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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.

Proximity is a crucial factor influencing innovation collaboration and performance. Most existing studies have primarily focused on the organizational level and been static in nature. Therefore, a further study on how proximity affects innovation performance is needed. This paper aims to fill this gap by highlighting the organizational, cognitive and geographical proximity in China’s open regional innovation system.

This paper analyzes the data from 2010 to 2015 through path analysis.

The results reveal that geographical proximity has a direct positive effect on regional innovation performance in China’s regional innovation system. It also shows that organizational proximity exerts a negative impact on absorptive capacity, and through it adversely affects regional innovation performance. In contrast, cognitive proximity is found to have a positive effect on absorptive capacity, enhancing regional innovation performance.

Based on these findings, this paper contributes to a better understanding of the role of proximity in innovation collaboration and performance. By highlighting the importance of different proximity types, it provides insights for policymakers and practitioners seeking to foster regional innovation.