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Deterioration and aging associated with building assets are becoming major concerns in most countries as their building portfolios continue to increase and expand. Healthcare facilities are a special case of building assets that inherit a significant criticality and complexity within its operation and maintenance regimes which makes monitoring the assets' condition and forecasting their life expectancy two of the most essential functions in a healthcare environment. In this paper, a stochastic deterioration prediction approach was developed to model and estimate the degradation of elevators systems within hospital building environments due to their importance to the continuity of the hospital mission and services. Different probability distributions were fitted using historical condition data and the performance of different distributions was then compared utilizing the Anderson-Darling test. Parameters of the best distribution were thus found using maximum likelihood estimate. The developed model is expected to aid decision makers in improving the planning process for their maintenance and rehabilitation programs and to efficiently conduct proactive maintenance activities in a timely manner which helps ensure the sustainability of hospital operation.

In recent decades, closed circuit television (CCTV) has been the most used tool for visually inspecting the internal condition of pipelines. However, CCTV inspection requires long videos to...

Urban railways play a critical role in the daily life of citizens. However, extensive deterioration of mostly aged systems complicates the management of this type of infrastructure in copin...

A significant share of the total primary energy belongs to buildings. In many buildings, the energy usage can be significantly reduced by adopting passive strategies. These methods might no...

Metro systems play a crucial role in the movement of millions of passengers worldwide as commuters rely on a fast, reliable, and convenient underground railway for their daily transportation. Howev...

In this paper, a combined biomass-geothermal system, intended to supply heat in low enthalpy areas with an extremely cold climate, is optimized based on a nonlinear optimization methodology. A Multiple Criteria Decision-Making technique is coupled with a two-step optimization to achieve the most exploitable energy with the least pollution and cost possible. Three nonlinear objective functions for optimization with three criteria for decision-making were used to minimize the heat generation cost and pollution for a modeled building in Kuujjuaq, Canada. The biomass-geothermal system is split into two parts, surface, and subsurface parts. Twelve scenarios, including three wood pellet types, in four distance ranges from pellet mills, are first defined. Then, via modeling a building for heat demand analysis, the required heat is yielded. Afterward, in the first step of optimization, the cost and pollution functions for surface parts are developed and optimized using the genetic algorithm and screened by the MCDM technique, called TOPSIS, to size the biomass and geothermal subsystems. In the second step, using the sizing from the first step as a constraint, the cost of the geothermal ground heat exchanger is minimized. Twelve scenarios are optimally configured in this way with minimum cost and pollution in relation to operational parameters, such as utilization time and rated powers. The research proposes a methodology that sizes the biomass geothermal (bio-geo) system and can be extended to other technologies, such as turbines, energy storages, or fuel. Furthermore. It provides a correlation between cost and heat generation from biomass-geothermal systems for Kuujjuaq, Canada, and twelve optimal scenarios with system operating parameters. A basis for system sizing and system selection for baseload and peak demand shaving is also considered. Geothermal- and biomass-rated capacities vary with scenarios from 44% to 56% of the total rated capacity.

During the normalized phase of COVID-19, droplets or aerosol particles produced by infected personnel are considered as the potential source of infection with uncertain exposure risk. As such, in densely populated open spaces, it is necessary to adopt strategies to mitigate the risk of infection disease transmission while providing sufficient ventilation air. An example of such strategies is use of physical barriers. In this study, the impact of barrier heights on the spread of aerosol particles is investigated in an open office environment with the well-designed ventilation mode and supply air rate. The risk of infection disease transmission is evaluated using simulation of particle concentration in different locations and subject to a number of source scenarios. It was found that a barrier height of at least 60 cm above the desk surface is needed to effectively prevent the transmission of viruses. For workstations within 4 m from the outlet, a 70 cm height is considered, and with a proper ventilation mode, it is shown that the barriers can reduce the risk of infection by 72%. However, for the workstations further away from the outlet (beyond 4 m), the effect of physical barrier cannot be that significant. In summary, this study provides a theoretical analysis for implementing physical barriers, as a low-cost mitigation strategy, subject to various height scenarios and investigation of their effectiveness in reducing the infection transmission probability.

