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The engineering construction standards in China play an important role in protecting the safety of the construction projects. They are the basic principles that standardize the construction activities and guarantee the quality of projects. However, there are many barriers that affect the adoption of the engineering construction standards. Therefore, the purpose of this paper is to investigate the barriers that challenge the adoption of the engineering construction standards in China.

The research reveals the barriers that affect the implementation of the engineering construction standards in China through a literature review. Then this study uses factor analysis to analyze 12 indices which we get from a questionnaire to build explanations from the results.

According to this paper, four main brands of uncorrelated variables are derived which are the main challenges in implementing the engineering construction standards in China: management barriers, policy barriers, knowledge barriers and market barriers. This paper gives a clear classification of the barriers that the enterprises face while adopting the engineering construction standards in China.

This paper makes a contribution to the understanding of the barriers that affect the adoption of the engineering construction standards in China.

Past studies highlight a wide range of labour-related problems resulting in productivity loss in the construction industry of many developing countries. This study aims to quantify the impacts of labour-related factors on the productivity of construction operations in Sri Lankan building projects based on the viewpoint of engineers and construction managers for upgrading management/organisational policies and practices.

Qualitative and quantitative approaches were used to identify the critical factors. Further, a series of industry consultative discussions were conducted through problem-based communication approaches to analyse the actions required.

A total of 21 factors were determined as critical, where skills shortage, labourers’ thinking abilities, work experience, knowledge in construction works and discipline were leading in the list. The statistical tests and the experts’ discussion outcomes ensured the validity and reliability of the study findings.

The study outcomes will contribute to finding out better ways for directing labour in the industry practices and revising organisational policies towards achieving higher productivity levels in construction operations. Though the study findings are limited to the Sri Lankan context, some findings may be tested in other developing countries in similar scenarios.

The study findings show why the identified factors are critical, how those influence construction practices and what actions need to be considered for addressing the industry’s productivity-related challenges. These can play a key role in upgrading the construction management practices and organisational policies to the near-future stages.

Competitive tendering in South Africa is often associated with procurement based on the lowest fee tendered. Previous research on this topic did not provide in-depth examinations of how pricing within consulting engineering companies was affected by competitive tendering nor did it illuminate the extent to which professional services were impacted by competitive tendering. This paper aims to examine the implications of competitive tendering on pricing and delivery of consulting engineering services in South Africa.

A survey research strategy with a questionnaire as the research instrument elicited qualitative data from 28 experienced consulting engineers in South Africa. Thematic analysis was used to analyse qualitative data from the questionnaires.

Three key themes were identified, namely: considerations when determining consulting engineering fees on competitively tendered projects; the impact of reduced fees due to competitive tendering on the delivery of consulting engineering services; and interventions to prevent unsustainably “low” professional fees. Many consulting engineers in South Africa still determine fees using fee scales, while other considerations include resources, project complexity, risk, etc. Most participants asserted that design optimisation/value engineering, training, meetings and construction monitoring were adversely impacted by “low” fees.

This paper provides in-depth qualitative feedback from experienced consulting engineers (most having more than 20 years’ experience) on a topical issue in the South African construction industry. Thematic analysis was a novel method of analysis that was not used previously in this area of study.

Prior research has emphasised the importance of the early phases of construction projects, as well as the difficulties of procuring engineering services – especially due to the uncertainties. Despite that, studies on the public procurement of engineering services are scarce. Although scholars have shown that uncertainty may affect the choice of control modes, the level of uncertainty that characterises services is not addressed by the two task characteristics: knowledge of the transformation process and output measurability. The purpose is to investigate organisational control in public procurement of engineering services.

The existing control model was adjusted in this study by conceptually adding uncertainty as a third aspect to the two task characteristics. A single case study of the Swedish Transport Administration was used. The empirical data, comprising 14 interviews with managers from the client and engineering consulting companies, were analysed using flexible pattern matching and visual mapping approaches and then illustrated using the model.

The public client did not base its choice of control modes on uncertainty, but rather on the other two task characteristics. Consequently, the service providers argued that the chosen control modes reduced their creativity, increased their financial risks and caused unclear responsibilities. This study therefore shows that uncertainty is an important factor to consider in the choice of control modes, both from a theoretical perspective and from the service providers' point of view. The developed model may therefore be useful for researchers as well as practitioners.

This study is the first attempt to add uncertainty as a task characteristic when choosing control modes. The results contribute to the scarce control literature regarding the procurement of engineering services for construction projects and the procurement of other services with high uncertainty.

Rapid advancements in blockchain technology transform various sectors, attracting the attention of industrialists, practitioners, policymakers and academics, and profoundly affect construction businesses through smart contracts and crypto-economics. This paper explores the blockchain innovation ecosystem in construction.

Through a qualitative study of 23 diverse interviewees, the study explores how open or closed the blockchain innovation ecosystem in construction is and who its emerging orchestrators are.

