Purpose: Despite the positive impacts of the construction sector on enhancing economic growth and ensuring societal well-being, its negative impacts on the environment from unsustainable resource consumption levels, emission of greenhouse gases (GHGs) and waste generation is monumental. Circular economy (CE) concept is identified globally as an avenue to address problems regarding adverse impacts of construction on the environment. This paper presents the principles of CE as an avenue for enhancing environmental sustainability during the pre-construction stage within Sri Lankan construction projects.Design/methodology/approach: This research was approached through a qualitative research method. Data were collected through semi-structured interviews with subject matter experts. The number of experts were limited due to lack of experts with knowledge on the subject area in Sri Lanka. Data were analysed using content analysis.Findings: Findings revealed a range of activities under each R principle of CE, that is, reduce, reuse, recycle, redesign, reclassification and renewability that could be implemented during the pre-construction stage, thereby providing a guide for construction professionals in implementing CE at the pre-construction stage. The need to expand knowledge on CE concepts within the Sri Lankan construction sector was recognized.Originality/value: This study provides a qualitative in-depth perspective on how 6R principles of CE could be integrated to a construction project during the pre-construction stage. By adopting the proposed activities under CE principles, construction professionals can enhance the environmental sustainability of construction projects.

Purpose: Many studies have focused on embodied energy (EE) and operational energy (OE), but a shortage of studies on decision making, which involves several decision makers whose decisions can affect the energy performance of buildings, is evident. From the stages of the project life cycle, the design stage is identified as the ideal stage for integrating energy efficiency into buildings. Therefore, the purpose of this paper is to revisit the role of professionals in designing energy-conscious buildings with low EE and OE. Design/methodology/approach: This study administered a qualitative approach. Data were collected through semi-structured interviews only with 12 experts, due to the lack of expertise in the subject matter. The data were analyzed using manual content analysis. Findings: The outcomes revealed the necessity to revisit the role of construction professionals in terms of adopting energy-efficient building design concepts from the project outset. The roles of the key professional groups (i.e. architects, structural engineers, services engineers and quantity surveyors) were identified through this research. Common issues in designing energy-efficient buildings and the means of addressing such problems were outlined. Originality/value: This study contributes to the knowledge by revisiting the roles of construction professionals and proposing how they could leverage their strengths to play the important role and contribute collectively to design buildings with both low OE and EE.

With the rising impact of technological advancements within all sectors, there is an increase in interest among construction industry practitioners to integrate novel technologies collaborated with concepts. Lean is one such concept, which has received growing attention of construction companies. However, past researchers have identified many barriers for successful lean implementation in global construction context, where, technological approaches seem to be a success factor. Many experts have identified Internet of Things (IoT) as an enabler for the lean implementation in construction, which is undoubtedly lacking in the Sri Lankan context. Hence, this paper aims to investigate the drivers and barriers for using IoT for successful lean implementation in Sri Lankan construction industry. Seven semi structured interviews with experts in IoT and lean construction were conducted under the qualitative approach for data collection to achieve the research aim. Findings highlighted increased product and process quality; reduced unnecessary delays in construction process; reduced unnecessary costs; enhanced inventory management system; and increased site safety as key drivers, whereas, expensive devises and high cost of implementation; resistance to change; lack of internet coverage, IT facilities and compatibility issues; difficulties to adapt due to lack of knowledge and skills; security and privacy issues; and lack of innovative culture were identified as key barriers of using IoT for successful lean implementation in Sri Lankan construction industry. Findings revealed the several similarities in drivers and barriers of using IoT for lean construction implementation both in global and Sri Lankan context.

Building cracks are among the critical building defects, as they can compromise structural integrity, occupant safety and building sustainability. Traditional laborious building inspection methods are cumbersome and erroneous. Computer vision-based crack detection relies on image recognition and does not analyse the underlying causes or suggest rectification strategies. This study explores the potential and limitations of multimodal AI models, that integrate image and text modalities for building crack analysis. As a proof-of-concept, the vision–language model, CracksGPT was built upon a fine-tuned MiniGPT-v2. It was trained on custom crack images with text descriptions detailing visual features, possible causes, and rectification options. It was tested on crack images from a building site in Sydney. When provided with an image of a wall crack, CracksGPT can classify crack patterns of vertical, horizontal, diagonal, and stair-step and interpret possible underlying causes with potential rectification strategies. The ROUGE metric was used for language generation quality evaluation followed by a performance evaluation by building inspection experts. The model’s performance is sensitive to input image quality and training data limitations, specifically in complex scenarios, reaffirming the value of expert overseeing. The findings highlight the potential and limitations of multimodal AI for integrating vision–language reasoning into building inspections.