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Many economic, political and socio-cultural events in the 2020s have been strong headwinds for architecture, engineering and construction (AEC). Nevertheless, technological advancements (e.g. artificial intelligence (AI), big data and robotics) provide promising avenues for the development of AEC. This study aims to map the state of the literature on automation in AEC and thereby be of value not only to those researching automation and its composition of a variety of distinct technological and system classes within AEC, but also to practitioners and policymakers in shaping the future of AEC.

This review adopts scientometric methods, which have been effective in the research of large intra and interdisciplinary domains in the past decades. The full dataset consists of 1,871 articles on automation in AEC.

This overarching scientometric review offers three interdisciplinary streams of research: technological frontiers, project monitoring and applied research in AEC. To support the scientometric analysis, the authors offer a critical integrative review of the literature to proffer a multilevel, multistage framework of automation in AEC, which demonstrates an abundance of technological paradigm discussions and the inherent need for a holistic managerial approach to automation in AEC.

The authors underline employee well-being, business sustainability and social growth outcomes of automation and provide several managerial implications, such as the strategic management approach, ethical management view and human resource management perspective. In doing so, the authors seek to respond to the Sustainable Development Goals proposed by the United Nations as this becomes more prevalent for the industry and all levels of society in general.

Multinational business deliver value via multiple sites with similar operational capacities. The age of the Fourth Industrial Revolution (4IR) delivers significant opportunities for the deployment of digital tools for business optimization. Therefore, this study aims to study the Industry 4.0 implementation for multinationals.

The key objective of this research is multi-site systems integration using a reproducible, modular and standardized “Cyber Physical System (CPS) as-a-Service”.

A best practice reference architecture is adopted to guide the design and delivery of a pioneering CPS multi-site deployment. The CPS deployed is a cloud-based platform adopted to enable all manufacturing areas within a multinational energy and petrochemical company. A methodology is developed to quantify the system environmental and sustainability benefits focusing on reduced carbon dioxide (CO₂) emissions and energy consumption. These results demonstrate the benefits of standardization, replication and digital enablement for multinational businesses.

The research illustrates the ability to design a single system, reproducible for multiple sites. This research also illustrates the beneficial impact of system reuse due to reduced environmental impact from lower CO₂ emissions and energy consumption. The paper assists organizations in deploying complex systems while addressing multinational systems implementation constraints and standardization.

Modular integrated construction (MiC) is a modern construction method innovating and reinventing the traditional site-based construction method. As it integrates advanced manufacturing principles and requires offsite production of volumetric building components, several factors and conditions must converge to make the MiC method suitable and efficient for building projects in each context. This paper aims to present a knowledge-based decision support system (KB-DSS) for assessing a project’s suitability for the MiC method.

The KB-DSS uses 21 significant suitability decision-making factors identified through literature review, consultation of experts and questionnaire surveys. It has a knowledge base, a DSS and a user interface. The knowledge base comprises IF-THEN production rules to compute the MiC suitability score with the efficient use of the powerful reasoning and explanation capabilities of DSS.

The tool receives the inputs of a decision-maker, computes the MiC suitability score for a given project and generates recommendations based on the score. Three real-world projects in Hong Kong are used to demonstrate the applicability of the tool for solving the MiC suitability assessment problem.

This study established the complex and competing significant conditions and factors determining the suitability of the MiC method for construction projects. It developed a unique tool combining the capabilities of expert systems and decision support system to address the complex problem of assessing the suitability of the MiC method for construction projects in a high-density metropolis.

Modular capacity units enable rapid reconfiguration, providing tactical flexibility to efficiently meet customer demand during disruptions and ensuring sustainability. Moreover, the Physical Internet (PI) enhances the potential of modular capacity in addressing efficiency, sustainability, and resilience challenges. To evaluate the sustainability and resilience advantages of the PI-enabled reconfigurable modular system (PI-M system), this paper studies a PI-enabled sustainable and resilient production-routing problem with modular capacity.

We develop a multi-objective optimization model to assess the sustainability and resilience benefits of combining PI and modular capacity in a chemical industry case study. A hybrid solution approach, combining the augmented e-constraint method, construction heuristic, and hybrid adaptive large neighborhood search, is developed.

The experimental results reveal that the proposed solution approach is capable of obtaining better solutions than the Gurobi and the existing heuristic in a shorter running time. Moreover, compared with the traditional system, the PI only and traditional with modular capacity systems, PI-M system has significant advantages in both sustainability and resilience.

