Muhammad Bilal

• PhD, MSc, BSc
• Professor (Associate) at University of the West of England, Bristol

The ability to process large amounts of data and to extract useful insights from data has revolutionised society. This phenomenon—dubbed as Big Data—has applications for a wide assortment of industries, including the construction industry. The construction industry already deals with large volumes of heterogeneous data; which is expected to increas...

A non-modular building layout is amongst the leading sources of offcut waste, resulting from a substantial amount of onsite cutting and fitting of bricks, blocks, plasterboard, and tiles. The field of design for dimensional coordination is concerned with finding an optimal configuration for non-overlapping spaces in the layout to reduce materials w...

The progress in the field of Machine Learning (ML) has enabled the automation of tasks that were considered impossible to program until recently. These advancements today have incited firms to seek intelligent solutions as part of their enterprise software stack. Even governments across the globe are motivating firms through policies to tape into M...

A reliable benchmarking system is crucial for the contractors to evaluate the profitability performance of project tenders. Existing benchmarks are ineffective in the tender evaluation task for three reasons. Firstly, these benchmarks are mostly based on the profit margins as the only key performance indicator (KPI) while there are other KPIs fit t...

Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images. Notwithstanding...

Recent advancements in machine learning (ML) and deep learning (DL), particularly through the introduction of foundational models (FMs), have significantly enhanced surgical scene understanding within minimally invasive surgery (MIS). This paper surveys the integration of state-of-the-art ML and DL technologies, including Convolutional Neural Netwo...

Over the past few years, surgical data science has attracted substantial interest from the machine learning (ML) community. Various studies have demonstrated the efficacy of emerging ML techniques in analysing surgical data, particularly recordings of procedures, for digitising clinical and non-clinical functions like preoperative planning, context...

Assuring the safety, fairness, robustness, and trustworthiness of Machine Learning (ML) models is essential as they become increasingly integrated into healthcare technologies. However, the black-box nature of these models makes them inherently error-prone and poses significant risks to patient health if not properly vetted. Traditional software as...

In response to the success of proprietary Large Language Models (LLMs) such as OpenAI's GPT-4, there is a growing interest in developing open, non-proprietary LLMs and AI foundation models (AIFMs) for transparent use in academic, scientific, and non-commercial applications. Despite their inability to match the refined functionalities of their propr...

Over the past few years, surgical data science has attracted substantial interest from the machine learning (ML) community. Various studies have demonstrated the efficacy of emerging ML techniques in analysing surgical data, particularly recordings of procedures, for digitizing clinical and non-clinical functions like preoperative planning, context...

Traffic-related air pollution (TRAP) remains one of the main contributors to urban pollution and its impact on climate change cannot be overemphasised. Experts in developed countries strive to make optimal use of traffic and air quality data to gain valuable insights into its effect on public health. Over the years, the research community has devel...

The ability to automatically detect and track surgical instruments in endoscopic videos can enable transformational interventions. Assessing surgical performance and efficiency, identifying skilled tool use and choreography, and planning operational and logistical aspects of OR resources are just a few of the applications that could benefit. Unfort...

In recent times, surgical data science has emerged as an important research discipline in interventional healthcare. There are many potential applications for analysing endoscopic surgical videos using machine learning (ML) techniques such as surgical tool classification, action recognition, and tissue segmentation. However, the efficacy of ML algo...

There has been an increasing interest in translating artificial intelligence (AI) research into clinically-validated applications to improve the performance, capacity, and efficacy of healthcare services. Despite substantial research worldwide, very few AI-based applications have successfully made it to clinics. Key barriers to the widespread adopt...

Recent advancements in technology, particularly in machine learning (ML), deep learning (DL), and the metaverse, offer great potential for revolutionizing surgical science. The combination of artificial intelligence and extended reality (AI-XR) technologies has the potential to create a surgical metaverse, a virtual environment where surgeries can...

In the recent decade, the citation recommendation has emerged as an important research topic due to its need for the huge size of published scientific work. Among other citation recommendation techniques, the widely used content-based filtering (CBF) exploits research articles’ textual content to produce recommendations. However, CBF techniques are...

The construction of intercity highways by the government has resulted in a progressive increase in vehicle emissions and pollution from noise, dust, and vibrations despite its recognition of the air pollution menace. Efforts that have targeted roadside pollution still do not accurately monitor deadly pollutants such as nitrogen oxides and particula...

Rainfall forecasting has gained utmost research relevance in recent times due to its complexities and persistent applications such as flood forecasting and monitoring of pollutant concentration levels, among others. Existing models use complex statistical models that are often too costly, both computationally and budgetary, or are not applied to do...

The growth of the construction industry is severely limited by the myriad complex challenges it faces such as cost and time overruns, health and safety, productivity and labour shortages. Also, construction industry is one the least digitized industries in the world, which has made it difficult for it to tackle the problems it currently faces. An a...

Electrical injury impacts are substantial and massive. Investments in electricity will continue to increase, leading to construction project complexities, which undoubtedly contribute to injuries and associated effects. Machine learning (ML) algorithms are used to quantify and model causes of injuries; however, conventional ML techniques do not pro...

Breast cancer is one of the most common cause of deaths among women. Mammography is a widely used imaging modality that can be used for cancer detection in its early stages. Deep learning is widely used for the detection of cancerous masses in the images obtained via mammography. The need to improve accuracy remains constant due to the sensitive na...

The recent decade has seen increased attention focused on understanding category formation–a cognition ability of preschool aged children. Children organize their knowledge about real-world objects by categorizing them under some common properties or functions. The advancement and popularity of mobile devices with touch screens provide a good oppor...

