estimated with the use of available tide gauge and tsunami buoy data. Third, the difficulties in tsunami detection in the Indian Ocean are critically assessed with case studies, followed by recommendations to improve the detection and warning.Findings: Probabilistic estimates show that given the occurrence of a significant earthquake, both Makran and Carlsberg/Chagos regions possess higher probabilities to harbour a tsunami than the Sunda subduction zone. Meanwhile, reliability figures of tsunami buoys have been declined from 79-92 to 68-91 per cent over the past eight years. In addition, a Chagos tsunami is left to be detected by only one tide gauge prior to it reaching Sri Lankan coasts.Research limitations/implications: The study uses an averaged tsunami speed of 882 km/h based on 2004 Asian tsunami. However, using exact bathymetric data, Tsunamis could be simulated to derive speeds and arrival times more accurately. Yet, such refinements do not change the main derivations and conclusions of this study.Practical implications: Tsunami detection and warning in the Indian Ocean region have shown room for improvement, based on the inadequate detection levels for Carlesberg and Chagos tsunamis, and inconsistent warnings of regional tsunami service providers. The authors attempted to remedy these drawbacks by proposing a series of suggestions, including a deployment of a new tsunami buoy south of Maldives, revival of offline buoys, real-time tsunami simulations and a strategy to deal with landslide tsunamis, etc.Social implications: Indian Ocean is prone to mega tsunamis as witnessed in 2004. However, more than 50 per cent of people in the Indian Ocean rim countries dwell near the coast. This is verified with deaths of 227,898 people in 14 countries during the 2004 tsunami event. Thus, it is of paramount importance that sufficient detection levels are maintained throughout the Indian Ocean without being overly biased towards Sunda tsunamis. With respect to Sri Lanka, Makran, Carlesberg or Chagos tsunamis could directly hit the most populated west coast and bring about far worse repercussions than a Sunda tsunami.Originality/value: This is the first instance where the threats from Carlesberg and Chagos tsunamis to Sri Lanka are discussed, probabilities of tsunamis are quantified and their detection levels assessed. In addition, reliability levels of tsunami buoys and tide gauges in the Indian Ocean are recomputed after eight years to discover that there is a drop in reliability of the buoy data. The work also proposes a unique approach to handle inconsistencies in the bulletins of regional tsunami service providers, and to uphold and improve dwindling interest on tsunami buoys.

This manuscript provides a robust framework for the extraction of common structural components, such as columns, from terrestrial laser scanning point clouds acquired at regular rectangular concrete construction projects. The proposed framework utilizes geometric primitive as well as relationship-based reasoning between objects to semantically label point clouds. The framework then compares the extracted objects to the planned building information model (BIM) to automatically identify the as-built schedule and dimensional discrepancies. A novel method was also developed to remove redundant points of a newly acquired scan to detect changes between consecutive scans independent of the planned BIM. Five sets of point cloud data were acquired from the same construction site at different time intervals to assess the effectiveness of the proposed framework. In all datasets, the framework successfully extracted 132 out of 133 columns and achieved an accuracy of 98.79% for removing redundant surfaces. The framework successfully determined the progress of concrete work at each epoch in both activity and project levels through earned value analysis. It was also shown that the dimensions of 127 out of the 132 columns and all the slabs complied with those in the planned BIM.

Automated segmentation of planar and linear features of point clouds acquired from construction sites is essential for the automatic extraction of building construction elements such as columns, beams and slabs. However, many planar and linear segmentation methods use scene-dependent similarity thresholds that may not provide generalizable solutions for all environments. In addition, outliers exist in construction site point clouds due to data artefacts caused by moving objects, occlusions and dust. To address these concerns, a novel method for robust classification and segmentation of planar and linear features is proposed. First, coplanar and collinear points are classified through a robust principal components analysis procedure. The classified points are then grouped using a new robust clustering method, the robust complete linkage method. A robust method is also proposed to extract the points of flat-slab floors and/or ceilings independent of the aforementioned stages to improve computational efficiency. The applicability of the proposed method is evaluated in eight datasets acquired from a complex laboratory environment and two construction sites at the University of Calgary. The precision, recall, and accuracy of the segmentation at both construction sites were 96.8%, 97.7% and 95%, respectively. These results demonstrate the suitability of the proposed method for robust segmentation of planar and linear features of contaminated datasets, such as those collected from construction sites.

