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Earned value management (EVM)–based models for estimating project actual duration (AD) and cost at completion using various methods are continuously developed to improve the accuracy and actualization of predicted values. This study primarily aimed to examine natural gradient boosting (NGBoost-2020) with the classification and regression trees (CART) base model (base learner). To the best of the authors' knowledge, this concept has never been applied to EVM AD forecasting problem. Consequently, the authors compared this method to the single K-nearest neighbor (KNN) method, the ensemble method of extreme gradient boosting (XGBoost-2016) with the CART base model and the optimal equation of EVM, the earned schedule (ES) equation with the performance factor equal to 1 (ES1). The paper also sought to determine the extent to which the World Bank's two legal factors affect countries and how the two legal causes of delay (related to institutional flaws) influence AD prediction models.

In this paper, data from 30 construction projects of various building types in Iran, Pakistan, India, Turkey, Malaysia and Nigeria (due to the high number of delayed projects and the detrimental effects of these delays in these countries) were used to develop three models. The target variable of the models was a dimensionless output, the ratio of estimated duration to completion (ETC(t)) to planned duration (PD). Furthermore, 426 tracking periods were used to build the three models, with 353 samples and 23 projects in the training set, 73 patterns (17% of the total) and six projects (21% of the total) in the testing set. Furthermore, 17 dimensionless input variables were used, including ten variables based on the main variables and performance indices of EVM and several other variables detailed in the study. The three models were subsequently created using Python and several GitHub-hosted codes.

For the testing set of the optimal model (NGBoost), the better percentage mean (better%) of the prediction error (based on projects with a lower error percentage) of the NGBoost compared to two KNN and ES1 single models, as well as the total mean absolute percentage error (MAPE) and mean lags (MeLa) (indicating model stability) were 100, 83.33, 5.62 and 3.17%, respectively. Notably, the total MAPE and MeLa for the NGBoost model testing set, which had ten EVM-based input variables, were 6.74 and 5.20%, respectively. The ensemble artificial intelligence (AI) models exhibited a much lower MAPE than ES1. Additionally, ES1 was less stable in prediction than NGBoost. The possibility of excessive and unusual MAPE and MeLa values occurred only in the two single models. However, on some data sets, ES1 outperformed AI models. NGBoost also outperformed other models, especially single models for most developing countries, and was more accurate than previously presented optimized models. In addition, sensitivity analysis was conducted on the NGBoost predicted outputs of 30 projects using the SHapley Additive exPlanations (SHAP) method. All variables demonstrated an effect on ETC(t)/PD. The results revealed that the most influential input variables in order of importance were actual time (AT) to PD, regulatory quality (RQ), earned duration (ED) to PD, schedule cost index (SCI), planned complete percentage, rule of law (RL), actual complete percentage (ACP) and ETC(t) of the ES optimal equation to PD. The probabilistic hybrid model was selected based on the outputs predicted by the NGBoost and XGBoost models and the MAPE values from three AI models. The 95% prediction interval of the NGBoost–XGBoost model revealed that 96.10 and 98.60% of the actual output values of the testing and training sets are within this interval, respectively.

Due to the use of projects performed in different countries, it was not possible to distribute the questionnaire to the managers and stakeholders of 30 projects in six developing countries. Due to the low number of EVM-based projects in various references, it was unfeasible to utilize other types of projects. Future prospects include evaluating the accuracy and stability of NGBoost for timely and non-fluctuating projects (mostly in developed countries), considering a greater number of legal/institutional variables as input, using legal/institutional/internal/inflation inputs for complex projects with extremely high uncertainty (such as bridge and road construction) and integrating these inputs and NGBoost with new technologies (such as blockchain, radio frequency identification (RFID) systems, building information modeling (BIM) and Internet of things (IoT)).

The legal/intuitive recommendations made to governments are strict control of prices, adequate supervision, removal of additional rules, removal of unfair regulations, clarification of the future trend of a law change, strict monitoring of property rights, simplification of the processes for obtaining permits and elimination of unnecessary changes particularly in developing countries and at the onset of irregular projects with limited information and numerous uncertainties. Furthermore, the managers and stakeholders of this group of projects were informed of the significance of seven construction variables (institutional/legal external risks, internal factors and inflation) at an early stage, using time series (dynamic) models to predict AD, accurate calculation of progress percentage variables, the effectiveness of building type in non-residential projects, regular updating inflation during implementation, effectiveness of employer type in the early stage of public projects in addition to the late stage of private projects, and allocating reserve duration (buffer) in order to respond to institutional/legal risks.

Ensemble methods were optimized in 70% of references. To the authors' knowledge, NGBoost from the set of ensemble methods was not used to estimate construction project duration and delays. NGBoost is an effective method for considering uncertainties in irregular projects and is often implemented in developing countries. Furthermore, AD estimation models do fail to incorporate RQ and RL from the World Bank's worldwide governance indicators (WGI) as risk-based inputs. In addition, the various WGI, EVM and inflation variables are not combined with substantial degrees of delay institutional risks as inputs. Consequently, due to the existence of critical and complex risks in different countries, it is vital to consider legal and institutional factors. This is especially recommended if an in-depth, accurate and reality-based method like SHAP is used for analysis.

