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This research aims to provide optimization and route safety planning employing the fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) technique.

This research combines the use of graphical, communication tools and simulated models based on building information modeling (BIM) technology and agent-based modeling (ABM) to identify a safe evacuation route. Adopting the multi-criteria decision-making (MCDM) approach, the proposed rescue plan can reduce potential hazards along the evacuation route by selecting a safe route for evacuating residents and entering firefighters to the scene of the incident.

The results show that the use of simulated models along with MCDM methods in the selection of safe routes improves the performance of safe evacuation operations for both relief groups and residents.

The introduced model can improve the performance management of different groups at the time of the incident and reduce casualties and property losses using the information received from sensors at the scene. Moreover, the proposed rescue plan prevents group and individual reactivation at the time of the incident.

Despite many advances in the architecture, engineering and construction (AEC) industry, the number of victims of fire incidents in buildings is increasing compared to other natural disasters. Improving decision management based on effective parameters at the time of incident reduces casualties of residents and rescue workers.

Architecture, engineering and construction (AEC) is an important industry worldwide and one of the largest economic sectors in several developing countries, particularly in Iran. The Iranian AEC sector suffers from low productivity and needs to adopt building information modeling (BIM) to reduce inefficiencies. Therefore, this paper was conducted to identify the BIM barriers and propose practical solutions to overcome them in Iran.

A comprehensive literature review, two rounds of the Delphi technique and semi-structured interviews with 12 Iranian experts in the AEC sector were conducted. The data were analyzed using the mean score, standard deviation and nonparametric tests.

The present study identified 26 BIM barriers in the Iranian AEC community and provided practical strategies for improving BIM adoption. The identified barriers were categorized into six main groups including source barriers, financial barriers, unawareness barriers, organizational barriers, regulatory barriers and market-demand barriers. The main three BIM barriers in Iran were the lack of government intervention, change-resistant and the gap between industry and academia. Kruskal–Wallis tests revealed that there are no statistically significant differences in perceptions of BIM barriers between respondents. The Mann–Whitney test indicated that there is no statistically significant difference in perceptions between engineers and architects except for one.

There are few studies on BIM adoption across developing countries, particularly in Iran. Moreover, the results can also be used in other developing nations with similar conditions.

Recent United Nations Climate Change Conferences recognise extreme climate change of heatwaves, floods and droughts as threatening risks to the resilience and success of public–private partnership (PPP) infrastructure projects. Such conferences together with available project reports and empirical studies recommend project managers and practitioners to adopt smart technologies and develop robust measures to tackle climate risk exposure. Comparatively, artificial intelligence (AI) risk management tools are better to mitigate climate risk, but it has been inadequately explored in the PPP sector. Thus, this study aims to explore the tools and roles of AI in climate risk management of PPP infrastructure projects.

Systematically, this study compiles and analyses 36 peer-reviewed journal articles sourced from Scopus, Web of Science, Google Scholar and PubMed.

The results demonstrate deep learning, building information modelling, robotic automations, remote sensors and fuzzy logic as major key AI-based risk models (tools) for PPP infrastructures. The roles of AI in climate risk management of PPPs include risk detection, analysis, controls and prediction.

For researchers, the findings provide relevant guide for further investigations into AI and climate risks within the PPP research domain.

This article highlights the AI tools in mitigating climate crisis in PPP infrastructure management.

This article provides strong arguments for the utilisation of AI in understanding and managing numerous challenges related to climate change in PPP infrastructure projects.

Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.

The purpose of this study is to understand user perceptions and misconceptions regarding security tools. Security and privacy-preserving tools (for brevity, the authors term them as “security tools” in this paper, unless otherwise specified) are designed to protect the security and privacy of people in the digital environment. However, inappropriate use of these tools can lead to unexpected consequences that are preventable. Hence, it is significant to examine why users do not understand the security tools.

The authors conducted a qualitative study with 40 participants in the USA to investigate the prevalent misconceptions of people regarding security tools, their perceptions of data access and the corresponding impact on their usage behavior and data protection strategies.

While security vulnerabilities are often rooted in people’s internet usage behavior, this study examined user’s mental models of the internet and unpacked how the misconceptions about security tools relate to those mental models.

Based on the findings, this study offers recommendations highlighting the design aspects of security tools that need careful attention from researchers and industry practitioners, to alleviate users’ misconceptions and provide them with accurate conceptual models toward the desired use of security tools.

Demand forecasts are a key component of planning efforts and are crucial for managing core operations. This study aims to evaluate the use of several machine learning (ML) algorithms to forecast demand for residential construction in Jordan.

The identification and selection of variables and ML algorithms that are related to the demand for residential construction are indicated using a literature review. Feature selection was done by using a stepwise backward elimination. The developed algorithm’s accuracy has been demonstrated by comparing the ML predictions with real residual values and compared based on the coefficient of determination.

Nine economic indicators were selected to develop the demand models. Elastic-Net showed the highest accuracy of (0.838) versus artificial neural networkwith an accuracy of (0.727), followed by Eureqa with an accuracy of (0.715) and the Extra Trees with an accuracy of (0.703). According to the results of the best-performing model forecast, Jordan’s 2023 first-quarter demand for residential construction is anticipated to rise by 11.5% from the same quarter of the year 2022.