To the best of our knowledge, this study is the first to integrate the PI and modular capacity and investigate sustainability and resilience in the production-routing problem.

To incentivize innovation, support competitiveness, lower skill scarcities, and alleviate the housing affordability difficulty, proponents underscore the pertinence of embracing contemporary construction methodologies, with particular emphasis on volumetric modular construction (VMC) as a sustainable paradigm for production and consumption. However, construction industry stakeholders in Australia have encountered profound challenges in adopting VMC, as its adoption remains significantly low. Therefore, this study investigated the constraints that hinder VMC in the Australian construction industry.

The study used qualitative methodology using semi-structured interviews as a core approach to glean professional experts' perspectives and insights, along with Pareto and mean index score analyses.

The study identified 77 reported and validated VMC constraints by professionals, categorizing them into eight categories: cultural, economic, knowledge, market, regulatory, stakeholder, supply chain, and technological. The mean index score analysis reveals stakeholder (µ = 9.67) constraints are the most significant, followed by cultural (µ = 9.62) and regulatory (µ = 9.11) constraints. Pareto analysis revealed 25 of the 77 constraints as ‘vital few” among different categories. This study presented causal relationships and mitigation strategies for VMC constraints, followed by an argument on whether VMC adoption in Australia requires a nudge or mandate.

This study offers guidance for efficient resource allocation, aiding management and government policy formulation. It's also valuable for global audiences, especially countries transitioning to modular construction.

This is one of the first studies to identify VMC constraints and delineate them into different categories in Australia, identify their causal interrelationships, and deliver countermeasures to overcome them.

This paper aims to present an overview of permanent magnet linear flux-switching machines (PMLFSM), field excited LFSM and hybrid excited LFSM (HELFSM) topologies as presented in literature for transportation systems such as high-speed trains and maglev systems.

The structural designs of different configurations are thoroughly investigated, and their respective advantages and disadvantages are examined. Based on the geometry and excitation sources, a detailed survey is carried out. Specific design and space issues, such as solid and modular structures, structure strength, excitation sources placement, utilization of PM materials, and flux leakage are investigated.

PMLFSM provide higher power density and efficiency than induction and DC machines because of the superior excitation capability of PMs. The cost of rare-earth PMs has risen sharply in the past few decades because of their frequent use, so the manufacturing cost of PMLFSM is increasing. Owing to the influence of high-energy PMs and magnetic flux concentration, the efficiency and power density are higher in such machines. PM is the only excitation source in PMLFSM and has constant remanence, limiting its applications in a wide speed operation range. Therefore, the field winding is added in the PMLFSM to flexibly regulate the magnetic field, making it a hybrid excited one. The HELFSM possess better flux linkage, high thrust force density and better flux controlling ability, leading to a wide speed range. However, the HELFSM have problems with the crowded mover, as PM, field excited and armature excitation are housed on a short mover. So, for better performance, the area of each excitation component has to compete with each other.

Transportation of goods and people by vehicles is becoming increasingly prevalent. As railways play a significant role in the transportation system and are an integral part of intercity transportation. So, this paper presents an overview of various linear machines that are presented in literature for rail transit systems to promote sustainable urban planning practices.

Formwork design and construction for reinforced concrete buildings take significant time, effort and money. Construction procedures are time-consuming for designers and costly for the contractor. Poor engineering decisions have led to several workplace accidents in the construction industry. This paper aims to present an integrated building information modeling – genetic algorithm (BIM-GA) model to automate formwork design, 3D visualization and optimization.

Data are precisely extracted from a 3D structural model and used to optimize formwork design based on available formwork components and prices. Optimization models are made using GA approach. A library of 3D formwork components was modeled and stored using Revit. The optimized design solution thereafter would be visualized automatically in Revit to readily acquire formwork quantities schedules and shop drawings.

A case study illustrating the proposed approach demonstrated that using BIM will reduce formwork design, quantification and drawing time by more than 50% of the traditional approach with safer design and accurate results due to process automation and optimize cost for the given data.

This research introduces an innovative integrated BIM and GA model for the optimization and automation of slab formwork design, which has significantly benefited the construction industry. The utilization of GA in the optimization process allows for the attainment of an optimal formwork design, ultimately leading to a reduction in construction cost and time.

Evaluating project success within the construction industry presents challenges due to the unique characteristics of the sector, the complexity of projects, and the involvement of diverse stakeholders. Conducting a bibliometric analysis, this paper aims to unravel the major research themes and methodologies utilised by researchers in studying the critical success criteria for construction projects, as well as extracting these success criteria.

