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Managing complex construction projects is a challenging task because it involves multiple factors and decision-making processes. A systematic evaluation of these complex factors is imperative for achieving project success. As most of these factors are qualitative or intangible in nature, decision makers often rely on subjective judgements when comparing and evaluating them. The hybrid techniques that integrate fuzzy set theory and the analytic hierarchy process (AHP) are able to deal with such problems. This chapter discusses various hybrid techniques of the fuzzy AHP and presents an application of these techniques to the evaluation of transportation project complexity, which is essential for prioritising resource allocation and assessing project performance. Project complexity can be quantified and visualised effectively with the application of the fuzzy AHP. This chapter enhances the understanding of construction project complexity and fuzzy hybrid computing in construction engineering and management. Future research should address the calibration of fuzzy membership functions in pairwise comparisons for each individual decision maker and develop computational tools for solving optimisation problems in the constrained fuzzy AHP. In the area of construction project complexity, future research should investigate how scarce resources are allocated to better manage complex projects and how appropriate resource allocation improves their performance.

The purpose of this paper is to examine the relationship between building floor and labor productivity of the structural work including formwork installation and rebar fabrication/installation.

The case study methodology and learning curve theory are adopted for the paper. Records from the structural work of a 20-storey apartment building were analyzed to calculate floor-based labor productivities.

Labor productivity of the formwork activity increased more than twice in the first five floors. If the first cycle (floor 2) is omitted, the straight-line learning curve model shows a learning rate of 83.5 percent. Labor productivity of the rebar activity tended to increase in the first 15 floors. If the first two cycles are omitted, the straight-line learning curve model indicates a learning rate of 83.6 percent.

Future research is needed to examine and quantify factors that affect the level of learning in high-rise building construction. The relationship between building floor and labor productivity should be further investigated for other construction activities.

Practitioners should consider the relationship between building floor and labor productivity and learning effects when planning manpower and construction duration for individual activities and for a building.

The paper substantiates the hypothesis that labor productivity does not reach 100 percent of the normal level at the very first floors while they do not support the hypothesis that labor productivity does not reach 100 percent at the top floors.

This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.

In the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.

The authors got very satisfactory classification results.

DDPML system is specially designed to smoothly handle big data mining classification.

The purpose of this study is to enable the planning of construction projects with simultaneous consideration of time, cost and safety risks. It also aims to improve the decision-making process by evaluating the effectiveness of the Rao-2 algorithm in solving multi-objective time-cost-safety risk problems. In the end, this model is designed to support project managers in enhancing management approaches by addressing project challenges and constraints more efficiently.

In this study, the Rao-2 algorithm, along with Grey Wolf Optimization (GWO) and Whale Optimization algorithm (WOA), were improved using the crowding distance-based non-dominated sorting method. Rao-2 was first compared to GWO and WOA. Subsequently, it was compared with well-established algorithms in the literature, including genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE). The C-metric, hypervolume and spread metrics were employed for performance measurement. The performance of the algorithms was evaluated on four case studies consisting of 11, 13, 18 and 25 activities.

The results revealed that Rao-2 performs better than other algorithms as the number of activities increases, when compared using the Hypervolume, Spread and C-metric measures. In terms of performance measures, the GWO algorithm outperformed Rao-2 in some evaluation metrics for the instance involving 11 activities. However, as the number of activities grew, the Rao-2 method consistently generated higher-quality Pareto fronts and outperformed GWO and WOA in all evaluation metrics. The solutions generated by Rao-2 were also superior to those obtained from GA, PSO and DE in all case studies, further demonstrating the capability of our framework to produce a wide range of optimal solutions with high diversity across different case studies.

This research demonstrates that Rao-2 not only improves solution quality when generating Pareto fronts but also achieves better results with fewer function evaluations compared to GA, PSO and DE. The algorithm's efficiency makes it particularly well-suited for optimizing time, cost and safety risks in large-scale construction projects, which in turn positions Rao-2 as a better choice for such projects by producing superior results compared to other algorithms. By providing high-quality solutions with reduced computational demands, Rao-2 offers a faster and more resource-efficient tool for decision-making, contributing to advancements in both the theory and practice of construction project management.

The purpose of this paper is to investigate how on-site blockwork craft gangs’ learning impacts productivity within the production environment on-site to optimise their productivity.

The research is adopting a quantitative method with the observation of seven craft gangs’ blockwork with an average of five members in each gang, using the learning curve model application in a 17-storey tri-tower construction project in Nigeria. The linear regression method was employed in the analysis stage of this study using labour-recorded productivity time input as the dependent variables.

The paper provides empirical insights about the significance of on-site craft gangs’ learning. The overall blockwork craft gangs learning observed at the site level shows an average learning rate of 94.21 per cent resulting in 5.79 per cent improvement gains.

Due to the nature of the study and the research question, the observations in this research study were limited to FCDA construction project in Nigeria. The limitation of this scenario is that the research results may lack generalisability. Therefore, there is the need for further study on the learning rate.

This research study includes the implications for the development of on-site blockwork craft gangs learning; the significant impact of learning rate of 94.21 per cent resulting in 5.79 per cent improvement gain can be used in the planning and to fast track the productivity of craft gangs’ construction.

This paper identified the need to improve construction productivity through craft gangs’ on-site learning with the application of the learning curve theory.