This research aims to develop a method for defining the identity of multilayered neighbourhoods by taking a case study in Nantes/France. It utilizes the urban identity concept to achieve this goal, which is defined by physical and identificatory relation to the neighbourhood.

The methodology includes historical periodical analysis, housing form and architectural stylistic definition, visualization and geographic information system (GIS) mapping. The research conducts spatial analysis to reveal the physical component of the urban identity of the neighbourhood and interviews (No = 50) with dwellers for the identificatory relation, asking about neighbourhood tangible/non-tangible elements. All these data are mapped through GIS.

The study found that the physical component is defined by three urban layers (identified as industrial, reconstruction and development, and post-industrial) and eleven housing typologies. As for the identificatory relation, the authors found that the interviewees mostly identified with their neighbourhood, whereas a minority did not. The most important form of identification with the neighbourhood was its atmosphere, and as reasons were given, the neighbourhood's positively evaluated quality, good location and social values.

It proposes the definition of the physical component through urban layers and housing typologies. The identificatory relation also considers the identification of the residents with the neighbourhood's tangible/non-tangible urban elements.

The purpose of this paper is to propose a novel data-driven approach for predicting energy performance of buildings that can address the scarcity of quality data, and consider the dynamic nature of building systems.

This paper proposes a reinforcing machine learning (ML) approach based on transfer learning (TL) to address these challenges. The proposed approach dynamically incorporates the data captured by the building management systems into the model to improve its accuracy.

It was shown that the proposed approach could improve the accuracy of the energy performance prediction compared to the conventional TL (non-reinforcing) approach by 19 percentage points in mean absolute percentage error.

The case study results confirm the practicality of the proposed approach and show that it outperforms the standard ML approach (with no transferred knowledge) when little data is available.

This approach contributes to the body of knowledge by addressing the limited data availability in the building sector using TL; and accounting for the dynamics of buildings’ energy performance by the reinforcing architecture. The proposed approach is implemented in a case study project based in London, UK.

While most efforts to combat climate change are focussed on energy efficiency and substitution of fossil fuels, growth in the built environment remains largely unquestioned. Given the current climate emergency and increasing scarcity of global resources, it is imperative that we address this “blind spot” by finding ways to support required services with less resource consumption.

There is now long overdue recognition to greenhouse gas emissions “embodied” in the production of building materials and construction, and its importance in reaching targets of net zero carbon by 2050. However, there is a widespread belief that we can continue to “build big”, provided we incorporate energy saving measures and select “low carbon materials” – ignoring the fact that excessive volume and area of buildings may outweigh any carbon savings. This is especially the case with commercial real estate.

As the inception and planning phases of projects offer most potential for reduction in both operational and embodied carbon, we must turn our attention to previously overlooked options such as “build nothing” or “build less”. This involves challenging the root cause of the need, exploring alternative approaches to meet desired outcomes, and maximising the use of existing assets. If new build is required, this should be designed for adaptability, with increased stewardship, so the building stock of the future will be a more valuable and useable resource.

This points to the need for increased understanding and application of the principles of strategic asset management, hitherto largely ignored in sustainability circles, which emphasize a close alignment of assets with the services they support.

Arguably, as the built environment consumes more material resources and energy than any other sector, its future configuration may be critical to the future of people and the planet. In this regard, this paper seeks to break new ground for deeper exploration.

This study examined data from 13 international tall residential timber building case studies to increase our understanding of the emerging global trends.

Data were collected through literature surveys and case studies to examine the architectural, structural and constructional points of view to contribute to knowledge about the increasing high-rise timber constructions globally.

The main findings of this study indicated that: (1) central cores were the most preferred type 10 of core arrangements; (2) frequent use of prismatic forms with rectilinear plans and regular extrusions were identified; (3) the floor-to-floor heights range between 2.81 and 3.30 m with an average of 3 m; (4) the dominance of massive timber use over hybrid construction was observed; (5) the most used structural system was the shear wall system; (6) generally, fire resistance in primary and secondary structural elements exceeded the minimum values specified in the building codes; (7) the reference sound insulation values used for airborne and impact sounds had an average of 50 and 56 dB, respectively.

There is no study in the literature that comprehensively examines the main architectural and structural design considerations of contemporary tall residential timber buildings.

Construction professionals are the major contributors to developing a sustainable construction industry, whereas architects, engineers and quantity surveyors are the key construction professionals who must play extraordinary roles in achieving better sustainable construction. Therefore, this study aims to identify the job attributes of key Sri Lankan construction professionals in addressing challenges associated with climate change.

The study adopted a mixed research approach. A literature review and preliminary semi-structured interviews were used to appraise the job roles of architects, engineers and quantity surveyors in addressing challenges associated with climate change. The data collected through the qualitative approach were used in an online questionnaire survey, and the findings were analysed using the relative index method.

