Professor Joe Tah

This paper investigates whether energy performance ratings measured by Energy Efficiency Certificates (EPCs) affect the sales prices of residential properties in Oxfordshire, UK. The study makes use of 186,913 sales transactions that took place between 1995 and 2023. Using a hedonic regression estimation, a nonlinear pattern between energy efficiency levels and the sales prices of dwellings, are observed. The results also show that there is evidence of a significant positive relationship between EPC ratings and detached, semi-detached, terraced houses and flats. In the second part of this study, we examine the effects of age, county, and type of dwelling on price premiums. To the best of the authors’ knowledge, this study is the only study, using the most comprehensive literature; and the largest, most up to date available data in analysing the Oxfordshire real-estate market, in terms of energy efficiency and price premiums.

The construction industry is facing enormous pressure to adopt digital solutions to solve the industry's inherent problems. The digital twin has emerged as a solution that can update a BIM model with real-time data to achieve cyber-physical integration, enabling real-time monitoring of assets and activities and improving decision-making. The application of digital twins in the construction industry is still in its nascent stages but has been steadily growing over the past few years. A wide variety of emerging technologies are being used in the development of digital twins in diverse applications in construction but it is not immediately clear from the literature which ones are key to the successful development of digital twins, necessitating a systematic literature review with a focus on technologies. This paper aims to identify the key technologies used in the development of digital twins in construction in the existing literature, the research gaps and the potential areas for future research. This is achieved by conducting a systematic review of studies with demonstrative case studies and experimental setups in construction. Based on the observed research gaps, prominent future research directions are suggested, focusing on technologies in data transmission, interoperability and data integration and data processing and visualisation.

International joint ventures (IJVs) are a specific type of strategic alliance between contractors from developed and developing countries and have been increasingly used. IJVs between multinational organisations are considered a successful strategy to benefit from international market opportunities in the globalised world. International construction joint ventures (ICJVs) have become of significant interest as the global construction market continues to be integrated into the more competitive business environment. The aim of this article is to uncover the knowledge transfer (KT) practices in an ICJV using social network analysis (SNA). The case presented here is the pilot study. A total of 19 questionnaire surveys were undertaken with selected team members. UCINET 6.0, an SNA package, was used to analyse the collected data and NetDraw was used to visualise the sociogram. This article first presents the actors' attributes; then, social network characteristics, which consist of network structure, network density and degree of centrality and cliques of actors, are presented. This analysis will be used to identify the key actors that influence the KT processes in this case study.

Post-conflict reconstruction has been one of the most challenging themes for the AEC industry, urban designers and planners, and related decision makers, especially in complex urban contexts with sever destruction of existing infrastructure. The city of Mosul in Iraq is a case where there is an urgent need for reconstruction, in particular the housing sector after the massive destruction caused by the ISIS war 2014-2017. The war left the city with massive destruction in the infrastructure and with around 1M displaced seeking shelters in the neighbouring cities, most of them under the poverty line. The governmental efforts along with the NGOs are continuing to plan the return of the displaced. However, these plans are limited by economic drivers and lack an active participation of the displaced in planning the post-war housing sector of Mosul city. This paper is part of a comprehensive research that discusses a methodological framework for the reconstruction of Mosul city, specifically the housing sector.  This study highlights the involvement of the displaced families in developing possible post-war housing paradigms based on their needs, requirements and desires. The main contributions include identifying the essential housing requirements, based on a sample from the displaced, as end-users. Most importantly, the study concludes with three developed housing paradigms.

Post-conflict reconstruction has been one of the most challenging themes for the AEC industry, urban designers and planners, and related decision-makers, especially in complex urban contexts with severe destruction in terms of infrastructure. The city of Mosul in Iraq is a case where there is an urgent need for reconstruction, in particular the housing sector after the enormous destruction caused by the ISIS war of 2014–2017. Today, advanced technologies in construction present opportunities to address post-conflict reconstruction challenges. BIM has been used in recent years since it is an integrated and effective process for planning, monitoring and managing contemporary construction projects. Nevertheless, BIM has not been investigated properly in planning and managing post-conflict reconstruction, especially in developing countries. This paper discusses the potential of adopting BIM in post-conflict reconstruction through investigating the validity of the BIM process in planning and assessing possible housing solutions for the reconstruction of Mosul city, using BIM applications. The main findings suggest that BIM applications present significant potential in the process of planning, assessing and managing the reconstruction of post-conflict contexts in developing countries, where conventional methods are limited, dysfunctional and inefficient.

