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Evidence-based design (EBD) is traditionally limited to using empirical research findings based on randomized controlled trials. The purpose of this study is to explore stakeholder experiential knowledge as alternate credible evidence in redesigning health-care facilities for improved usability.

This research, based on critical participatory action research, involved a case study of redesign and post-occupancy evaluation (POE) of an emergency department (ED) at Holy Family Hospital, Techiman, Ghana. Observation, interviews and document analysis were used to collect data in the redesign and POE. The data was analyzed through directed content analysis.

Findings indicate that the redesign interventions, generated from stakeholder experiential knowledge, led to improved effectiveness, efficiency and satisfaction in the ED. This presents stakeholder experiential knowledge as alternate credible evidence in EBD. Furthermore, the POE revealed that open and flexible spatial arrangement, zoning care areas according to severity, and providing staff-support amenities are some redesign interventions for improving ED usability.

Compared to the hard and controlled nature of experimental research knowledge, the soft and fluid experiential knowledge of stakeholders could be more useful for health-care redesign process, especially in iteratively structuring design thinking and making choices.

This paper contributes to theory by validating and illustrating stakeholder experiential knowledge as credible evidence for EBD. Practically, it provides strategies, based on POE findings, for designing EDs to improve usability.

Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.

The construction industry is the major sector in China but it has been exposed to a series of problems including low productivity and workforce shortage. However, construction robots as an effective and sustainable approach to overcome the difficulties in construction industry have not been widely adopted. Few studies attempted to investigate on the adoption of construction robots in China. In order to fill this gap, this study aim to address the barriers to the adoption of construction robots in China.

Through literature review, semi-structured interview 24 factors hindering the adoption of construction robots are summarized. Next, a total of valid 150 questionnaires delivered to the 7 stakeholders were collected. Ranking analysis was used to identify 21 critical factors was determined by the mean score analysis and factor analysis extracted 21 critical factors into 5 clusters.

Results indicate that the “technological performance and management” cluster is the most dominant of the 5 clusters. The most important barrier is “Limited research and design input”, followed by “High purchase cost” and “Unstructured construction environment”. Construction robots are still under R&D have had limited field applications in the production and construction process.

The research findings provide a useful reference for different stakeholders to identify the critical factors appropriate strategies to promote the adoption of construction robots. Furthermore, this study provides recommendations to promote adoption of construction robots.