The development of decision support tools for use in the maintenance management and renewal prioritization of healthcare facility assets is considered a highly challenging task due to the multiplicity of uncertainties and subjectivity levels available in such a decision-making process. Accordingly, this study utilizes a combination of Neutrosophic logic, Analytic Network Process (ANP) and Multi-Attribute Utility Theory (MAUT) to reduce the subjectivity pertaining to expert-driven decisions and produce a reliable ranking of hospital building assets based on their variable criticality levels and performance deficiencies. This is further integrated with the novel use of machine learning algorithms in this field, namely: Decision Trees, K-Nearest Neighbors and Naive Bayes to automate the priority setting process and make it reproducible diminishing the need for additional expert judgments. The developed model was applied to Canadian healthcare facilities, and its corresponding predictive performance was validated by means of comparison against a previously established model, and its excelling capability was clearly demonstrated. Accordingly, the developed integrated framework is expected to aid in creating a consistent, unbiased and automated prioritization scheme for hospital asset renewals, which in turn is expected to contribute to an efficient, informed and sound resources allocation process.

Purpose The complexity and criticality of healthcare services highlight the importance of maintenance management function in healthcare facilities. The purpose of this paper is to review the literature on maintenance management of healthcare facilities and hospital buildings to provide an organized literature review and identify gaps from the perspective of research and practice. Design/methodology/approach The paper categorizes the literature and adopts a review hierarchy according to maintenance management functions in hospital buildings. It explores the impact of those functions on the performance of maintenance activities in hospitals. Furthermore, it examines the role of information technology and automated decision support systems in facilitating hospital maintenance management functions and performance. Findings Literature on maintenance management in healthcare facilities and hospital buildings has so far been very limited. Recently published literature focusing on healthcare facilities management and its maintenance management functions is classified into various areas and sub-areas. The paper highlights gaps in the literature and suggests avenues for future research and improvements. Originality/value The paper contains a comprehensive listing of publications and their classifications according to various attributes. It will be useful for researchers, maintenance managers, practitioners and stakeholders concerned with facility management of hospital buildings.

Over 10,000 failures have occurred in US oil and gas pipelines in the past 15 years, highlighting the significance of safety measures for such facilities. Various models have been proposed ...

This study proposes a whole life asset-supply chain optimization model for integration of biomass boilers into non-domestic (non-residential) buildings, under a renewable heat incentive scheme in the UK. The proposed model aims at identifying the optimal energy generation capacities and schedules for biomass and backup boilers, along with the optimal levels of biomass ordering and storage. The sensitivity of these decisions are then analyzed subject to changes in source, types and pricing of biomass materials as well as the choice of technologies and their cost and operational performance criteria. The proposed model is validated by applying it to a case study scenario in the UK. The results indicate that a Renewable Heat Incentive scheme could incentivize the adoption of biomass boilers, with a 3 to 1 ratio for biomass and backup boilers’ utilization. As such, the findings from this study will be useful for industry managers, tasked with the decision of which biomass boiler system to utilize, considering the support from RHI. On the other hand, it is shown that RHI does not provide an encouragement for efficiency when it comes to the choice of biomass technologies and fuels. This presents itself as a major implication for the success and sustainability of the UK government’s renewable heat incentive scheme.

Water reuse networks have been emerging globally for the last 50 years. This article reviews the economic, social and environmental issues related to implementing water reuse networks in cities. This is reflecting the fact that globally many cities are categorised as water scarce areas, where there is growing imbalance between water demand and availability. In this sense, there is a need for sustainable water supply solutions in the imminent future to provide and maintain service reliability, particularly in the face of climate change. To demonstrate the sustainability implications of water reuse practices, we review a case study in London, UK.

The complex, uncertain, and dynamic nature of the maintenance management environment is a source of concern to facility managers in hospitals due to the unexpected failure of building components, daily arrival of maintenance orders, and changes in schedule. In such circumstances, centralized systems become far-fetched because of their top-down approach which lacks a feedback mechanism and ignores new information. Therefore, to address any change, centralized systems have to be reformulated making it impractical, short-sighted, and problematic to adopt them in hospitals. As such, the use of centralized systems can lead to financial loss and dissatisfaction of patients. It, therefore, becomes necessary to establish a distributed maintenance management system to support the facility managers’ making-decision process. Multi-agent facility management system (MAFMS) was conceptually designed. This design employed Unified Modeling Language (UML) diagrams that illustrate the specific agents of the system and how these agents interact with each other. Maintenance data of a hospital building was used to initiate the multi-agent simulation for workflow management. The simulation results show the benefits of the proposed system, to reduce the response time to maintenance requests compared to the current maintenance system.