The urgent need to improve performance in the construction industry has led to the adoption of many innovative technologies. 3D laser scanners are amongst the leading technologies being used to capture and process assets or construction project data for use in various applications. Due to its nascent nature, many questions are still unanswered about 3D laser scanning, which in turn contribute to the slow adaptation of the technology. Some of these include the role of 3D laser scanners in capturing and processing raw construction project data. How accurate are the 3D laser scanner or point cloud data? How does laser scanning fit with other wider emerging technologies such as building information modeling (BIM)? This study adopts a proof-of-concept approach, which in addition to answering the aforementioned questions, illustrates the application of the technology in practice. The study finds that the quality of the data, commonly referred to as point cloud data, is still a major issue as it depends on the distance between the target object and 3D laser scanner’s station. Additionally, the quality of the data is still very dependent on data file sizes and the computational power of the processing machine. Lastly, the connection between laser scanning and BIM approaches is still weak as what can be done with a point cloud data model in a BIM environment is still very limited. The aforementioned findings reinforce existing views on the use of 3D laser scanners in capturing and processing construction project data

Condition assessment and health monitoring (CAHM) of built assets, requires effective and continuous monitoring of any changes to the material and/or geometric properties of the assets in order to detect any early signs of defects or damage and act on time. Most of the traditional CAHM techniques, however, depend on manual labour despite that, in some cases, the inspection environment can be unsafe and could lead to low efficiency or miss-judgement of the severity of the defect. In recent years, computer vision techniques have been proposed as an automated alternative to the traditional CAHM techniques as methods for extracting and analysing feature-related information from asset images and videos. Such methods have proven to be robust and effective solutions, complementary to current time-consuming and unreliable manual observational practices. This work is concerned with the development of a deep learning-based smartphone App which allows real-time detection of four types of defects in buildings namely; cracks, mould, stain, and paint deterioration. Since smartphones are widely available and equipped with high-resolution cameras, this application can offer a practical, low-cost solution for condition assessment procedures of built assets. The obtained results are promising and support the feasibility and effectiveness of the approach to identify and classify various types of building defects.

In the field of supervised machine learning, the quality of a classifier model is directly correlated with the quality of the data that is used to train the model. The presence of unwanted outliers in the data could significantly reduce the accuracy of a model or even worse, result in a biased model leading to an inaccurate classification. Identifying the presence of outliers and eliminating them is, therefore, crucial for building good quality training datasets. Pre-processing procedures for dealing with missing and outlier data, commonly known as feature engineering, are standard practice in machine learning problems. They help to make better assumptions about the data and also prepare datasets in a way that best expose the underlying problem to the machine learning algorithms. In this work, we propose a multistage method for detecting and removing outliers in high-dimensional data. Our proposed method is based on utilising a technique called t-distributed stochastic neighbour embedding (t-SNE) to reduce high-dimensional map of features into a lower, 2-dimensional, probability density distribution and then use a simple descriptive statistical method called interquartile range (IQR) to identifying any outlier values from the density distribution of the features. t-SNE It is a machine learning algorithm and a nonlinear dimensionality reduction technique well-suited for embedding high-dimensional data for visualisation in a low-dimensional space of two or three dimensions. We applied this method on a dataset containing images for training a convolutional neural network model (ConvNet) for an image classification problem. The dataset contains four different classes of images: three classes contains defects in construction (mould, stain, and paint deterioration) and a no-defect class (normal). We used the transfer learning technique to modify a pre-trained VGG-16 model. We used this model as a feature extractor and as a benchmark to evaluate our method. We have shown that when using this method, we can identify and remove the outlier images in the dataset.  After removing the outlier images from the dataset and re-training the VGG-16 model, the results have also shown that the accuracy of the classification has significantly improved and the number of misclassified cases has also dropped. While many feature engineering techniques for handling missing and outlier data are common in predictive machine learning problems involving numerical or categorical data, there is little work on developing techniques for handling outliers in high-dimensional data which can be used to improve the quality of machine learning problems involving images such as ConvNet models for image classification and object detection problems.

