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Heating, ventilating, air-conditioning and cooling (HVAC) systems are crucial in daily health-care facility services. Design-related defects can lead to maintenance issues, causing service disruptions and cost overruns. These defects can be avoided if a link between the early design stages and maintenance feedback is established. This study aims to use experts’ experience in HVAC maintenance in health-care facilities to list and evaluate the risk of each maintenance issue caused by a design defect, supported by the literature.

Following semistructured interviews with experts, 41 maintenance issues were identified as the most encountered issues. Subsequently, a survey was conducted in which 44 participants evaluated the probability and impact of each design-caused issue.

Chillers were identified as the HVAC components most prone to design defects and cost impact. However, air distribution ducts and air handling units are the most critical HVAC components for maintaining healthy conditions inside health-care facilities.

The unavailability of comprehensive data on the cost impacts of all design-related defects from multiple health-care facilities limits the ability of HVAC designers to furnish case studies and quantitative approaches.

This study helps HVAC designers acquire prior knowledge of decisions that may have led to unnecessary and avoidable maintenance. These design-related maintenance issues may cause unfavorable health and cost consequences.

The proposed model aims to consider the flying hours as a criterion to initiate maintenance operation. Based on this condition, aircraft must be checked before flying hours threshold is met. After receiving maintenance service, the model ignores previous flying hours and the aircraft can keep on flying until the threshold value is reached again. Moreover, the model considers aircraft age and efficiency to assign them to flights.

The aircraft maintenance routing problem (AMRP), as one of the most important problems in the aviation industry, determines the optimal route for each aircraft along with meeting maintenance requirements. This paper presents a bi-objective mixed-integer programming model for AMRP in which several criteria such as aircraft efficiency and ferrying flights are considered.

As the solution approaches, epsilon-constraint method and a non-dominated sorting genetic algorithm (NSGA-II), including a new initializing algorithm, are used. To verify the efficiency of NSGA-II, 31 test problems in different scales are solved using NSGA-II and GAMS. The results show that the optimality gap in NSGA-II is less than 0.06%. Finally, the model was solved based on real data of American Eagle Airlines extracted from Kaggle datasets.

The authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.

This paper aims to develop a dynamic civil facility degradation prediction model to forecast the reliability performance tendency and remaining useful life under imperfect maintenance based on the inspection records and the maintenance actions.

A real-time hidden Markov chain (HMM) model is proposed in this paper to predict the reliability performance tendency and remaining useful life under imperfect maintenance based on rare failure events. The model assumes a Poisson arrival pattern for facility failure events occurrence. HMM is further adopted to establish the transmission probabilities among stages. Finally, the simulation inference is conducted using Particle filter (PF) to estimate the most probable model parameters. Water seals at the spillway hydraulic gate in a Taiwan's reservoir are used to examine the appropriateness of the approach.

The results of defect probabilities tendency from the real-time HMM model are highly consistent with the real defect trend pattern of civil facilities. The proposed facility degradation prediction model can provide the maintenance division with early warning of potential failure to establish a proper proactive maintenance plan, even under the condition of rare defects.

This model is a new method of civil facility degradation prediction under imperfect maintenance, even with rare failure events. It overcomes several limitations of classical failure pattern prediction approaches and can reliably simulate the occurrence of rare defects under imperfect maintenance and the effect of inspection reliability caused by human error. Based on the degradation trend pattern prediction, effective maintenance management plans can be practically implemented to minimize the frequency of the occurrence and the consequence of civil facility failures.

The United Nations has demonstrated a commitment to preserving the ecosystem through its 2030 sustainable development goals agenda. One crucial objective of these goals is to promote a healthy ecosystem and discourage practices that harm it. Building materials production significantly contributes to the emissions of greenhouse gases. This poses a threat to the ecosystem and prompts a growing demand for sustainable building materials (SBMs). The purpose of this study is to investigate SBMs to determine their utilization in construction operations and the potential impact their application could have on construction productivity.

A systematic review of the existing literature in the field of SBMs was conducted for the study. The search strings used were “sustainable” AND (“building” OR “construction”) AND “materials” AND “productivity”. A total of 146 articles were obtained from the Scopus database and reviewed.