COVID-19 hits every country’s health-care system and economy. There is a trend toward using automation technology in response to the COVID-19 crisis not only in developed countries but also in those with lower levels of technology development. However, current studies mainly focus on the world level, and only a few ones report deployments at the country level. The purpose of this paper is to investigate the use of automation solutions in Vietnam with locally available materials mainly in the first wave from January to July 2020.

The authors collected COVID-related automation solutions during the first wave of COVID-19 in Vietnam from January to July 2020 through a search process. The analysis and insights of a panel consisting of various disciplines (i.e. academia, health care, government, entrepreneur and media) aim at providing a clear picture of how and to what extent these solutions have been deployed.

The authors found seven groups of solutions from low to high research and development (R&D) levels deployed across the country with various funding sources. Low R&D solutions were widely spread owing to simplicity and affordability. High R&D solutions were mainly deployed in big cities. Most of the solutions were deployed during the first phases when international supply chains were limited with a significant contribution of the media. Higher R&D solutions have opportunities to be deployed in the reopening phase. However, challenges can be listed as limited interdisciplinary research teams, market demand, the local supporting industry, end-user validation and social-ethical issues.

To the authors’ best knowledge, this is the first study analyzing the use of automation technology in response to COVID-19 in Vietnam and also in a country in Southeast Asia. Lessons learned from these current deployments are useful for future emerging infectious diseases. The reality of Vietnam’s automation solutions in response to COVID-19 might be a reference for other developing countries with similar social-economic circumstances and contributes to the global picture of how different countries adopt technology to combat COVID-19.

Growth enterprise market (GEM) in Hong Kong is acknowledged as one of the world’s most successful examples of small and medium enterprise (SME) stock market. The purpose of this paper is to examine the evolving efficiency and dual long memory in the GEM. This paper also explores the joint impacts of thin trading, structural breaks and inflation on the dual long memory.

State-space GARCH-M model, Kalman filter estimation, factor-adjustment techniques and fractionally integrated models: ARFIMA–FIGARCH, ARFIMA–FIAPARCH and ARFIMA–HYGARCH are adopted for the empirical analysis.

The results indicate that the GEM is still weak-form inefficient but shows a tendency towards efficiency over time except during the global financial crisis. There also exists a stationary long-memory property in the market return and volatility; however, these long-memory properties weaken in magnitude and/or statistical significance when the joint impacts of the three aforementioned factors were taken into account.

A forecasts of the hedging model that capture dual long memory could provide investors further insights into risk management of investments in the GEM.

The findings of this study are relevant to market authorities in improving the GEM market efficiency and investors in modelling hedging strategies for the GEM.

This study is the first to investigate the evolving efficiency and dual long memory in an SME stock market, and the joint impacts of thin trading, structural breaks and inflation on the dual long memory.

The purpose of this paper is to identify variables and their effects on the value of technology transaction according to technology demand approach in Vietnam technology market, by testing the hypotheses including the effects of technology absorption capacity, internal research and development (R&D) productivity of firms and difficulties in external infrastructure on technology demand.

The technology transaction value and its impact factors are assessed using Vietnam annual enterprise survey and using technology in production survey from 2012 to 2016. The effects of factors on value of technology transaction are determined by using feasible generalized least squares model.

The results indicate three main points. First, companies having higher technology absorption capacity and higher dominance in the domestic or foreign markets tend to acquire higher technology demand in the technology market. Second, companies having lower internal R&D productivity tend to require higher external technology demand. Finally, higher level of difficulty from external infrastructure prevents enterprises in accessing technology demand.

The main limitation of the study is that data of firm’s R&D productivity are not available. The study also does not mention information flows from competitors that perhaps have potentially significant impacts on external technology demand of firms.

The paper includes policy implications for the government and industry managers to increase technology transaction value.

The focus of many previous research papers on technology transactions was generally to look at the decisive factors behind firm’s technology supply in both developed and developing countries. However, knowledge about firm’s technology demand is very limited, particularly in the context of developing countries. This paper clarifies the effect of factors on the decision buying external technology for innovation purpose and productivity improvement in Vietnamese manufacturing sector.

Emerging economies have been highlighted as an important growth source of the global economy. However, this group of countries has not received enough academic attention yet. Therefore, this study aims to identify the impact of research and development (R&D) expenditure on economic growth in emerging economies.

The theoretical framework of the production function is applied to quantitatively analyse the impact of R&D expenditure on economic growth with a sample of 29 emerging economies in the period between 1996 and 2019.

The panel cointegration test confirms the existence of long-run cointegration relationships between economic growth and independent variables in these emerging economies. Besides, the estimated results show that the national R&D expenditure has positive effects on economic growth from both direct and interaction dimensions. This evidence has filled the empirical research gap in the R&D-growth nexus in the case of emerging economies. Finally, while gross capital and education have positive impacts on growth, corruption has a harmful effect on economic growth in these countries.

The results highlight that policymakers should enhance R&D expenditure and R&D activities as the key national development strategy. The investment in R&D not only helps emerging economies avoid the middle-income trap but also pushes these countries to successfully join the group of developed countries.

To the best of the authors’ knowledge, this research is among the first to examine the impact of R&D expenditure on economic growth with a homogeneous sample of emerging economies. The results are obviously helpful for policymakers to use R&D as the key development strategy for supporting economic growth in emerging economies in the future.