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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 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.

The use of technological innovations to effectively deliver construction projects is gaining significant coverage. This study aims to assess the inhibiting factors to the utilisation of laser scanners for the delivery of construction projects in developing economies using South Africa as the study area.

Adopting a quantitative technique, this study elicited responses from construction professionals using a questionnaire as the instrument for data collection. A four-pronged data analysis method was used, comprising descriptive statistics, Kruskal−Wallis h-test, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA).

Findings from the study show that lack of awareness and high cost of acquisition are the barriers rated by the study’s respondents the most. Also, findings from the EFA and CFA conducted showed and affirmed the significance of three constructs inhibiting factors to the utilisation of laser scanners for construction project delivery: technical hindrances, financial impediments and institutional challenges.

This study makes practical contributions to the discourse of using innovative technologies for effective construction project delivery by inhibiting factors to the use of laser scanners.

Evidence from the literature shows that no study has assessed the barriers to the utilisation of laser scanning technology for construction projects in the South African construction industry. This study strives to close this gap in the literature.