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This paper aims to communicate the relevant management model with optimal performance in the collection of construction resources (human, non-human), supervision of workers and activities in the development of adequate and quality school infrastructure facilities.

The study employed a mixed methods research approach in collecting and analysing research findings. Semi-structured face-to-face interviews and questionnaires which consisted of closed questions were the data collection tools. Numerical data collected were processed and analysed by using quantitative methods and techniques such as descriptive analysis, whereas text data were subjected to content analysis.

The findings revealed that the Post-New Public Management (PNPM) is relevant in the collection of construction resources and suitable for the supervision of workers and tasks towards the development of adequate and quality school infrastructure facilities. Based on the evidence generated, the PNPM model was recommended to be adapted to collection of construction resources and supervision of workers and activities.

This paper highlights the need for construction committees and other education policy implementers to adapt a relevant management model in realising optimal performance in the development of adequate and quality school infrastructure facilities.

Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.

This chapter explores middle leadership identity through the real-world accounts of how two middle leaders construct and develop their leadership identity and how this impacts the way their middle leadership is practiced. Leadership identity, an internal narrative of oneself as a leader which is practised professionally in context, represents a concept that is best understood as being unique to an individual, enduring over time, and a consequence of human experiences. Middle leadership is often the first promotion for teachers from teacher to leader and, as such, how middle leaders perceive themselves as a leader and how this formative process of leadership identity underpins middle leaders’ practices can make a significant impact on a leader’s decision making, professional relationships, behaviours, and actions. This chapter is co-authored by two researchers and two middle leaders with the intention of understanding middle leader identity development and its influence on middle leadership practices. Using interviews, middle leaders’ stories of identity were co-composed and re-storied to construct each middle leader’s narrative. This chapter concludes with a discussion on the influences of identity for middle leaders and considerations for leadership development.

The purpose of this study is to identify the possible efficiency gains in using tokenization for the execution of public expenditure on governmental investments.

Through design science research methodology, the exploratory research produced a tokenized prototype in the blockchain, through the Ernst and Young OpsChain traceability solution, allowing automated processes in the stages of public expense. A focus group composed of auditors from the public sector evaluated the possibility of improving the quality of information available in the audited entities, where the tokens created represent and register the actions of public agents in the blockchain Polygon.

The consensus of the experts in the focus group indicated that the use of tokenization could improve the quality of the information, since the possibility of recording the activities of public agents in the metadata of the tokens at each stage of the execution of the expenditure allows the audited entities the advantages of the information recorded on the blockchain, according to the following ranking: first the immutability of audited data, followed by reliability, transparency, accessibility and efficiency of data structures.

This research makes an empirical contribution to the real use of tokenization in blockchain technology to the public sector through a value chain in which tokens were created and moved between the wallets of public agents to represent, register and track the operations regarding public expense execution.

Completing Right-of-Way (ROW) acquisition process on schedule is critical to avoid delays and cost overruns on transportation projects. However, transportation agencies face challenges in accurately forecasting ROW acquisition timelines in the early stage of projects due to complex nature of acquisition process and limited design information. There is a need of improving accuracy of estimating ROW acquisition duration during the early phase of project development and quantitatively identifying risk factors affecting the duration.

The quantitative research methodology used to develop the forecasting model includes an ensemble algorithm based on decision tree and adaptive boosting techniques. A dataset of Georgia Department of Transportation projects held from 2010 to 2019 is utilized to demonstrate building the forecasting model. Furthermore, sensitivity analysis is performed to identify critical drivers of ROW acquisition durations.

The forecasting model developed in this research achieves a high accuracy to predict ROW durations by explaining 74% of the variance in ROW acquisition durations using project features, which is outperforming single regression tree, multiple linear regression and support vector machine. Moreover, number of parcels, average cost estimation per parcel, length of projects, number of condemnations, number of relocations and type of work are found to be influential factors as drivers of ROW acquisition duration.

This research contributes to the state of knowledge in estimating ROW acquisition timeline through (1) developing a novel machine learning model to accurately estimate ROW acquisition timelines, and (2) identifying drivers (i.e. risk factors) of ROW acquisition durations. The findings of this research will provide transportation agencies with insights on how to improve practices in scheduling ROW acquisition process.

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 develop a building information modelling (BIM)-based multi-objective optimization (MOO) framework for volumetric analysis of buildings during early design stages. The objective is to optimize volumetric spaces (3D) instead of 2D spaces to enhance space utilization, thermal comfort, constructability and rental value of buildings

The integration of two fundamental concepts – BIM and MOO, forms the basis of proposed framework. In the early design phases of a project, BIM is used to generate precise building volume data. The non-sorting genetic algorithm-II, a MOO algorithm, is then used to optimize extracted volume data from 3D BIM models, considering four objectives: space utilization, thermal comfort, rental value and construction cost. The framework is implemented in context of a school of architecture building project.

