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This paper aims to describe current trends in probabilistic structural fire engineering and provides a comprehensive summary of the state-of-the-art of performance-based structural fire engineering (PSFE).

PSFE has been introduced to overcome the limitations of current conventional design approaches used for the design of fire-exposed structures, which investigate assumed worst-case fire scenarios and include multiple thermal and structural analyses. PSFE permits buildings to be designed in relation to a level of life safety or economic loss that may occur in future fire events with the help of a probabilistic approach.

This paper brings together existing research on various sources of uncertainty in probabilistic structural fire engineering, such as elements affecting post-flashover fire development, material properties, fire models, fire severity, analysis methods and structural reliability.

Prediction of economic loss would depend on the extent of damage, which is further dependent on the structural response. The representative prediction of structural behaviour would depend on the precise quantification of the fire hazard. The incorporation of major uncertainty sources in probabilistic structural fire engineering is explained, and the detailed description of a pioneering analysis method called incremental fire analysis is presented.

The fire resistance of precast prestressed concrete floor systems is heavily influenced by the end connections and the stiffness of the surrounding structure, both of which must be considered in any analysis. Analysing floor slabs with beam or shell elements whose end nodes share the nodes of supporting beams leads to a major problem for precast prestressed flooring systems where the steel tendons terminate at the end of the flooring units, because the approach of sharing nodes of the supporting beam and floor assumes that these tendons are anchored into the supporting beams. In order to solve this problem, a "multi-spring" connection element has been developed. Experimental data available from previous research work has been used to validate the finite element model. It is concluded that the inclusion of the multi-spring connection results in more accurate predictions of prestressed hollowcore slab behaviour in fire conditions.

This research investigated the fire performance and failure behaviour of timber-concrete composite floor systems currently under development in New Zealand, resulting in a design method for evaluating the fire resistance of these floors with different types of connections. Furnace tests were performed on two full-size floor specimens at the Building Research Association of New Zealand (BRANZ). Both floor specimens were 4 m long and 3 m wide, consisting of 65 mm concrete topping on plywood formwork, connected to double LVL (laminated veneer lumber) floor joists. They were tested over a 4 m span, subjected to a nominal design live load of 2.5 kPa. Both floors were subjected to the ISO 834 test fire for over 60 minutes. Two separate connection types were tested; concrete notches cut into the timber beams with an incorporated shear key, and metal toothed plates pressed between the double beams.

It was found that the reduction in section of the timber beams due to the fire governed the failure mode of the floors. The test data and visual observations aided in the development of an analytical model for evaluating the fire resistance of timber-concrete composite floors. This was implemented into a spreadsheet that is able to predict the expected fire resistance of these floors, taking into account some major time dependent variable properties that can have an effect on the overall performance. Load-span tables have been produced to give the estimated fire resistance of floors with differing dimensions, span lengths and applied loads.

Over the years, India has witnessed irregular FDI inflows. Therefore, this study aims to explore the asymmetric impact of per capita income, final consumption expenditure, globalization index and exchange rate on FDI inflows in India.

Using the nonlinear autoregressive distributed lag bounds framework and unknown structural break, the study investigates the impacts of selected macroeconomic variables in driving FDI inflows in India during the study period (1979-2016).

The outcomes of the study confirm the asymmetric relationship between FDI inflows and its determinants during the study period. The results have confirmed that the improvement in per capita income, private consumption expenditure, globalization index and currency value appreciation play a crucial role in increasing FDI inflows in India. In contrast, the downside movements in the volume of consumption expenditure, globalization index and depreciation of the currency value in relation to the trade partners result in reducing the volume of FDI inflows in the long run.

For determining FDI inflows, previous studies have considered the overall impact of its potential determinants, which may provide partial information about the phenomenon. The adopted nonlinear approach highlights that both the types of fluctuations (i.e. upside and downside) in the independent variables may affect FDI inflows differently and substantially. The nonlinear association between FDI and selected determinants may be vital in formulating a long-term policy.

This study assesses the SDG-related well-being of indigenous communities in India using bibliometric analysis and the ADO-TCM framework. It provides insights into their alignment with sustainable development objectives.

This study analysed 74 high-impact journals using bibliometric analysis to evaluate the well-being of India’s indigenous peoples about the SDGs.

This study analyses the well-being of tribal communities in India using existing scholarly articles and the ADO-TCM framework. It emphasises the importance of implementing Sustainable Development Goals (SDGs) to promote the well-being of indigenous populations.

This study uses bibliometric analysis and the ADO-TCM framework to investigate factors impacting tribal community welfare. It proposes theoretical frameworks, contextual considerations and research methodologies to achieve objectives.