The results of this study extend to the existing body of knowledge through the identification of the most influential variables in the Jordanian residential construction industry. In addition, the models developed will enable users in the fields of construction engineering to make reliable demand forecasts while also assisting in effective financial decision-making.

While Digitalisation is gaining momentum among practitioners and the scientific world, there is still a struggle to embark on the digitalisation journey successfully. The struggles are more significant for SMEs compared to large companies. Such transformation could face internal resistance, which evokes the need to put it into a socio-technical perspective such as lean. This paper investigates how SMEs could implement digital tools and technologies in their operations.

We relied on a multiple case study design in three SME manufacturing companies in Italy. Based on the experience of those companies, the struggles in the implementation and the lessons learned, we formulate an implementation model of digital tools driven by lean thinking.

Companies tend to implement first digital tools that help with real-time data collection and stress that introducing digital tools becomes challenging without reducing waste in production. The model stresses top management commitment, middle-line involvement and operator training to resist change. All these factors coincide with socio-technical lean bundles developed by seminal works. In addition, the study highlights that financial incentives are not necessarily the common barrier to digital tools implementation in SMEs but rather the cultural aspect.

Our paper enriches the extant body of knowledge by deriving knowledge around digitalisation implementation through lessons learned and corrective actions. It allows managers to benchmark and compare the current state of the implementation process with that of other companies and the one proposed to make corrective actions when necessary.

The question of whether and how communication departments contribute to organizational value creation has rarely been addressed in research. Such evidence is crucial, however, as communications compete internally with other functions (e.g. marketing and human resources (HR)) for budgets and staff. This article fills the gap by applying the business model concept, an established approach from management theory and practice, to communication units.

Based on an interdisciplinary literature review, the authors propose the Communication Business Model (CBM) as a new management approach for communications. To this end, pertinent definitions, frameworks and typologies of business models are analyzed and combined with insights from corporate communications literature.

The CBM outlines the generic architecture of business models for communication departments. Such models describe the basic principles of how such a unit operates, what services and products it provides, how it creates value for an organization and what revenues and resources are allocated.

The approach stimulates the debate on communication units as objects of observation when researching communication management practices. Further research with appropriate empirical methods is needed to identify and study different types of business models for communications.

The CBM can be used as a management tool to analyze, explain and innovate communication management in organizations. It is a fertile approach for communication practitioners to make the work of their department visible and to position themselves internally and externally.

Transferring a well-known concept from general management to communication management enriches the value creation debate in theory and practice. It allows communication leaders to align their work with organizational goals and make it accessible to top management and other decision-makers in the organization. It also opens up new avenues for research and education.

In light of climate change, the design and construction of buildings needs to shift from conventional to lower-carbon practices to maximise carbon reduction. Over the past few years, the zero carbon buildings (ZCBs) approach has been promoted worldwide as an effective way to reduce environmental impacts and mitigate climate change. Although zero-carbon policies, technologies, processes and products are widely available in the construction market, construction stakeholders play an important part in adopting relevant strategies to implement ZCBs successfully. This study investigates the knowledge of construction stakeholders involved in the design and construction of buildings regarding zero carbon initiatives in New Zealand.

The research was conducted using a literature review and an online questionnaire survey with various New Zealand's construction stakeholders.

The findings indicate a low level of knowledge regarding the design and construction of ZCBs. To successfully deliver ZCBs, the study suggests that construction stakeholders must have their self-awareness increased, especially in improving knowledge of whole-of-life embodied carbon reduction. The governments and construction sectors should devote more effort to establishing training programmes and knowledge-sharing platforms to improve stakeholder knowledge in carbon literacy, building assessment methods, energy modelling and life cycle assessment.

The research implications may assist the real-world uptake of the ZCBs approach by offering academics and practitioners an insight into the ZCBs knowledge gaps.

This study uses computer-aided design to improve the ecological and environmental sustainability of early-stage landscape designs. Urban expansion on open land and natural habitats has led to a decline in biodiversity and increased climate change impacts, affecting urban inhabitants' quality of life and well-being. While sustainability indicators have been employed to assess the performance of buildings and neighbourhoods, landscape designs' ecological and environmental sustainability has received comparatively less attention, particularly in early-design stages where applying sustainability approaches is impactful.

The authors propose a computation framework for evaluating key landscape sustainability indicators and providing real-time feedback to designers. The method integrates spatial indicators with widely recognized sustainability rating system credits. A specialized tool was developed for measuring biomass optimization, precipitation management and urban heat mitigation, and a proof-of-concept experiment tested the tool's effectiveness on three Mediterranean neighbourhood-level designs.

The results show a clear connection between the applied design strategy to the indicator behaviour. This connection enhances the ability to establish sustainability benchmarks for different types of landscape developments using parametric design.

The study allows non-expert designers to measure and embed landscape sustainability early in the design stages, thus lowering the entry level for incorporating biodiversity enhancement and climate mitigation approaches.

This study expands the parametric vocabulary for measuring landscape sustainability by introducing spatial ecosystem services and architectural sustainability indicators on a unified platform, enabling the integration of critical climate and biodiversity-loss solutions earlier in the development process.