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This paper is based on research addressing quality of construction schedules. The paper aims to structure a Schedule Health Assessment method and present it as a means to carry out the evaluation of construction schedules.

The development of the Schedule Health assessment method can be characterised as constructive research. The structuring of the method is based on analysis of factors forming the overall quality of construction schedules. The method has been tested in a proof of concept study. This comprised a case study in which four master schedules developed by junior production managers were evaluated using the Schedule Health assessment method.

It is possible to construct a method for the quality evaluation of construction schedules.

The completed testing is still rather limited since it is based merely on experiences of junior production managers with a single case.

The Schedule Health assessment method can in a useful manner make the quality evaluation of construction schedules easy to approach and effective process.

This research has produced a novel method for the quality evaluation of construction schedules.

The purpose of the study is to explore the dominant ideas in research on the management of time in construction. The focus of research has been to improve techniques for optimising the timing and sequence of activities.

A critical review of research on construction time management, challenging the typical focus. We examine the assumptions different authors make, underline the limitations of the dominant research approaches and examine the prospects for developing a new approach to researching these issues.

The dominant approach in literature focuses on unique activity traits in construction planning and measurable patterns between time-related variables. This assumes that time in construction can be managed by changing the way activities are calculated. These approaches have not been correlated with improvement in performance. Social practice theory may help to explain how programmes figure as one of many objects used during construction.

The focus is on reviewing indicative literature from key journals in construction management. The implication is that research is needed about how such documents are used in practice, which goes further than optimising plans in theory.

Future research could focus on understanding the context of construction planning practice and shift the debate from a focus on optimisation to practice.

An interpretivist approach with a focus on how tools such as planning documents are used on site. Social practice theory may provide a clearer explanation of the place of construction planning within the practice of construction management. This could provide solutions that deal effectively with stakeholder expectations around timely completion of construction projects.

The future construction site will be pervasive, context aware and embedded with intelligence. The purpose of this paper is to explore and define the concept of the digital skin of the future smart construction site.

The paper provides a systematic and hierarchical classification of 114 articles from both industry and academia on the digital skin concept and evaluates them. The hierarchical classification is based on application areas relevant to construction, such as augmented reality, building information model-based visualisation, labour tracking, supply chain tracking, safety management, mobile equipment tracking and schedule and progress monitoring. Evaluations of the research papers were conducted based on three pillars: validation of technological feasibility, onsite application and user acceptance testing.

Technologies learned about in the literature review enabled the envisaging of the pervasive construction site of the future. The paper presents scenarios for the future context-aware construction site, including the construction worker, construction procurement management and future real-time safety management systems.

Based on the gaps identified by the review in the body of knowledge and on a broader analysis of technology diffusion, the paper highlights the research challenges to be overcome in the advent of digital skin. The paper recommends that researchers follow a coherent process for smart technology design, development and implementation in order to achieve this vision for the construction industry.

The purpose of this research is to develop a generic framework of a digital twin (DT)-based automated construction progress monitoring through reality capture to extended reality (RC-to-XR).

IDEF0 data modeling method has been designed to establish an integration of reality capturing technologies by using BIM, DTs and XR for automated construction progress monitoring. Structural equation modeling (SEM) method has been used to test the proposed hypotheses and develop the skill model to examine the reliability, validity and contribution of the framework to understand the DRX model's effectiveness if implemented in real practice.

The research findings validate the positive impact and importance of utilizing technology integration in a logical framework such as DRX, which provides trustable, real-time, transparent and digital construction progress monitoring.

DRX system captures accurate, real-time and comprehensive data at construction stage, analyses data and information precisely and quickly, visualizes information and reports in a real scale environment, facilitates information flows and communication, learns from itself, historical data and accessible online data to predict future actions, provides semantic and digitalize construction information with analytical capabilities and optimizes decision-making process.

The research presents a framework of an automated construction progress monitoring system that integrates BIM, various reality capturing technologies, DT and XR technologies (VR, AR and MR), arraying the steps on how these technologies work collaboratively to create, capture, generate, analyze, manage and visualize construction progress data, information and reports.

Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

The coronavirus disease 2019 (COVID-19) pandemic has affected the global economy and, thus, the global construction industry. This paper aims to study the impact of COVID-19 on construction project performance in the United Arab Emirates (UAE).

This study adopted a qualitative and exploratory approach to investigate the impact of COVID-19 and its policies on project performance in the UAE construction industry in critical areas of the project management body of knowledge (e.g. schedule, cost, resources and contracts). Semi-structured interview questions were asked from ten construction professional to obtain valuable insights into the pandemic’s effects on the UAE construction industry and the effectiveness of policies implemented to rectify the damage and identify the industry’s new normal.

The findings indicate that the construction industry faced several challenges such as schedule delays, disrupted cashflows, delayed permits, approvals and inspections, travel restrictions, serious health and safety concerns, material and equipment shortages, among others which hindered the timely delivery of construction projects. It also indicates that efforts made by the government institutions and the construction industry of the UAE such as economic support programs, digitization of processes, fee and fine waivers, health facilities, among other statutory relaxations proved effective in supporting the construction industry against the adverse effects of the pandemic.

The research findings are limited to the literature review and ten semi-structured interviews seeking an expert’s opinion from industry professionals working in the UAE construction industry. The research team did not get access to project documents, contracts and project progress reports which may be required to validate the interview findings, and to perform an in-depth analysis quantifying the impact of COVID 19 on construction projects performance, which is a limitation of this research.