The findings of case study demonstrate significant improvements resulting from MOO of building volumes. Space utilization increased by 30%, while thermal comfort improved by 20%, and construction costs were reduced by 10%. Furthermore, rental value of the case study building increased by 33%.

The proposed framework offers practical implications by enabling project teams to generate optimal building floor layouts during early design stages, thereby avoiding late costly changes during construction phase of project.

The integration of BIM and MOO in this study provides a unique approach to optimize building volumes considering multiple factors during early design stages of a project

Studies showed that digital technology applications in the construction industry are low, especially in many developing nations. Construction incentivisation (CI) is one of the long-standing principles adopted to enhance project performance. There is a paucity of studies concerning CI to improve digital technology applications. Thus, this research investigated the relevance and perceived hindrances that may hinder the implementation of CI from promoting digital technologies and proffer ways to improve digital technology applications in the construction sector.

In Nigeria’s context, this research is exploratory. Twenty-four semi-structured virtual interviews were conducted in Lagos and Abuja, Nigeria, with knowledgeable participants that indicated interest and were interviewed. The engaged interviewees were drawn from government agencies, academicians in construction consultancy, Internet and communication technology experts, construction contracting firms and construction consulting firms. The collected data were coded and analysed through a thematic method.

Digitalisation of the industry via CI may face some hindrances. The perceived issues that may hinder CI implementation were classified into most severe, severe and fairly severe in Nigeria’s construction industry context. Findings proffer feasible policy solutions that can mitigate these issues and improve digital technology applications in the industry via the CI.

This study covered the relevance and perceived issues that may hinder the implementation of the CI to improve digital technology applications in the industry. Also, the study proffers policy solutions to enhance digital technology applications in the industry via the CI concept.

Findings from this research will support and offer a valuable understanding of the relevance of the “incentivisation concept” to improve digital technology applications in the Nigerian-built environment. Other developing countries with low applications of digital technology in construction may consider the suggested policy solutions from this research. Also, this study will stir policymakers and construction practitioners to support policies tailored towards improving digital technology applications in construction.

This research contributes by exploring the effectiveness of the CI concept and informing construction practitioners and policymakers on how to improve digital technology applications in the Nigerian construction industry.

This study aims to construct a mental health service system for middle school students in the post-COVID-19 era with the framework of Six Sigma DMAIC (define, measure, analyze, improve and control) and analyze the influencing factors of the mental health service system to study the implementation strategies of quality-oriented mental health services in middle schools.

This study was conducted in Tianjin, China, from September to November 2022, and 350 middle school students from Tianjin Public Middle School were selected as subjects. A questionnaire survey was used to collect data. In this study, the Six Sigma DMAIC method, sensitivity analysis method, exploratory factor analysis and principal component analysis were used to analyze the mental health services provided to middle school students.

Based on the Six Sigma DMAIC framework, this study indicates that the contribution rate of the mental health service process factor is the largest in the post-COVID-19 era. The mental health cultivation factor ranks second in terms of its contribution. Mental health quality and policy factors are also important in the construction of middle school students’ mental health service system. In addition, the study highlights the importance of parental involvement and social support in student mental health services during the post-COVID-19 era.

To the best of the authors’ knowledge, a study on middle school students’ mental health in the post-Covid-19 era has not yet been conducted. This study developed a quality-oriented mental health system and analyzed the influencing factors of mental health for middle school students based on data analysis and the Six Sigma DMAIC method.

The utilisation of products with recycled content (PwRC) in construction projects has been identified as a targeted way to achieve sustainable management of construction and demolition waste resources. However, sustainable applications of these resources are subject to a wide array of factors that demand a thorough investigation. This study, therefore, explores the motivations, barriers and strategies for optimal PwRC uptake using a multiple-case study approach.

This study adopted an interpretive multiple-case study approach. The case studies were selected from recently completed construction projects including two infrastructure projects, one commercial project and one residential project. A series of semi-structured interviews were carried out to collect the data. For each case study, four participants were interviewed; these participants represented design, client, supply and building teams.

The study revealed the main barriers, motivations and opportunities for adoption of PwRC resources in four construction projects. These factors are believed to influence the utilisation of PwRC to varying extents and/or in diverse ways. The findings also suggest that there is a significant opportunity for stakeholders to adopt more sustainable waste management practices, and the use of institutional drivers can help achieve this goal.

The primary research contribution of the study lies in proposing three key research directions: investigating regulatory constraints impacting the use of PwRC, addressing supply chain challenges and enhancing quality assurance.

The research has a practical contribution to the industry through a suite of actionable strategies to increase the uptake of PwRC. The strategies are mostly focussed on stakeholders' education, the regulation that supports PwRC and project management planning. The two major motivations – referring to two of the three pillars of sustainability (economy and environment) – provide a basis for organisational changes to ensure achieving sustainability in construction activities.