Bio-based, cementitious and phase change materials were the main categories of SBMs. Materials in these categories have the potential to substantially contribute to sustainability in the construction sector. However, challenges such as availability, cost, expertise, awareness, social acceptance and resistance to innovation must be addressed to promote the increased utilization of SBMs and enhance construction productivity.

Many studies have explored SBMs, but there is a dearth of studies that address productivity in the context of SBMs, which leaves a gap in understanding. This study addresses this gap by drawing on existing studies to determine the potential implications that using SBMs could have on construction productivity.

The purpose of this paper is to summarize the status and characteristics of rail technology of high-speed railway in China, and point out the development direction of rail technology of high-speed railway.

This study reviews the evolution of high-speed rail standards in China, comparing their chemical composition, mechanical attributes and geometric specifications with EN standards. It delves into the status of rail production technology, shifts in key performance indicators and the quality characteristics of rails. The analysis further examines the interplay between wheels and rails, the implementation of grinding technology and the techniques for inspecting rail service conditions. It encapsulates the salient features of rail operation and maintenance within the high-speed railway ecosystem. The paper concludes with an insightful prognosis of high-speed railway technology development in China.

The rail standards of high-speed railway in China are scientific and advanced, highly operational and in line with international standards. The quality and performance of rail in China have reached the world’s advanced level. The 60N profile guarantees the operation quality of wheel–rail interaction effectively. The rail grinding technology system scientifically guarantees the long-term good service performance of the rail. The rail service state detection technology is scientific and efficient. The rail technology will take “more intelligent” and “higher speed” as the development direction to meet the future needs of high-speed railway in China.

The development direction of rail technology for high-speed railway in China is defined, which will promote the continuous innovation and breakthrough of rail technology.

Water pipes degrade over time for a variety of pipe-related, soil-related, operational, and environmental factors. Hence, municipalities are necessitated to implement effective maintenance and rehabilitation strategies for water pipes based on reliable deterioration models and cost-effective inspection programs. In the light of foregoing, the paramount objective of this research study is to develop condition assessment and deterioration prediction models for saltwater pipes in Hong Kong.

As a perquisite to the development of condition assessment models, spherical fuzzy analytic hierarchy process (SFAHP) is harnessed to analyze the relative importance weights of deterioration factors. Afterward, the relative importance weights of deterioration factors coupled with their effective values are leveraged using the measurement of alternatives and ranking according to the compromise solution (MARCOS) algorithm to analyze the performance condition of water pipes. A condition rating system is then designed counting on the generalized entropy-based probabilistic fuzzy C means (GEPFCM) algorithm. A set of fourth order multiple regression functions are constructed to capture the degradation trends in condition of pipelines overtime covering their disparate characteristics.

Analytical results demonstrated that the top five influential deterioration factors comprise age, material, traffic, soil corrosivity and material. In addition, it was derived that developed deterioration models accomplished correlation coefficient, mean absolute error and root mean squared error of 0.8, 1.33 and 1.39, respectively.

It can be argued that generated deterioration models can assist municipalities in formulating accurate and cost-effective maintenance, repair and rehabilitation programs.

The case study has been prepared for management students/business executives to understand electric vehicle (EV) business, business environment, industry competition and strategic planning and strategy implementation.

The size of the Indian passenger vehicle market was valued at US$32.70bn in 2021; it was projected to touch US$54.84bn by 2027 with a Compound Annual Growth Rate (CAGR) of more than 9% during the period 2022–2027. The passenger vehicle industry, a part of the overall automotive industry, was expected to grow at a rapid pace, as the Indian economy was rising at the fastest rate. However, the Government of India (GoI) had put a condition on the growth scenario by mandating that 100% of vehicles produced would be EVs by 2030. Tata Motors (TaMo), a domestic player in the market, had been facing a challenging competitive environment. Although it had been incurring losses, it had successfully ventured into the EV business. TaMo had taken advantage of the first mover by creating an electric mobility business vertical to enable the company to deliver on its aspiration of providing innovative and competitive e-mobility solutions. TaMo leadership had been putting efforts to scale up the electric mobility business, thus, contributing to GoI’s plan for electric mobility. Shailesh Chandra, president of electric mobility business, had a big task in hand. He had to scale up EV production and sales despite insufficient infrastructure for charging and shortages of electronic components for manufacturing.

