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Despite the technological paradigm shift presented to the architecture, engineering, construction and operations sector (AECO), the full-fledged acceptance of the building information modelling (BIM) methodology has been slower than initially anticipated. Indeed, this study aims to acknowledge the need for increasing supportive technologies enabling the use of BIM, attending to available human resources, their requirements and their tasks.

A complete case study is described, including the development process centred on design science research methodology followed by the usability assessment procedure validated by construction projects facility management operational staff.

Results show that participants could interact with BIM using openBIM processes and file formats naturally, as most participants reached an efficiency level close to that expected for users already familiar with the interface (i.e. high-efficiency values). These results are consistent with the reported perceived satisfaction and analysis of participants’ discourses through 62 semi-structured interviews.

The contributions of the present study are twofold: a proposal for a virtual reality openBIM framework is presented, particularly for the semantic enrichment of BIM models, and a methodology for evaluating the usability of this type of system in the AECO sector.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

Factors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.

In this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.

The findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.

The model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.

Although the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.

Previous research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

This paper aims to further the discussion on Building Information Modelling (BIM) legal requirements, providing a framework with key energy parameters capable of supporting the Appointing Party in the definition of the Exchange Information Requirements (EIR) for a BIM project appointment. The EIR is described in ISO-19650–1:2018 as a fundamental step in the information delivery cycle.

A literature review on the topic of BIM energy analysis was completed to identify current knowledge gaps and support the need for the proposed framework. Afterwards, the framework was established based on the review findings and the authors’ domain knowledge. The applicability of the proposed framework was assessed through a case study, where several energy simulations were performed in three different design stages of the same BIM model.

This study identified a lack of standards and legislation capable of supporting the Appointing Party in the definition of energy-related BIM requirements. To this end, a new framework is proposed to mediate existing practices, linking prior knowledge with BIM’s new reality. The study showcases the applicability of the framework, identifying that the performance of different energy studies involves distinct Level of Development (LOD) requirements, which in turn have an impact on the modelling time and cost.

A BIM framework for the specification of information requirements in energy-related projects was developed to support the Appointing Party. The framework presents appropriate parameters for energy analysis in each design stage, as well as the suitable LOD for the BIM model.

This study aims to analyse the relationship between health human capital and economic growth for a maximum sample of 92 countries over the period 1980-2010 taking into account countries’ heterogeneity by assessing how health variables affect different countries according to their position on the conditional growth distribution.

The paper estimates a growth regression applying the methodology proposed by Canay (2011) for regression by quantiles (Koenker, 1978, 2004, 2012a, 2012b) in a panel framework. Quantile regression analysis allows us to identify the growth determinants that present a non-linear relationship with growth and determine the policy implications specifically for underperforming versus over achieving countries in terms of output growth.

The authors’ findings indicate that better health is positively and robustly related to growth at all quantiles, but the quantitative importance of the respective coefficients differs across quantiles, in some cases, with the sign of the relationship greater for countries that recorded lower growth rates. These results apply to both positive (life expectancy) and negative (infant mortality rate, undernourishment) health status indicators.

Given the predominantly public nature of health funding, cuts in health expenditure should be carefully balanced even in times of public finances sustainability problems, particularly when growth slowdowns, as a decrease in the stock of health human capital could be particularly harmful for growth in under achievers. Additionally, the most effective interventions seem to be those affecting early childhood development that should receive from policymakers the necessary attention and resources.

This study contributes to the existing literature by answering the question of whether the growth effects of health human capital can differ in sign and/or magnitude depending on a country’s growth performance. The findings may help policymakers to design the most adequate growth promoting policies according to the behaviour of output growth.