In recent years, deep learning and extended reality (XR) technologies have gained popularity in the built environment, especially in construction engineering and management. A significant amount of research efforts has been thus dedicated to the automation of construction-related activities and visualization of the construction process. The purpose of this study is to investigate potential research opportunities in the integration of deep learning and XR technologies in construction engineering and management.

This study presents a literature review of 164 research articles published in Scopus from 2006 to 2021, based on strict data acquisition criteria. A mixed review method, consisting of a scientometric analysis and systematic review, is conducted in this study to identify research gaps and propose future research directions.

The proposed research directions can be categorized into four areas, including realism of training simulations; integration of visual and audio-based classification; automated hazard detection in head-mounted displays (HMDs); and context awareness in HMDs.

This study contributes to the body of knowledge by identifying the necessity of integrating deep learning and XR technologies in facilitating the construction engineering and management process.

The unitized curtain wall system (UCWS), one of the prefabricated technologies, is increasingly attracting attention in the Hong Kong construction industry. However, this innovative technology still lacks on-site implementation in high-rise residential buildings. To promote its development, this study aims at identifying the influential factors of UCWS adoption in Hong Kong's high-rise residential buildings from a multi-stakeholder perspective.

Factors were first selected through an in-depth literature review and a semi-structured interview. Then the factors were validated through a questionnaire survey using Cronbach's Alpha Reliability Test. Next, the factors were ranked regarding their importance using mean-score ranking and standard deviation. Meanwhile, different stakeholders were clustered using an experimental factor analysis (EFA) model to find the shared preferences (namely common factors).

The result shows that reduction of construction time (B1) and insufficient site storage area (C1) are the most important factors. The six stakeholder groups were clustered into two segments. B1 and improved quality control are the shared interests. While C1 and the need of specification change are the common concerns.

There are two major breakthroughs in this study. First is the novelty of research objects. UCWS, particularly its application preference in high-rise residential buildings, has rarely been studied, yet it is urgently required. Second is the novel research perspective. The influential factors were studied from a multi-stakeholder perspective. Not only the significant factors for six specific stakeholders but also the shared preference for stakeholder groups was identified. The findings contribute to promoting UCWS more targeted, efficient and comprehensive, as well as demonstrating the collaborative possibilities of multi-stakeholders.

Motivated by the growing and urgent demands for a unified set of internationally accepted, and high-quality environmental, social and governance (hereafter ESG) disclosure standards, this exploratory study aims to propose a roadmap for setting out the proper technical groundwork for global ESG disclosure standards.

An exploratory study is conducted to gain initial understanding and insights into establishing a worldwide set of standards for reporting on sustainability, as this topic has not been extensively studied. This study examines the viewpoints of various stakeholders, including sustainability practitioners, academics and organizations focused on ESG issues, to generate knowledge that is more solid than knowledge produced when one group of stakeholders work alone.

The results revealed that there is an ongoing and incompatible debate regarding several conceptual and practical challenges for setting a unified set of ESG disclosure standards.

The study results provide multidimensional insights for regulatory parties and standard-setters to develop a high-quality package of global ESG reporting standards. This, in turn, enables different groups of stakeholders to understand the firm’s impact on the environment, society and economy.

Research into this timely and relevant global issue is considered an appealing area of study and deserves significant attention. Thereby, working on this topic merits remarkable attention. Furthermore, this exploratory article provides valuable and informative suggestions for creating a unified and high-quality set of internationally accepted sustainability reporting standards.

The present work has proposed and employed an innovative hybrid method based on the combination of factor analysis and an artificial neural network (ANN) model to forecast customer satisfaction from the identified dimensions of service quality in India, a developing country.

The qualitative study is conducted with Internet banking users to understand e-banking clients' perceptions. The data is collected with the help of a questionnaire from randomly selected 208 customers in India. Firstly, factor analysis was performed to determine the influential factors of customer satisfaction, and four factors i.e. efficiency, reliability, security and privacy, and issue and problem handling were extracted accordingly. The neural network model is then applied to the factor scores to validate the key elements. Lastly, the comparative analysis of the actual ANN and the regression predicted result is done.

The success ability of the linear regression model is challenged when approximated to nonlinear problems such as customer satisfaction. It is concluded that the ANN model is a better fit than the linear regression model, and it can recognise the complex connections between the exogenous and endogenous variables. The results also show that reliability, security and privacy are the most influencing factors; however, problem handling and efficiency have the slightest effect on bank client satisfaction.

This research is conducted in India, and the sample is chosen from the urban area. The limitation of the purposeful sampling technique and the cross-sectional nature of the data may hamper the generalisation of the results.

The conclusions from the study will be helpful for policymakers, bankers and academicians. To our knowledge, few studies used ANN modelling to predict customer satisfaction in the service sector