Clients are increasingly looking for fast and effective means to quickly and frequently survey and communicate the condition of their buildings so that essential repairs and maintenance work can be done in a proactive and timely manner before it becomes too dangerous and expensive. Traditional methods for this type of work commonly comprise of engaging building surveyors to undertake a condition assessment which involves a lengthy site inspection to produce a systematic recording of the physical condition of the building elements, including cost estimates of immediate and projected long-term costs of renewal, repair and maintenance of the building. Current asset condition assessment procedures are extensively time consuming, laborious, and expensive and pose health and safety threats to surveyors, particularly at height and roof levels which are difficult to access. This paper aims at evaluating the application of convolutional neural networks (CNN) towards an automated detection and localisation of key building defects, e.g., mould, deterioration, and stain, from images. The proposed model is based on pre-trained CNN classifier of VGG-16 (later compaired with ResNet-50, and Inception models), with class activation mapping (CAM) for object localisation. The challenges and limitations of the model in real-life applications have been identified. The proposed model has proven to be robust and able to accurately detect and localise building defects. The approach is being developed with the potential to scale-up and further advance to support automated detection of defects and deterioration of buildings in real-time using mobile devices and drones.

In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (Cc) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is costly, time-consuming, and requires skilled technicians. To overcome these drawbacks, we aimed to predict Cc through other soil parameters, i.e., the liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Using these parameters is more convenient and requires substantially less time and cost compared to the conventional tests to estimate Cc. This study presents a novel prediction model for the Cc of fine-grained soils using gene expression programming (GEP). A database consisting of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of the developed GEP-based model was evaluated through the coefficient of determination (R2), the root mean squared error (RMSE), and the mean average error (MAE). The proposed model performed better in terms of R2, RMSE, and MAE compared to the other models.

The combination of machine learning and numerical methods has recently become popular in the prediction of macroscopic and microscopic hydrodynamics parameters of bubble column reactors. Such numerical combination can develop a smart multiphase bubble column reactor with the ability of low-cost computational time when considering the big data. However, the accuracy of such models should be improved by optimizing the data parameters. This paper uses an adaptivenetwork-based fuzzy inference system (ANFIS) to train four big data inputs with a novel integration of computational fluid dynamics (CFD) model of gas. The results show that the increasing number of input variables improves the intelligence of the ANFIS method up to R = 0.99, and the number of rules during the learning process has a significant effect on the accuracy of this type of modeling. Furthermore, the proper selection of model’s parameters results in higher accuracy in the prediction of the flow characteristics in the column structure.

Purpose

Knowledge transfer (KT) practices in five construction projects delivering sustainable office buildings in Germany and the UK are examined using Social Network Analysis (SNA).

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Design/methodology/approach

Case studies were adopted as research strategy, with one construction project representing one case study. A combination of quantitative data, social network data and some qualitative data on perceptions of the sustainable construction process and its KT were collected through questionnaires. The data was analysed using a combination of descriptive statistics, cross tabulations, content analysis and SNA. This resulted in a KT map of each sustainable construction project.

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Findings

The findings resulted in a better understanding of how knowledge on sustainable construction is transferred and adopted. They show that large amounts of tacit knowledge were transferred through strong ties in sparse networks.

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Research limitations/implications

The findings could offer a solution to secure a certain standard of sustainable building quality through improved KT. The findings indicate a need for further research and discussion on network density, tie strength and tacit KT.

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Originality/value

This paper contributes to the literature on KT from a social network perspective. It provides a novel approach through combining concepts of network structure and relatedness in tie contents regarding specialised knowledge, i.e. sustainable construction knowledge. Thereby it provides a robust approach to mapping knowledge flows in office building projects that aim to achieve high levels of sustainability standards.