The findings highlight that regardless of the knowledge of the professional category on green rating tools, carbon footprint, adaptation of renewable energies for the reduction of energy consumption, building information modelling-related applications and waste management concepts/practices are the foremost job attributes required for the key Sri Lankan construction professionals in addressing challenges associated with climate change.

The results from this study provide a handful of guidance to construction industry professionals, national and international professional institutions, non-governmental organisations and other relevant authorities to address climate change within the built environment by identifying ways for improving the relevant key job attributes of construction industry professionals.

To the best of the authors’ knowledge, this is the first study that explores the job attributes of key Sri Lankan construction professionals in addressing the challenges associated with climate change.

This study intends to analyze the trend of walkability research over two years (2021–2022) in several aspects, including the research problem, gap and aim underlying walkability issues, research setting, the scale of analysis, the type of data, the data collection tool, the analysis method and contribution.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) is employed to conduct a systematic literature review from three leading databases including Web of Science (WoS), Scopus and Sage.

From the review, it can be concluded that the research focuses on two aspects, namely, the built environment and people. The studies in this field are conducted at different scales of the built environment, from macroscale to microscale, including region, city, neighborhood and street-level studies that are conducted around the world including Asia, Europe and America which receive high attention from researchers. Meanwhile, the study of walkability in Australia and Africa gains less attention. It is revealed that there are four factors of walkability including land use and accessibility for macroscale which cover region, city and neighborhood, while street network and pedestrian infrastructure and facility are mainly analyzed for street-level. Most of the studies widely utilize a quantitative approach for analyzing physical walkability factors in a measurement-based approach by using certain tools/software, and a perception-based approach by using questionnaires.

The findings can assist to keep up with the rapid pace of scientific publications and help accelerate the understanding of particular ideas in the field of walkability for the planning and design practice and policy that can be used by researchers/academia, urban designers, urban planners, architects, engineers and policymakers. Also, this study hopefully could assist further research direction for the future and could add value to a more advanced way of understanding the research on walkability.

This research provides a robust framework for walkability trend analysis and a comprehensive understanding of the main ideas from the existing research on walkability worldwide.

The study aims to identify and evaluate relevant criteria in the proposal and support of public administration policies for preventive maintenance comprised in a conservation approach to built heritage and aligned with local sustainable development of the historic center of the city of Sobral, in Brazil.

A novel multicriteria decision model adopting the Bayesian best-worst method is presented and its application and results are described. Though a systematic procedure, criteria were selected in order to protect the tangible and intangible values of cultural heritage, as well as its sustainable development. Then experts evaluate these criteria through an elicitation instrument.

The results show that for the decision problem over preventive maintenance, social contribution and historical record of built heritage are more important than its structural vulnerability, while architecture is less relevant. Due to the low restrictions, the subcriterion related to this property has the least influence. The weights can assist in the characterization of measures and policies for the protection of the built cultural heritage.

The use of a novel decision-making method in cultural heritage is an important initiative, given the frequent use of simple and inefficient methods. The identified and weighted criteria are important data to characterize the scenario and the topic. The results contribute to protection and development of the built heritage, encouraging the implementation of preventive conservation in the historic center, conferring to the public administration valuable information to support and propose initiatives.

The paper aims to understand why and how paratextual elements are included in annual reports, hence how meaning is made through the workings of language and imagery.

A comprehensive framework of analysis, combining Genette’s paratextual elements with Barthes’ rhetoric and denotation and connotation concepts, is applied to the case study of the dstgroup, a Portuguese engineering and construction group.

The study demonstrates the potential of the annual report as a communication tool between an organisation and its stakeholders. The framework of analysis evidences that the paratextual elements highlight and supplement accounting information and that the denotative and connotative meanings associated with them make visible and enhance intangible features of the organisation.

The paper extends theories from other interdisciplinary fields to accounting communication and proposes a comprehensive framework that combines the writings of Genette and Barthes. By exploring the Portuguese under-researched context, it also adds to the literature by analysing the rationales and choices of the preparers on the inclusion of paratextual elements in annual reports.

Este artículo pretende entender por qué y cómo se incluyen elementos paratextuales en los informes anuales y, cómo se construye el significado a través del funcionamiento del lenguaje y las imágenes.

Se desenvolvió un marco de análisis global, que combina los elementos paratextuales de Genette y los conceptos de retórica y denotación y connotación de Barthes. Este marco se aplicó al estudio de caso del grupo dst, un grupo portugués de ingeniería y construcción.

El estudio muestra el potential del informe anual como herramienta de comunicación entre una organización y sus grupos de interés. El marco de análisis evidencia que los elementos paratextuales resaltan y complementan la información contable y que los significados denotativos y connotativos asociados a ellos hacen visibles y realzan rasgos intangibles de la organización.

Este artículo extiende teorías de otros campos interdisciplinarios a la comunicación contable y propone un marco global que combina los escritos de Genette y Barthes. Explorando el contexto portugués, insuficientemente investigado, también se añade a la literatura mediante el análisis de las motivaciones y opciones de los preparadores de los informes anuales sobre la inclusión de elementos paratextuales.