Educational building conditions and the health of users

In order to maintain a healthy learning environment, diagnosis and management of defects in the educational facility are paramount. The preliminary results of the ongoing research reported here seek to identify defects associated with educational buildings and their effects on the health of polytechnic students and staff in Nigeria. A questionnaire survey, including 34 defects based on a post-occupancy evaluation (POE) was used to establish relationships with the health of polytechnic students and staff. Two hundred (200) respondents were randomly selected based on their schools (faculty) within Lagos State Polytechnic. Descriptive and inferential statistics were used for analysis of the collected data. The results of the study indicate that defects such as plumbing and dampness problems, cobwebs and dust, are prominent in the institution. Also the relationship between building conditions (defects) and health problems was established, with the predictors of the health problems. Based on the findings, it is important for designers and managers of facilities within tertiary institutions to develop and implement design and maintenance policies targeted at minimizing the likelihood of plumbing, dampness, electrical, cobweb and dust problems in educational buildings due to the health risks induced by the defects. It is evident that effective maintenance schedules and policies should be put in place to ensure that facilities are not left to decay before replacement

Property Rental Value Classification Model: A Case of Osogbo, Osun State, Nigeria

Residential property rental value forecasting has an impact on property investment decision. This necessitates the need for a study to forecast residential property rental value considering all associated variables including presence of cultural sites in the study area. Data for the study were gathered from the record of recent lettings in the study area. For the purpose of precision, this study adopted three artificial intelligence models. These are artificial neural network, logistic regression and support vector machine as models of classifying the rental value of residential property in Osogbo. The study considered relevant input variables among which are distance to cultural site, age of building, state of exterior/interior of building. Findings from the study revealed that the three adopted forecasting models had over 80% of the forecasted properties correctly classified thus making the residential property rental forecasting very reliable. Also, it was established that, in the study area, distance from cultural site is the property attribute with the highest negative impact on rental value

Apprenticeship for craftspeople in the construction industry: a state-of-the-art review

Purpose: Apprenticeship programmes are designed to provide young trainees with essential broad-based skills. Through apprenticeships, different sectors that are underpopulated can fill up their skills gaps. Apprenticeships are particularly useful to the construction sector which has a high ageing workforce and associated lower labour productivity. However, the completion rates of apprenticeship training programmes in the construction sector remain low in several countries across the globe. Thus, the purpose of this paper is to review the published research on apprenticeship training that is specifically focused on the construction sector, to determine the current status quo and suggest a direction for future research. Design/methodology/approach: A systematic review approach was adopted. Based on a comprehensive search using SCOPUS databases, 33 relevant journal articles were identified and analysed. Findings: It was found that monitoring and control is the most mentioned factor responsible for improvements in the completion rates of apprenticeship training. In contrast, the length of time required for going through the full training is the most common factor responsible for low completion rates. Three research gaps were identified, among which is the dearth of studies that has focused on apprentices training in developing countries. Research limitations/implications: The gaps identified in the current knowledge on apprenticeship training would serve as a justification for future investigations. However, the scope of the review is limited to papers published in academic journals and citable through SCOPUS. Practical implications: The outcomes of the study provide researchers and other relevant stakeholders with a concise report on the findings of previous studies. It also provides insight into strategies for improving the completion rates of apprenticeship training in the construction sector. Originality/value: A systematic evaluation of the extant literature draws on theoretical evidence and highlights the factors that are more likely to influence the outcomes of apprentice training for craftspeople in the construction sector

Strategies for improving construction craftspeople apprenticeship training programme: Evidence from the UK