The researchers systematically searched and screened 95 papers from Scopus and Web of Science (WoS) databases. This study conducted research focus parallelship network (RFPN) analysis and keywords co-occurrence network (KCON) analysis using BibExcel and Gephi to cluster the papers, illuminate the relationships among keywords within each cluster, and identify the primary research directions.

Using the RFPN analysis, this study classified the papers into three distinct clusters: infrastructure and public projects success, risk and knowledge management, and contractors and procurement management. Statistical techniques such as structural equation modelling (SEM) and multi-criteria decision-making methods such as analytic hierarchy process (AHP) have been used to analyse project success in the construction industry.

Considering the intensified demand for streamlined digital interactions and the increasing emphasis on sustainability and safety performance, construction companies are recommended to allocate greater investments toward the automation and digitisation of their products and processes. Prioritising modular construction and embracing transformative technologies alongside data science is crucial for enabling well-informed decision-making, and enhancing project success.

This study contributes to the existing body of knowledge by conducting a quantitative and systematic evaluation of the literature on project success criteria in the construction industry and uncovering key research areas. It addresses the pressing need to understand the complexities of construction projects amidst evolving industry dynamics and emerging disruptions. Moreover, by highlighting the implications of digital innovations and modular construction, this study urges deeper exploration into their impact on project performance and stakeholder satisfaction. This research sets a comprehensive framework for investigating the interplay between project complexity, technological advancements, and sustainable practices in the construction sector, paving the way for strategic advancements in the field.

Recent research advocates that there are plenty of opportunities for key players in the offsite construction (OSC) sector to harness the full potential of advanced project management techniques. While previous research mainly focuses on transformations related to digital and advanced technologies driven by industry 4.0 principles, a research gap still exists on the intersection of project management capabilities and OSC. This study attempts to bridge this gap by capturing the homogeneity of different capabilities and integrating them into an overarching framework.

A scientometric analysis is conducted to provide an overview of the co-occurrence network of keywords in the representative studies. A systematic literature review (SLR) of articles published between 2010 and 2022, followed by a subsequent full-text examination of 63 selected articles, revealed 34 interrelated capabilities to be categorised under three exhaustive planning-oriented, design-oriented and delivery-oriented groups.

This review revealed an upward trend of publication on project management capabilities for OSC with a specific interest in optimisation of resources allocated to offsite operations. The top five capabilities discussed more frequently in the literature include (1) artificial intelligence for design error detection, (2) enhanced resource productivity, (3) cost saving in offsite production, (4) real-time traceability of modules and (5) applying lean agile production principles to OSC, which imply the critical role of quality, cost saving, traceability and agility in OSC.

This study elicits core capabilities and develops a new offsite project management framework for the first time. The authors provide directions for researchers and practitioners to apply capabilities for obtaining better outcomes and higher value out of offsite operations.

The purpose of this systematic review is to critically analyze pharmaceutical industry case studies on the implementation of Lean 4.0 methodologies to synthesize key lessons, benefits and best practices. The goal is to inform decisions and guide investments in related technologies for enhancing quality, compliance, efficiency and responsiveness across production and supply chain processes.

The article utilized a systematic literature review (SLR) methodology following five phases: formulating research questions, locating relevant articles, selecting and evaluating articles, analyzing and synthesizing findings and reporting results. The SLR aimed to critically analyze pharmaceutical industry case studies on Lean 4.0 implementation to synthesize key lessons, benefits and best practices.

Key findings reveal recurrent efficiency gains, obstacles around legacy system integration and data governance as well as necessary operator training investments alongside technological upgrades. On average, quality assurance reliability improved by over 50%, while inventory waste declined by 57% based on quantified metrics across documented initiatives synthesizing robotics, sensors and analytics.

As a comprehensive literature review, findings depend on available documented implementations within the search period rather than direct case evaluations. Reporting bias may also skew toward more successful accounts.

Synthesized implementation patterns, performance outcomes and concealed pitfalls provide pharmaceutical leaders with an evidence-based reference guide aiding adoption strategy development, resource planning and workforce transitioning crucial for Lean 4.0 assimilation.

This systematic assessment of pharmaceutical Lean 4.0 adoption offers an unprecedented perspective into the real-world issues, dependencies and modifications necessary for successful integration, absent from conceptual projections or isolated case studies alone until now.