Purpose: This paper aims to apply network modelling and simulation methods to critically analyse the tornado detection, warning and communication system in the Canadian Prairies. Design/methodology/approach: The simulation results of the developed network illustrate the role of collaborating partners and provide a probabilistic representation of the overall time from tornado detection to warning issuance. Furthermore, the total time from the warning issuance to when evacuation is complete is analysed by combining the time distribution of the network and the evacuation time distribution, which is developed based on survey data. Findings: A set of recommendations are offered as guidelines for consideration and possible adoption by collaborating partners who are involved at different stages of the detection, warning, communication and evacuation process. Practical implications: The research contributes to a deeper understanding of the pre-disaster phase of tornadoes by providing an overall analysis that spans different areas under the general umbrella of disaster mitigation. Social implications: This research paper helps the community to work together in developing mitigation measures to enhance social values and benefits. Originality/value: This paper shows how activity network modelling and simulation methods, which are normally applied in construction management, can be used to analyse the overall process from tornado detection to the warning issuance.

Productivity benchmarking has been an elusive element for construction companies around the world. With the advent of the Internet, many companies have attempted Web-based database and benchmarking efforts since the late 1990s. However, an industry consensus on a collaborative database and benchmarking tool has yet to be established. New opportunities are being generated for efficient and free database services that are challenging the established paradigm. Google fusion tables (GFT) Web application was launched in 2009 and has been evolving ever since release. GFT provides a user-friendly, collaborative, and interactive database tool that is available to the public on an international scale at no charge. This tool provides users with options to publish their data on another site, make it publically available and discoverable by search engines, or keep it private. The research presented in this paper aims to develop a pilot database using GFT for the storage and management of data from work-sampling observations for designated work trades on a project located in Calgary, Alberta, Canada. Data were uploaded to the Google cloud to commence the pilot database. The collected data were complemented further by the tool's charts and shared with participating parties. The application of GFT as a data management system was tested against a selected company's extranet and individually assessed through the survey of the users of the pilot program.

Efficient onsite data acquisition of a construction project enables the comparison of the actual state of the project to the as-plan state so that potential delays can be identified early within the project life cycle. Traditionally, onsite data are collected manually, a time consuming, costly and error-prone task, and therefore not justifiable in modern construction management. To overcome the challenges corresponding to such manual approaches, the application of automated progress monitoring of construction sites has attracted the attention of researchers. To enable an effective application, it is necessary to evaluate the reliability of the available technologies in collecting onsite data. In this paper, a qualitative evaluation of the applicability of the state-of-the-art automated progress monitoring technologies, namely camera, LiDAR, and 3D range imaging, has been carried out. A set of experiments has been carried out to compare the time of data collection for each technology. LiDAR provides the most accurate 3D estimates. The time of data collection of the Leica HDS6100 laser scanner is shown to be seven times faster than that of the DSLR camera in an indoor construction site simulated laboratory. However, the cost of LiDAR devices is the major economical drawback of the technology.

Construction productivity improvement has become a key area of focus among academia and industry over the last decade attributable to its strong potential in benefitting the construction industry. Despite its high impact on the construction industry, productivity improvement is still an area in which much research work needs to be done to explore its true potential in a practical industry context. Today’s construction industry seems to adopt productivity improvement initiatives to gain a competitive edge in the global market place; however, systematization of these approaches is still an area of concern. This paper discusses a framework for the implementation of productivity improvement activities on a construction site, making the process more systematic, accountable, and sustainable with the creation of the construction productivity improvement officer (CPIO), a dedicated position, on construction sites.

The Leadership in Energy and Environmental Design (LEED) Green Building Rating System, developed by the U.S. Green Building Council (USGBC) and later adopted by the Canada Green Building Council (CaGBC), has been widely accepted by public and private owners. Nevertheless it has been proven that adherence to LEED requirements has various effects on construction worker performance and productivity, construction cost and schedule and the environment. As a result, this limits the extent to which industry professionals apply the LEED principles, and are faced with difficulties in selecting the credits to be implemented in LEED certified projects. Therefore, there is a growing need to improve the sustainable goals by optimizing the LEED credit selection process to gain higher efficiency and productivity, which would result in increased popularity among contractors and design consultants. It has been identified that each LEED credit would have a different impact on cost, schedule, environment and the construction productivity. A considerable amount of literature has been published regarding these impact areas and it is clear that the impact is different from each credit and each project. However, very little research has been carried out that considers the combined effect of the identified factors. This paper describes the development of a multi-criteria prediction model that has the ability to model phenomena with significant uncertainty in inputs and multiple criteria such as project cost variation, the environmental impact, the impact on schedule and the impact on construction productivity. This simulation tool can be used by the design team at an early stage of the design process to optimise the benefits and minimise the negative impacts of LEED implementation in a new construction project.