This study seeks to address two research questions: (i) what are the factors responsible for the non-completion of the craftspeople apprentice training programmes? and (ii) how can the non-completion of construction craftspeople apprenticeship programmes be improved? Eighteen semi-structured interviews were conducted with relevant stakeholders involved apprenticeship in the UK. The finding reveals that multiplicity of factors contribute to the non-completion of craftspeople apprenticeship. The study reveals that ’underestimation of the apprentice programme’, ’poor career guidance’ and inappropriate placement’ have not been previously mentioned in literature as factors responsible for the non-completion. Also, it was found that ‘improvements in recruitment methods’ and ‘improvements in learner's experiences during apprenticeship’ are vital strategies for improving completion rates of craftspeople apprenticeships. By understanding these strategies, the construction sector would have a better chance of attracting and training a young workforce to meet its future needs. Young workforce is vital for improving productivity and organisational sustainability in the construction sector. The research contributes to the current body of knowledge by providing an in-depth understanding of the challenges faced in craftspeople apprenticeship training and how to improve its outcome

A Bibliometric Analysis of Recycled Concrete Research (1978–2019)

ArticlePurpose – The use of recycled concrete (RC) can reduce the greenhouse emissions associated with the production of cement, which is one of the primary materials used for the execution of construction projects. This research aims to review the state of knowledge in the field of RC research. An understanding of the state of the art in the RC domain justifies future research in this field. Design/methodology/approach – A systematic and comprehensive search of RC-related literature was conducted using the Scopus database. In this research, the bibliometrix R-package was used for the analysis of bibliometric information of the selected papers. The software was used to create a map, which highlights the trends and gaps in the RC knowledge domain. Findings – The results reveal the research themes, clusters, collaboration networks and advancement of knowledge in the field of RC research. The study integrates the literature focussed on RC research and provides a platform for progression of knowledge in this field. Originality/value – The study contributes to the growing body of knowledge by providing an up-to-date RC knowledge map based on an analysis of bibliographic data. Information gleaned from previous studies suggests that bibliometric review can strengthen and complement the findings emerging from other forms of literature reviews. The study reported here is one of the first studies to provide insights into the evolution of RC research

The Exposure of Workers at a Busy Road Node to PM2.5: Occupational Risk Characterisation and Mitigation Measures

The link between air pollution and health burden in urban areas has been well researched. This has led to a plethora of effective policy-induced monitoring and interventions in the global south. However, the implication of pollutant species like PM2.5 in low middle income countries (LMIC) still remains a concern. By adopting a positivist philosophy and deductive reasoning, this research addresses the question, to what extent can we deliver effective interventions to improve air quality at a building structure located at a busy road node in a LMIC? This study assessed the temporal variability of pollutants around the university environment to provide a novel comparative evaluation of occupational shift patterns and the use of facemasks as risk control interventions. The findings indicate that the concentration of PM2.5, which can be as high as 300% compared to the WHO reference, was exacerbated by episodic events. With a notable decay period of approximately one-week, adequate protection and/or avoidance of hotspots are required for at-risk individuals within a busy road node. The use of masks with 80% efficiency provides sufficient mitigation against exposure risks to elevated PM2.5 concentrations without occupational shift, and 50% efficiency with at least ‘2 h ON, 2 h OFF’ occupational shift scenario

Factors supporting the implementation of buildability assessment as a tool for buildability improvement

Purpose- Improving buildability of building designs with the aid of buildability assessment is essential because of the effect of designs on construction. Despite the plethora of research into buildability reported over the years, a review of the literature shown a dearth of research into the factors supporting the implementation of buildability assessment. Because buildability assessment has been confirmed to be highly beneficial to construction business, this study aims to investigate the factors supporting the implementation of buildability assessment as a tool for buildability improvement using Nigeria as a representative case. Design/methodology/approach- Survey research method was adopted for the study. Questionnaires were administered to a purposively selected group of architects, engineers, builders and quantity surveyors involved in construction project delivery within client, consulting and contracting organisations in Nigeria. A total of 368 questionnaires were distributed among the sampled participants, out of which, a total of 219 representing 60% were sufficiently filled and returned. Data collected were analysed using inferential and descriptive statistics. Findings- The results revealed owner’s commitment, clients’ awareness of the benefits of conducting buildability assessment on building design, unity amongst different professionals in the construction industry, designers consider buildability important, adequate coordination amongst different design disciplines, adequate channel for co-ordination and communication between designers and constructors at the design stage and adequate support from the government as the top most seven factors supporting buildability assessment implementation in construction sector of Nigeria. Secondly, the results from the research revealed that there is no statistically significant difference in factors supporting buildability assessment implementation in construction between clients and consulting and contracting construction organisations in Nigeria. Originality/value- The findings provide in-depth insight of the factors supporting the implementation of buildability assessment in construction that can help principal stakeholders in construction to facilitate development of strategies required in supporting the adoption and implementation of buildability assessment tool for buildability improvement