A large portion of natural gas is transferred through transmission pipelines. For these facilities, in the United States, external corrosion has led to more than 1700 failures in the past decades, causing a property damage of approximately $189 M. Such numbers highlight the importance of maintaining such facilities in safe conditions to postpone corrosion failure. Given such criticality, addressing pipeline availability is of high importance from economic consequences perspective for a national economy. However, most of the developed methods merely rely on considering costs, reliability or condition levels as maintenance decision criteria and ignore the importance of continuity of operation and pipeline availability. In this research, a maintenance planning framework is proposed for external corrosion of gas transmission pipelines through an availability-centered reliability-based maintenance planning procedure. This framework is based on the pipeline reliability profile obtained from a Monte Carlo simulation. This simulation is carried on a previously developed failure prediction model for gas transmission pipelines buried in the Great Plains region of the U.S. and considers failure uncertainties. In addition, a discrete event simulation (DES) approach is proposed to assess an availability-cost indicator of different maintenance scheduling alternatives for the case study of a 24-inch gas pipeline. This research reveals that a combination of wrap and replacement maintenance actions at the service life of 30.1 and 40.5 years respectively, is the most effective maintenance alternative in terms of improvement of availability per unit cost for the presented case study. This framework can help pipeline professionals in maintaining such facilities by considering their criticality using an availability-based maintenance scheduling approach. Such method can complement the conventional cost-based industry practices.

Canada's railway industry has invested more than $20 billion since 1999 to improve the safety of users as a top priority. Although the accident rates have decreased, the users' safety and comfort could still be impacted by vehicle's vibration, air quality, and noise levels. In this regard, comfort and safety are mutually related aspects; worsening of air quality could compromise users' safety from a respiratory health perspective. Further, noise, level of lighting, thermal condition and movement could contribute to hearing problems, visual impairments, bacteria growth, and nausea, respectively. Management of urban transit systems should ensure a convenient transit service with adequate levels of ride and quality (time and cost) as well as users' comfort and safety. This article proposes an approach for measurement of riders' comfort (as it relates to safety) in terms of humidity, temperature, vibration, concentration of CO2, noise, and lighting level based on a set of maximum thresholds identified with respect to public health guidelines. This enables the use of a weighting method to establish an overall network comfort level. Such an index can be used to direct the allocation of investments for improvement of comfort as well as ridership. A case study of several lines in Sydney trains network is presented to illustrate the applicability and usefulness of the proposed approach. Data were collected and analyzed using low-cost technologies such as smart phones and mobile sensors. The comfort factors are measured and reported per train line and then combined to generate an overall comfort index.

Transit agencies concentrate their efforts to satisfy most commuters and convince them to abandon the use of the private car in daily trips, which is a key element in promoting environmentally sustainable cities. Travel time and cost have been widely investigated as travelers’ satisfaction factors, while human aspects such as comfort are mostly neglected. Management of urban transit systems should ensure a convenient transit service with adequate levels of ride and quality (time and cost) as well as users’ comfort, health and safety to achieve sustainable societies. This study proposes an approach to quantitatively measure railway riders’ comfort in terms of humidity, temperature, vibration, the concentration of CO2, noise, and lighting. Such indexes can be used to direct the allocation of investments for improvement of comfort in urban transit infrastructures as well as capture reality in demand prediction modeling. A case study of several lines in the Montreal metro network is presented to illustrate the applicability and usefulness of the proposed approach. The newer trains in the Montreal metro are an improvement over older trains when it comes to rider comfort; however, they could still be better, especially when it comes to auditory comfort.

This article reviews the literature on supervisory elements in distance postgraduate research programmes leading to a master's or doctoral degree. The authors first identify and review the main supervisory challenges from the perspectives of both supervisor and supervisee. This paves the path to investigate and categorise the strategies that have been put into practice in order to address these challenges. This is a first attempt to provide a structured categorisation of the strategies and solutions tailored to address these challenges. Through linking the challenges to strategies, the authors will emphasise the elements of a successful distance postgraduate research supervision experience with a set of guidelines on how to improve the overall learning in this process. The aim of this paper is to conclude by summarising the lessons learned from distance postgraduate research supervision practices and to identify the areas in need of further research.

Reducing dependency on fossil fuels and mitigating their environmental impacts are among the most promising aspects of utilizing renewable energy sources. The availability of various biomass resources has made it an appealing source of renewable energy. Given the variability of supply and sources of biomass, supply chains play an important role in the efficient provisioning of biomass resources for energy production. This paper provides a comprehensive review and classification of the excising literature in modeling of biomass supply chain operations while linking them to the wider strategic challenges and issues with the design, planning and management of biomass supply chains. On that basis, we will present an analysis of the existing gaps and the potential future directions for research in modeling of biomass supply chain operations.

