The small and medium-sized enterprises (SMEs) which are the backbone of any economy are often on the disadvantaged side of the digital divide in the construction industry. With the advent of building information modelling (BIM), the SMEs are facing challenges and are slow with its uptake. Hitherto, extant research studies on BIM have focused primarily on the large firms and there is an observed trend of underrepresentation of the SMEs in BIM studies. Thus, this paper aims to investigate the major drivers of sustainable adoption of Building Information Modelling in SMEs and the dynamics of these drivers in developing countries using interpretive structural modelling approach and Matrice d’Impacts croises-multipication applique a classement (MICMAC) analysis. The findings reveal that organizational readiness is of utmost importance for the proliferation of BIM in SMEs. Also, the independent drivers which are the most important drivers consist of BIM characteristics, internal and external environment drivers and thus portray the BIM adoption as a complex socio-technical system. This study categorizes the drivers for easy intervention of SMEs’ managers and policymakers. It contributes to the nascent studies of BIM adoption in SMEs of developing countries.

Human long bones exhibit pore size gradients with small pores in the exterior cortical bone and large pores in the interior cancellous bone. However, most current bone tissue engineering (BTE) scaffolds only have homogeneous porous structures that do not resemble the graded architectures of natural bones. Pore-size graded (PSG) scaffolds are attractive for BTE since they can provide biomimicking porous structures that may lead to enhanced bone tissue regeneration. In this study, uniform pore size scaffolds and PSG scaffolds were designed using the gyroid unit of triply periodic minimal surface (TPMS), with small pores (400 μm) in the periphery and large pores (400, 600, 800 or 1000 μm) in the center of BTE scaffolds (designated as 400-400, 400–600, 400–800, and 400–1000 scaffold, respectively). All scaffolds maintained the same porosity of 70 vol%. BTE scaffolds were subsequently fabricated through digital light processing (DLP) 3D printing with the use of biphasic calcium phosphate (BCP). The results showed that DLP 3D printing could produce PSG BCP scaffolds with high fidelity. The PSG BCP scaffolds possessed improved biocompatibility and mass transport properties as compared to uniform pore size BCP scaffolds. In particular, the 400–800 PSG scaffolds promoted osteogenesis in vitro and enhanced new bone formation and vascularization in vivo while they displayed favorable compressive properties and permeability. This study has revealed the importance of structural design and optimization of BTE scaffolds for achieving balanced mechanical, mass transport and biological performance for bone regeneration.

Previous studies have explored the factors to the usage of construction robotics (CRs). However, what is the most important factor affecting adoption of CRs considering multistakeholders? Do different stakeholders share similar interests or concerns? These questions have yet to be answered. Therefore, this study aims at identifying and assessing benefits and barriers affecting adoption of CRs in Hong Kong from a multistakeholder perspective. Factors were firstly identified through a systematic literature review in a scientometric approach and validated through a questionnaire survey. Then, the relative importance of these factors was calculated using mean scores ranking, and the stakeholder groups were segmented using $k$ -means algorithm. As a result, 14 benefit factors and 14 barrier factors were identified. Stakeholders were segmented into five groups based on common interests and concerns. The results present a better understanding for the importance of benefits and barriers affecting CRs adoption in Hong Kong considering the multiple stakeholders both individually and collectively. The findings suggest ways to facilitate the improvement of CRs to be more focused, efficient, and user-oriented. The results also contribute to finding further collaboration patterns between different stakeholders.

Purpose: This paper aims to make a systematic review of the manpower prediction model of the construction industry. It aims to determine the forecasting model's development trend, analyse the use limitations and applicable conditions of each forecasting model and then identify the impact indicators of the human resource forecasting model from an economic point of view. It is hoped that this study will provide insights into the selection of forecasting models for governments and groups that are dealing with human resource forecasts. Design/methodology/approach: The common search engine, Scopus, was used to retrieve construction manpower forecast-related articles for this review. Keywords such as “construction”, “building”, “labour”, “manpower” were searched. Papers that not related to the manpower prediction model of the construction industry were excluded. A total of 27 articles were obtained and rated according to the publication time, author and organisation of the article. The prediction model used in the selected paper was analysed. Findings: The number of papers focussing on the prediction of manpower in the construction industry is on the rise. Hong Kong is the region with the largest number of published papers. Different methods have different requirements for the quality of historical data. Most forecasting methods are not suitable for sudden changes in the labour market. This paper also finds that the construction output is the economic indicator with the most significant influence on the forecasting model. Research limitations/implications: The research results discuss the problem that the prediction results are not accurate due to the sudden change of data in the current prediction model. Besides, the study results take stock of the published literature and can provide an overall understanding of the forecasting methods of human resources in the construction industry. Practical implications: Through this study, decision-makers can choose a reasonable prediction model according to their situation. Decision-makers can make clear plans for future construction projects specifically when there are changes in the labour market caused by emergencies. Also, this study can help decision-makers understand the current research trend of human resources forecasting models. Originality/value: Although the human resource prediction model's effectiveness in the construction industry is affected by the dynamic change of data, the research results show that it is expected to solve the problem using artificial intelligence. No one has researched this area, and it is expected to become the focus of research in the future.

Project risks must be managed to deliver construction projects on time and within budget. In recent years, the New Engineering Contract (NEC) provides an alternate contracting method for procuring construction projects. As stipulated in the NEC contract, NEC risk register must be used to record any project risks. The risk register is designed to record each risk item in the context of textual description, likelihood, and consequence. However, it is time-consuming to identify, quantify, and analyse NEC project risks based on experience, questionnaire, simulation, and data-mining approach. Any method to fully utilise the records of NEC risk registers of past projects for managing NEC project risks remains unexplored. As such, a data-driven approach is proposed to categorise common risks of NEC projects and to analyse risk rating of risk categories by combining the use of text mining analysis and decision tree analysis. A practical case study in Hong Kong is used to illustrate the method of application. Top four common types of NEC project risks, which are ground and utilities, design information, structures, and workmanship, were identified, quantified, and analysed. The new approach helps NEC project planners to identify, quantify, and analyse NEC project risks time-efficiently.