Construction cost estimation is one of the most important aspects of construction project design. For generations, the process of cost estimating has been manual, time-consuming and error-prone. This has partly led to most cost estimates to be unclear

and riddled with inaccuracies that at times lead to over- or underestimation

of construction cost. The development of standard set of measurement rules that are understandable by all those involved in a construction project, have not totally solved the challenges. Emerging Building Information Modelling (BIM) technologies can exploit standard measurement methods to automate cost estimation process and improve accuracies. This requires standard measurement methods to be structured in ontological and machine readable format; so that BIM software packages can easily read them. Most standard measurement methods are still text-based in textbooks and require manual editing into tables or Spreadsheet during cost estimation. The aim of this study is to explore the development of an ontology based on New Rules of Measurement (NRM) commonly used in the UK for cost estimation. The methodology adopted is Methontology, one of the most widely used ontology engineering methodologies. The challenges in this exploratory study are also reported and recommendations for future studies proposed. 

BIM has recently gained ground in developed countries. However, the use of BIM in developing countries including Cameroon is not well-known. In this study, BIM implementation in Cameroon is explored. The research methods used are a pilot study, electronic email surveys and in-depth phone interviews. Altogether, 179 professionals having at least a Master’s Degree in Civil Engineering from the National Advanced School of Engineering Yaoundé I - Cameroon, a leading engineering institution in Francophone Africa, were sampled. Forty six provided feedback yielding a response rate of 25.7%. It emerged that some BIM software are already being used in Cameroon. However, major barriers hindering BIM uptake are high license fee and lack of huge projects that can pay off the cost of investment in BIM. Perhaps, partly because the respondents were highly skilled, it emerged that the lack of expertise was/is not a major problem to use BIM in projects. Although this study is limited to Cameroon, many recommendations could be relevant to other African countries.

The construction industry including its support industries is one of the highest consumers of natural resources. In the act of consumption of natural resources during construction processes, embodied energy and greenhouse gases are emitted which have adverse effects on the natural environment. Thus, recent studies have revealed a significant interest in the quantification of embodied energy and greenhouse gases in construction processes. Unfortunately, current interpretations and quantification procedures of embodied energy and greenhouse gases are quite unclear. More also, while greenhouse gas and embodied energy quantification models are so disaggregated, studies reveal their existence in isolation without any links to other important environmental/construction management variables such as waste, time and cost. The objectives of this study are to identify the gaps in the current computation models, to reveal the relationships between the identified models and to propose a framework towards developing an integrated model for measuring embodied energy, greenhouse gases, construction waste, time and cost. The contributions of this study are three-fold. Firstly, the identification of the different models and variables, such that they can be used in computations, that can lead to consistent and comparable results. Secondly, investigate the relationships amongst embodied energy, greenhouse gases, construction waste, cost and time variables, that can facilitate the quantification process and hence potentially facilitate the engagement into low carbon building design by construction professionals. Lastly, lay the foundation for further research especially with regards to the integration of the different models and variables so that they can be measured simultaneously.

The built environment sector impacts significantly on communities. At the same time, it is the sector with the highest cost and environmental saving potentials provided effective strategies are implemented. The emerging Semantic Web promises new opportunities for efficient management of information and knowledge about various domains. While other domains, particularly bioinformatics have fully embraced the Semantic Web, knowledge about how the same has been applied to the built environment is sketchy. This study investigates the development and trend of Semantic Web applications in the built environment Understanding the different applications of the Semantic Web is essential for evaluation, improvement and opening of new research. A review of over 120 refereed articles on built environment Semantic Web applications has been conducted. A classification of the different Semantic Web applications in relation to their year of application is presented to highlight the trend. Two major findings have emerged. Firstly, despite limited research about easy-to-use applications, progress is being made from often too-common ontological concepts to more innovative concepts such as Linked Data. Secondly, a shift from traditional construction applications to Semantic Web sustainable construction applications is gradually emerging. To conclude, research challenges, potential future development and research directions have been discussed.