A reverse supply chain, as a post-consumption activity, aims at extracting value from products at the end of their life cycle. It could be comprised of reusing, refurbishing, remanufacturing, and recycling activities. In this paper, we revisit the issue of revenue sharing in reverse supply chains to formulate the decision problems of coordinating parties, manufacturer and retailer. The coordination is formed in a dynamic setting, where a feedback relationship exists between the return incentive policy of the retailer, and the revenue sharing incentive strategy of the manufacturer. To model this process, we have adopted the use of a system dynamics (SD) approach. SD is well suited for studying the behavior of complex systems over time, and where internal feedbacks and delays exist. We first identify Pareto-optimal solutions, for individual players, and from environmental perspectives of landfill diversion and carbon offset. We then focus on revenue sharing, as a widely practiced coordination scheme in reverse supply chains. It resembles a leader–follower (Stackelberg) strategic decision-making game, with an equilibrium solution that yields lower performance for environmental criteria. In this context, we investigate if/how a revenue sharing mechanism can be coordinated to achieve a higher environmental performance.

Water reuse is an obvious and important response to water scarcity in cities. It takes many forms – potable and non-potable, centralised and decentralised, direct and indirect, and planned and unplanned. How different forms of reuse emerge and stabilise depends on technical, economic, social, environmental and political factors, and specific local conditions. This chapter reviews trends in potable and non-potable reuse, including international examples of urban water reuse. The analysis shows that public acceptance, regulation, proven technology and support for innovation are needed to provide the conditions for water reuse systems to function. The diversity of approaches to water reuse in cities indicates that urban water infrastructure is diverging from the twentieth century ideal of a centralised, universal supply of potable water. The different forms of water reuse present specific challenges for regulating and governing water infrastructure that require reform of existing arrangements and new institutions and management strategies.

Retrofitting existing buildings with appropriate green technologies is an important element of strategies to mitigate climate change. The selection of green technologies can be a challenging task, where multiple criteria exist and interrelate. However, it is still common for decisions to be based on a single criterion, such as energy efficiency or cost. This paper aims to evaluate the application of Multi-Criteria Decision Making (MCDM) methods to the selection of green technologies for retrofitting to existing buildings. The paper begins with a review of MCDM methods and the use of these techniques for selecting technologies to retrofit existing buildings. The applicability of Analytical Hierarchy Process (AHP), a widely used MCDM method, is demonstrated through a case study of a building that is part of a university estate. The findings show that AHP can help to formulate the problem, and facilitate the assessment and ranking of retrofitting measures when multiple criteria are jointly considered. We have shown that by considering environmental and economic criteria, control technologies such as variable speed drives in air handling units, rank most highly in this case. It has also been suggested that social criteria, such as occupant satisfaction, should also be considered as part of the sustainability agenda, although this can be more difficult to achieve than consideration of environmental and economic criteria, which are more readily characterised using quantitative data. We conclude by proposing an integrated green technology assessment and selection framework, which is applicable to existing buildings.

A sustainable supply of both energy and water is critical to long-term national security, effective climate policy, natural resource sustainability, and social wellbeing. These two critical resources are inextricably and reciprocally linked; the production of energy requires large volumes of water, while the treatment and distribution of water is also significantly dependent upon energy. In this paper, a hybrid analysis approach is proposed to estimate embodied energy and to perform a structural path analysis of drinking water supply systems. The applicability of this approach is then tested through a case study of a large municipal water utility (city of Kalamazoo) in the Great Lakes region to provide insights on the issues of water-energy pricing and carbon footprints. Kalamazoo drinking water requires approximately 9.2 MJ/m(3) of energy to produce, 30% of which is associated with indirect inputs such as system construction and treatment chemicals.

This study proposes a game-theoretic approach to model and analyze the process of utilizing biomass for power generation considering three players: distributor, facility developer, and participating farmer. We characterize the Nash equilibrium of the sequential game and discuss its features. A special attention is devoted to the analysis of the impact of incentives and initial target on the equilibrium, in which the biomass is part of electricity production.

In this study, an optimality assessment framework is proposed to assist in freight transportation planning with environmental considerations. In doing so, a multiple-objective optimization model with cost and environmental objectives is constructed. Because of the fact that optimization with several conflicting objectives can only provide solutions with degrees of optimality, in this study, alternative solutions of model are examined in terms of a decision-maker's targets along with local and global environmental requirements. In such an optimality assessment, fuzzy set theory is applied to interpret the satisfaction levels of objectives by using the concept of fuzzy membership function to generate the optimality index of alternative solutions. To illustrate the applicability of the proposed approach, a real-world case study will be presented.