The case study has been prepared for management students/business executives for strategic management class. It is recommended that the case study is distributed in advance so that the students can prepare well in advance for classroom discussions. Groups will be created to delve into details for a specific question. While one group will make their presentation, the other groups will question the solution provided and give suggestions.

Teaching notes are available for educators only.

CSS 11: Strategy.

The purpose of this study is to focus on structured data provision and asset information model maintenance and develop a data provenance model on a blockchain-based digital twin smart and sustainable built environment (DT) for predictive asset management (PAM) in building facilities.

Qualitative research data were collected through a comprehensive scoping review of secondary sources. Additionally, primary data were gathered through interviews with industry specialists. The analysis of the data served as the basis for developing blockchain-based DT data provenance models and scenarios. A case study involving a conference room in an office building in Stockholm was conducted to assess the proposed data provenance model. The implementation utilized the Remix Ethereum platform and Sepolia testnet.

Based on the analysis of results, a data provenance model on blockchain-based DT which ensures the reliability and trustworthiness of data used in PAM processes was developed. This was achieved by providing a transparent and immutable record of data origin, ownership and lineage.

The proposed model enables decentralized applications (DApps) to publish real-time data obtained from dynamic operations and maintenance processes, enhancing the reliability and effectiveness of data for PAM.

The research presents a data provenance model on a blockchain-based DT, specifically tailored to PAM in building facilities. The proposed model enhances decision-making processes related to PAM by ensuring data reliability and trustworthiness and providing valuable insights for specialists and stakeholders interested in the application of blockchain technology in asset management and data provenance.

This paper proposes selecting a construction project portfolio in the context of a public institution, which makes it possible to assess quantitative and qualitative criteria, thereby meeting the needs of the institution and the existing constraints.

The research design follows a framework using technique for order preference by similarity to ideal solution (TOPSIS) associated with integer linear programming.

The method involves a flow of assessments allowing criteria and weights to be elicited where outcomes are based on the experts' intra-criteria assessment of alternatives and decision-makers' inter-criteria assessment. This is of utmost interest to public organizations, where selections must result in benefits and lower costs, integrating the experts' technical and management perspectives.

Public institutions are characterized by having limited financial and personnel resources for project development despite having a high demand for requests not associated with profits, making it essential to have a framework that enables using multiple criteria to better evaluate the benefits related to these decisions.

The main contributions of this article are: (1) the proposition of a framework for selecting construction project portfolios considering the organization's strategic needs; (2) identifying quantitative and qualitative assessment criteria for project selection; (3) integrating TOPSIS with an optimization process for selecting the construction project portfolios and (4) providing a structured decision process for selecting the portfolio that best represents the interests of the institution within its limited resources and personnel.

To come up with a prudent decision on the installation of an appropriate green wall (GW) on buildings, this study presents a novel decision-making algorithm. The proposed algorithm considers the importance of barriers hampering GW adoption, as well as their relationships with regard to different types of GWs existing in a contextual setting.

The proposed methodological approach is based on the integration of qualitative and quantitative techniques by employing focus group discussion, fuzzy-based best-worst method and fuzzy TOPSIS.

Based on the experiences of qualified experts involved in related projects in Hong Kong, the following conclusions are drawn: (1) cost, installation and maintenance-related barriers are perceived to have the highest importance, (2) modular living wall system is the most suitable GW system for the context of Hong Kong and (3) existing barriers are found to have a pivotal role in the ranking of the most suitable GW systems.

The findings provide valuable insight not only for policymakers and stakeholders, but also for establishing a methodological approach that can assist decision-makers in identifying the most beneficial GW system rather than the most applicable one. This could have significant implications and introduce potential changes to the common way of practice within the industry and lay the foundation for wider adoption of GW.

While previous studies have investigated the sustainability-related issues of GW façade applications, the current body of knowledge is deprived of a comprehensive methodological approach for the selection of the most suitable GW systems.