The implication is that, owing to the imposed lockdowns and strict precautionary measures to curb the rapid spread of the pandemic, smooth execution of the construction project across the country was affected. The government institutions and stakeholders of the construction projects introduced and implemented various techniques and solutions which effectively handled the implications of the COVID-19 pandemic on the construction industry of the UAE.

This study has identified the challenges faced by the construction industry of the UAE in the context of the management of project schedule, project cost, construction contracts, health and safety of construction employees and other related aspects of the construction projects. This study also identified the techniques and solutions adopted by various public and private institutions of the country and their implications on construction projects. Therefore, this study provides guidelines for policymakers and future research studies alike.

This paper aims to study the effects of different weather conditions on typical concrete work tasks’ productivity. Weather is one important factor that has a negative impact on construction productivity. Knowledge about how weather affects construction works is therefore important for the construction industry, e.g. during planning and execution of construction projects.

A questionnaire survey method is used involving means to perform pairwise comparisons of different weather factors according to the analytical hierarchical process (AHP). The survey also contains means to enable assessment of the loss in productivity for typical work tasks exposed to different weather types. The survey targets practitioners involved in Swedish concrete construction projects, and the results are compared with previous research findings.

The survey covers responses from 232 practitioners with long experience of concrete construction. The pairwise comparisons reveal that practitioners rank precipitation as the most important followed by wind and temperature. The loss in productivity varies significantly (from 0 to 100%) depending on the type of work and the type of weather factor considered. The results partly confirm findings reported in previous research but also reveal a more complex relationship between weather and productivity indicating several underlying influencing factors such as type of work, type of weather (e.g. rain or snow) and the intensity of each weather factor.

This paper presents new data about how 232 practitioners assess the effects of weather on construction productivity involving novel means to perform objective rankings such as the AHP methodology.

The purpose of this paper is to define the integration capability dimensions and create a model for self-assessing the integration capability in inter-organizational projects.

A theoretical construct of, referred in this study as integration capability framework is elaborated following a systematic literature review. Thereafter, an integration capability self-assessment model, based on maturity thinking, is derived from the theoretical framework. The self-assessment model is further developed and tested for validity within five inter-organizational project networks in cooperation with industry practitioners, representing construction, industrial engineering, and mining sectors.

The results show that inter-organizational projects can use the developed model in self-assessing the maturity levels of various integration mechanisms, thus the state of integration capability at any point in time during inter-organizational projects.

This study is an attempt to identify how the integration capability dimensions can be self-assessed in inter-organizational projects, through the maturity levels of various integration mechanisms. The results offer insights for both academics and project management practitioners.

This study aims to evaluate the efficiency of various techniques for enhancing indoor air quality (IAQ) in construction. It analyzed the alterations in the concentration of indoor air pollutants over time for each product employed in controlling pollution sources and removing it, which included eco-friendly substances and adsorbents. The study will provide more precise and dependable data on the effectiveness of these control methods, ultimately supporting the creation of more efficient and sustainable approaches for managing indoor air pollution in buildings.

The research investigates the impact of eco-friendly materials and adsorbents on improving indoor air quality (IAQ) in Dubai's tall apartment buildings. Field experiments were conducted in six units of The Gate Tower, comparing the IAQ of three units built with “excellent” grade eco-friendly materials with three built with “good” grade materials. Another experiment evaluated two adsorbent products (H and Z) in the Majestic Tower over six months. Results indicate that “excellent” grade materials significantly reduced toluene emissions. Adsorbent product Z showed promising results in pollutant reduction, but there is concern about the long-term behavior of adsorbed chemicals. The study emphasizes further research on household pollutant management.

The research studied the effects of eco-friendly materials and adsorbents on indoor air quality in Dubai's new apartments. It found that apartments using “excellent” eco-friendly materials had significantly better air quality, particularly reduced toluene concentrations, compared to those using “good” materials. However, high formaldehyde (HCHO) emissions were observed from wood products. While certain construction materials led to increased ethylbenzene and xylene levels, adsorbent product Z showed promise in reducing pollutants. Yet, there is a potential concern about the long-term rerelease of these trapped chemicals. The study emphasizes the need for ongoing research in indoor pollutant management.

The research, while extensive, faced limitations in assessing the long-term behavior of adsorbed chemicals, particularly the potential for rereleasing trapped pollutants over time. Despite the study spanning a considerable period, indoor air pollutant concentrations in target households did not stabilize, making it challenging to determine definitive improvement effects and reduction rates among products. Comparisons were primarily relative between target units, and the rapid rise in pollutants during furniture introduction warrants further examination. Consequently, while the research provides essential insights, it underscores the need for more prolonged and comprehensive evaluations to fully understand the materials' and adsorbents' impacts on indoor air quality.

The research underscores the importance of choosing eco-friendly materials in new apartment constructions for better IAQ. Specifically, using “excellent” graded materials can significantly reduce harmful pollutants like toluene. However, the study also highlights that certain construction activities, such as introducing furniture, can rapidly elevate pollutant levels. Moreover, while adsorbents like product Z showed promise in reducing pollutants, there is potential for adsorbed chemicals to be rereleased over time. For practical implementation, prioritizing higher-grade eco-friendly materials and further investigation into furniture emissions and long-term behavior of adsorbents can lead to healthier indoor environments in newly built apartments.

The research offers a unique empirical assessment of eco-friendly materials' impact on indoor air quality within Dubai's rapidly constructed apartment buildings. Through field experiments, it directly compares different material grades, providing concrete data on pollutant levels in newly built environments. Additionally, it explores the efficacy of specific adsorbents, which is of high value to the construction and public health sectors. The findings shed light on how construction choices can influence indoor air pollution, offering valuable insights to builders, policymakers and residents aiming to promote public health and safety in urban living spaces.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.