Forecasting construction output: a comparison of artificial neural network and Box-Jenkins model

Purpose: Fluctuations in construction output has an adverse effect on the construction industry and the economy due to its strong linkage. Developing reliable and accurate predictive models is vital to implementing effective response strategies to mitigate the impact of such fluctuations. The purpose of this paper is to compare the accuracy of two univariate forecast models, i.e. Box-Jenkins (autoregressive integrated moving average (ARIMA)) and Neural Network Autoregressive (NNAR). Design/methodology/approach: Four quarterly time-series data on the construction output of Hong Kong were collected (1983Q1-2014Q4). The collected data were divided into two parts. The first part was fitted to the model, while the other was used to evaluate the predictive accuracy of the developed models. Findings: The NNAR model can provide reliable and accurate forecast of total, private and “others” construction output for the medium term. In addition, the NNAR model outperforms the ARIMA model, in terms of accuracy. Research limitations/implications: The applicability of the NNAR model to the construction industry of other countries could be further explored. The main limitation of artificial intelligence models is the lack of explanatory capability. Practical implications: The NNAR model could be used as a tool for accurately predicting future patterns in construction output. This is vital for the sustained growth of the construction industry and the economy. Originality/value: This is the first study to apply the NNAR model to construction output forecasting research

The capital budgeting evaluation practices (2014) of contractors in the Hong Kong construction industry

Capital budget evaluation plays a crucial role in financial management. This places a firm in a competitive position. Recent development points to the need for implementing capital budgeting in construction organizations due to the capital-intensive nature of construction business. The aim here is to investigate the trends in the practice of capital budget evaluation among construction contractors operating in Hong Kong over a 20-year period. A longitudinal survey approach is used; four cross-sectional surveys were conducted between 1994 and 2014. The findings indicate that ‘formal financial evaluation’ is the most common technique used for capital budget evaluation. In addition, the practice of capital budget evaluation is more pronounced in the large-sized firms. A comparative analysis of the results of the four surveys reveals that there are variations in the degree and frequency of use of capital budget evaluation techniques over the study period. Further research is needed to understand the challenges associated with the use of sophisticated capital budget evaluation techniques in the construction industry of Hong Kong

Using Univariate Models for Construction Output Forecasting: Comparing Artificial Intelligence and Econometric Techniques

Forecasting of the volume of construction works plays an important role in ensuring that stakeholders (contractors, consultants, government, etc.) formulate policies for strategic long-term planning in the construction sector. In construction economics, research efforts targeted at construction output forecasting have been limited due to the lack of availability of quantitative data. To address this problem, the current study explores the use of univariate modeling techniques in construction output forecasting. Three univariate modeling techniques [namely, Box-Jenkins, neural network autoregression (NNAR), and support vector machine (SVM)] were used to predict output of various construction sectors. In the literature, the Box-Jenkins model is considered a benchmark univariate method due to its simplicity, sound theoretical basis, and predictive performance. Out-of-sample forecasting was used to evaluate the predictive accuracy of the developed models. The results of the study revealed that the SVM model can reliably and accurately predict residential, maintenance, and total construction output in the medium term. The univariate models reported here can be implemented using historical data on construction output in other countries; this is particularly useful in cases where data are inadequate for multivariate models. The developed SVM model can serve as a tool for estimating future trends in construction output