The data showed that construction aims towards an open innovation blockchain ecosystem, although there are elements of hybridisation and closedness, each system pointing out to different orchestrators.

The study has implications for governments and large companies in construction, showing that open innovation initiatives need to be encouraged by policymakers through rules, regulations and government-sponsored demonstrator projects.

The data showed that there is lack of readiness for business model change to support open innovation blockchain ecosystems in construction.

This is the first study applying the open innovation theory in the construction industry and sheds light into the phenomenon of blockchain, suggesting routes for further democratisation of the technology for policymakers and practitioners.

Building Information Modeling (BIM) is a revolutionary innovation in the construction industry to virtually design and mange projects throughout the building lifecycle. Although Estonia is one of the foremost countries in the Information and Communications Technology (ICT) sector, BIM adoption in the Estonian construction industry is still lagging behind other countries. This paper is part of doctoral research that aims to determine the barriers to BIM adoption and develop a framework for effective implementation of BIM in the Estonian construction industry. The purpose of this paper is to examine the status of BIM adoption, BIM benefits and common barriers to BIM adoption in the construction industry worldwide.

The methodology used in this study is a literature review of journal articles, conference proceedings and published reports from various sources.

This study showed BIM benefits through building lifecycle phases and explored the BIM adoption rate in the construction industry of various countries. Eighteen barriers to BIM adoption were also identified.

The study presented is limited to a literature review – some related literature may have been missed.

The main practical significance of this study is that the findings can be used to inform a further survey to model the barriers to BIM adoption in the Estonian construction industry.

This study offers information on BIM adoption in the construction industry and will form the basis of further research.

The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.

This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).

The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.

The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.

This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.

The purpose of this paper is to analyze the potential for implementing Sustainable Development Goals (SDGs) into the civil engineering bachelor degree in the School of Civil Engineering at Universitat Politècnica de València (Spain).

All the 2019/2020 course syllabi were analyzed to diagnose at which extent each subject within the program curriculum contributes to achieving the different SDGs.

The results show a promising starting point as 75% of the courses address or have potential to address targets covering the 2030 Agenda. This paper also presents actions launched by the School of Civil Engineering to boost the SDGs into the civil engineering curriculum.

This paper presents a rigorous and systematic method that can be carried out in different bachelor degrees to find the subjects that have the potential to incorporate the SDGs into their program. This paper also presents actions launched by the Civil Engineering School to boost the SDGs into the civil engineering curriculum.

The importance of robotics and automation (R&A) in delivering a safe built environment cannot be overemphasised. This is because R&A systems can execute a hazardous job function that the construction workforce may not execute. Based on this knowledge, this study aims to present the result of an assessment of the impediments to the deployment of R&A for a safe and healthy construction environment.

This study adopted a post-positivist philosophical stance, using a quantitative research approach and a questionnaire administered to construction professionals in South Africa. The data gathered were analysed using frequency, percentage, mean item score, Kruskal–Wallis H-test, exploratory factor analysis and partial least square structural equation modelling (SEM).

This study revealed that the impediments to the deployment of R&A could be grouped into: industry, technology, human and cost-related factors. However, SEM assessment showed that only the industry, human and cost-related factors would significantly impact attaining specific health and safety-related outcomes.

The findings offer valuable benefits to construction organisations as the careful understanding of the identified impeding factors can help lead to better deployment of R&A and the attainment of its inherent safety benefits.

This study attempts to fill the gap in the shortage of literature exploring the deployment of R&A for a safe construction environment, particularly in developing countries like South Africa, where such studies are non-existent. This paper, therefore, offers a theoretical backdrop for future works on R&A deployment, particularly in developing countries where such a study has not been explored.

This study reviews the extent of application of artificial intelligence (AI) tools in the construction industry.

A thorough literature review (based on 165 articles) was conducted using Elsevier's Scopus due to its simplicity and as it encapsulates an extensive variety of databases to identify the literature related to the scope of the present study.

The following items were extracted: type of AI tools used, the major purpose of application, the geographical location where the study was conducted and the distribution of studies in terms of the journals they are published by. Based on the review results, the disciplines the AI tools have been used for were classified into eight major areas, such as geotechnical engineering, project management, energy, hydrology, environment and transportation, while construction materials and structural engineering. ANN has been a widely used tool, while the researchers have also used other AI tools, which shows efforts of exploring other tools for better modelling abilities. There is also clear evidence of that studies are now growing from applying a single AI tool to applying hybrid ones to create a comparison and showcase which tool provides a better result in an apple-to-apple scenario.

The findings can be used, not only by the researchers interested in the application of AI tools in construction, but also by the industry practitioners, who are keen to further understand and explore the applications of AI tools in the field.

There are no studies to date which serves as the center point to learn about the different AI tools available and their level of application in different fields of AEC. The study sheds light on various studies, which have used AI in hybrid/evolutionary systems to develop effective and accurate predictive models, to offer researchers and model developers more tools to choose from.