Agility is the ability of a project to respond to a changing environment effectively. This may include the capability of a project to be adapted to the dynamism that exists in the stakeholders' needs, technological changes, etc. To assure such a capability, it is necessary to assess the extent of projects' adaptability to change. This could be done by expressing the agility in terms of some quantifiable parameters such as size of the project's organisation, levels of expertise, etc. However, there are uncertainties embedded in measurement of such parameters caused by imprecision and lack of well-defined information, which cannot be well treated by conventional assessment approaches. To address such a complexity, in this article, a decision aid model using fuzzy set theory is proposed for agility assessment of projects. The applicability of the proposed model will be demonstrated by a case study in software development project management.

This study proposes an integrated model for capacity planning in electricity generation. It utilizes a multiple-criteria linear programming to incorporate cost and environmental objectives into the planning. To treat the uncertainties embedded in definition of model parameters, the concept of decision-maker degree of optimism will be used. Optimization of the model provides different planning scenarios. To determine the best compromise plan, a post-optimization assessment based on fuzzy set theory concepts is developed. The proposed methodology is employed for a medium-term capacity planning in Canada's electricity generation sector. The results approve a major capacity growth for natural gas facilities accompanied by retirement of most coal-burning facilities.

Selecting an optimal project delivery system is a critical task that owners should do to ensure project success. This selection is a complex decision-making process. The complexity arises from the uncertain or not well-defined parameters and/or the multiple criteria structure of such decisions. In this study, a decision aid model using the analytical hierarchy process (AHP) coupled with rough approximation concepts is developed to assist the owners. The selection criteria are determined by studying a number of benchmarks. The model ranks the alternative delivery systems by considering both benchmark results and owner's opinion. In interval AHP, an optimization procedure is performed via obtaining the upper and the lower linear programming models to determine the interval priorities for alternative project delivery systems. In cases having incomparable alternatives, which is the most likely case in uncertain decision making, the model uses rough set-based measures to reduce the number of decision criteria to a subset, which is able to fully rank the alternatives. To illustrate the applicability and usefulness of this methodology, a real world case study will be demonstrated.

Water quality management policies, which are proposed to prevent, control, or treat environmental problems related to quality of water, are broad and complex issues. We have various types of water resources, different water uses, and a lot of decision parameters with several levels of decision makers involved. Moreover, there are a lot of strategies and technologies available to be applied for water quality management and so environmental decision makers are required to evaluate and prioritize them in order to choose the best possible plan for each particular problem. To provide a comprehensive but easy to use tool in the assessment and evaluation of water quality policies, the concept of water quality index (WQI) has been developed. Due to the abovementioned complexities, to get this index, there is a need for a methodology to not only structure and identify information relevant to the problem but also to help users reach a decision. Designing a multiple-attribute decision support expert system, which makes expert knowledge available to nonexpert users, can do this. In doing so, we may encounter qualitative or linguistic assessments in the index making process. Thus, fuzzy set theory can be applied to recognize this inherent fuzziness of such a process. Briefly, in this study we propose a fuzzy multiple-attribute decision support expert system to compute the water quality index and to provide an outline for the prioritization of alternative plans based on the amount of improvements in WQI. At the end, applicability and usefulness of the proposed methodology is revealed by a case study.

This article will review the issues surrounding the use of humor as an informal teaching method in higher education lecturing. The impact and usefulness of humor, from both a teacher’s and a student’s perspective, will be investigated. The aim is to classify the challenges and limitations of using humor in classrooms and to investigate and categorize the strategies that have been put into practice in order to approach humor in lecturing. This could serve as a set of guidelines on if/how humor can be used to improve the overall learning experience in classrooms.

As declared by UNESCO, Qanats are considered as a great human heritage. For many centuries, they presented a rational way of groundwater management in arid rural areas. This paper aims at revisiting this ancient water supply system reviewing its structure and characteristics including construction and operational issues. On that basis, we highlight some key sustainability perspectives related to this ancient water supply practice. We advocate that this ancient technology should not only be protected as a great human heritage but also be reconsidered as a sustainable way of groundwater management in arid/semi-arid regions.

The implementation of groundwater remediation strategies in contaminated areas includes not only a cost-benefit analysis and an environmental risk assessment but also another type of study called compatibility analysis. A compatibility analysis targets the interactions between remediation technologies and site characteristics, such as the types of active contaminants and their concentrations, soil composition and geological features, etc. The purpose of this analysis is to identify the most compatible remediation plan for the contaminated site. In this paper, we introduce a decision support system for the prioritization of remediation plans based on their estimated compatibility index. As this model receives data in terms of linguistic judgments and experts' opinions, we use fuzzy sets theory to deal with these uncertainties. First, we break down the concept of compatibility into the measurable factors. Then by using a multiple-attribute decision-making (MADM) outline, we compute a factorial, regional and overall compatibility indicator for each plan. Finally, by comparing these generated indicators, we rank the remediation policies.