Search results

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 study aims to identify opportunities and constraints of community forestry in the context of forest decentralization in Cameroon and what can be capitalized on for sound REDD+ design and implementation.

A qualitative approach to data collection was used through content analysis of 1994 forestry law, reports and publications related to decentralized forest management, community forestry and REDD+ in Cameroon. Principles that govern community forest and REDD+ were highlighted and opportunities and constraints of community forestry for REDD+ projects were discussed.

Community forestry was developed principally to protect forests in order to support the subsistence and income-generating extractive activities of forest-dependent communities. Community forestry governance arrangements were not designed with the objective of achieving verifiable emissions reductions or carbon stock values. Hence, existing community forestry institutions may not address all the specific demands of REDD+ programs. However, existing community institutions and practices can be strengthened or modified to align better with climate change mitigation goals and to achieve REDD+ objectives in community forestry sites. On the other hand, REDD+ was developed principally to mitigate climate change by reducing emissions from deforestation and forest degradation principally within developing countries where the livelihoods of forest-dependent people are a central component of all forest management policies. However, despite fundamental differences between community forestry and REDD+, there is substantial synergy between their objectives, and the dual forest conservation and livelihood development focus of both programs means that policies that strengthen and support existing community forestry institutions and sites will advance REDD+ objectives. As such, REDD+ will likely to be more successful if it builds on lessons learned from community forestry.

This paper demonstrates how REDD+ is more likely to succeed if it builds on the lessons learned from community forestry over the past 20-plus years in Cameroon. It also discusses how REDD+ can benefit from community forestry and how some of the many challenges related to community forestry can be directly addressed by the REDD+ mechanism. Further, this paper also argues how the congruence between community forestry and REDD+ can effectively facilitate the direct use of community forestry as a tool to achieve REDD+ goals.

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.