A persistent barrier to the adoption of offsite construction is the lack of information for assessing prefabrication alternatives and the choices of suppliers. This study integrates three aspects of offsite construction, including BIM, DFMA and big data, to propose a Big data Design Options Repository (BIG-DOR). The proposed BIG-DOR system will con...

Cloud computing technologies have revolutionised several industries for several years. Although the construction industry is well placed to leverage these technologies for competitive and operational advantage, the diffusion of the technologies in the industry follows a steep curve. This study therefore highlights the current contributions and use...

In response to the gradual degradation of natural sources, there is a growing interest in adopting renewable resources for various building energy supply. In this study, a comprehensive life cycle assessment approach is proposed for a renewable multi-energy system (MES) to evaluate its primary energy consumption, economy cost and carbon emission fr...

The construction industry is known to be overwhelmed with resource planning, risk management and logistic challenges which often result in design defects, project delivery delays, cost overruns and contractual disputes. These challenges have instigated research in the application of advanced machine learning algorithms such as deep learning to help...

Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images. Notwithstanding...

Chest X-ray scan is a most often used modality by radiologists to diagnose many chest related diseases in their initial stages. The proposed system aids the radiologists in making decision about the diseases found in the scans more efficiently. Our system combines the techniques of image processing for feature enhancement and deep learning for clas...

Forecasting imminent accidents in power infrastructure projects require a robust and accurate prediction model to trigger a proactive strategy for risk mitigation. Unfortunately, getting ready-made machine learning algorithms to eliminate redundant features optimally is challenging, especially if the parameters of these algorithms are not tuned. In...

Chest X-ray scan is a most often used modality by radiologists to diagnose many chest related diseases in their initial stages. The proposed system aids the radiologists in making decision about the diseases found in the scans more efficiently. Our system combines the techniques of image processing for feature enhancement and deep learning for clas...

This study investigates project financiers' perspectives on the bankability of completion risk in private finance initiative and public–private partnership (PFI/PPP) megaprojects. Using a mixed methodology approach, focus group discussions with financier stakeholders in the UK's PFI/PPP industry were used to identify 23 criteria relevant for evalua...

Inaccurate cost estimates have significant impacts on the final cost of power transmission projects and erode profits. Methods for cost estimation have been investigated thoroughly, but they are not used widely in practice. The purpose of this study is to leverage a big data architecture, to manage the large and diverse data required for predictive...

Purpose Earlier studies on risk evaluation in private finance initiative and public private partnerships (PFI/PPP) projects have focussed more on quantitative approaches despite increasing call for contextual understanding of the bankability of risks. The purpose of this paper is to explore the perspectives of UK PFI financiers’ regarding the banka...

This study explores the current practices of Design for Deconstruction (DfD) as a strategy for achieving circular economy. Keeping in view the opportunities accruable from DfD, a review of the literature was carried out and six focus group interviews were conducted to identify key barriers to DfD practices. The results of phenomenology reveal 26 ba...

Inappropriate management of health and safety (H&S) risk in power infrastructure projects can result in occupational accidents and equipment damage. Accidents at work have detrimental effects on workers, company, and the general public. Despite the availability of H&S incident data, utilizing them to mitigate accident occurrence effectively is chal...

Cross-lingual speech emotion recognition (SER) is a crucial task for many real-world applications. The performance of SER systems is often degraded by the differences in the distributions of training and test data. These differences become more apparent when training and test data belong to different languages, which cause a significant performance...

The construction industry generates different types of data from the project inception stage to project delivery. This data comes in various forms and formats which surpass the data management, integration and analysis capabilities of existing project intelligence tools used within the industry. Several tasks in the project lifecycle bear implicati...

The construction industry is a major economic sector, but it is plagued with inefficiencies and low productivity. Robotics and automated systems have the potential to address these shortcomings; however, the level of adoption in the construction industry is very low. This paper presents an investigation into the industry-specific factors that limit...

Purpose Although the UK Guarantee Scheme for Infrastructures (UKGSI) was introduced in 2012 to address the huge financing gap for critical infrastructures, PFI sponsors have so far guaranteed only few projects. Many stakeholders in the project finance industry have blamed this situation on lack of general understanding of strategies for harnessing...

Despite the relevance of building information modelling for simulating building performance at various life cycle stages, Its use for assessing the end-of-life impacts is not a common practice. Even though the global sustainability and circular economy agendas require that buildings must have minimal impact on the environment across the entire life...

Accurate prediction of potential delays in public private partnerships (PPP) projects could provide valuable information relevant for planning and mitigating completion risk in future PPP projects. However, existing techniques for evaluating completion risk remain incapable of identifying hidden patterns in risk behavior within large samples of pro...

Purpose The purpose of this paper is to highlight the use of the big data technologies for health and safety risks analytics in the power infrastructure domain with large data sets of health and safety risks, which are usually sparse and noisy. Design/methodology/approach The study focuses on using the big data frameworks for designing a robust...

Purpose In a circular economy, the goal is to keep materials values in the economy for as long as possible. For the construction industry to support the goal of the circular economy, there is the need for materials reuse. However, there is little or no information about the amount and quality of reusable materials obtainable when buildings are dec...

Purpose A major challenge for foreign lenders in financing public private partnerships (PPP) infrastructure projects in an emerging market (EM) is the bankability of country-related risks. Despite existing studies on country risks in international project financing, perspectives of foreign lenders on bankability of country-specific risks in an EM...

Using 693 000 datacells from 33 000 sample construction firms that operated or failed between 2008 and 2017, failure prediction models were developed using artificial neural network (ANN), support vector machine, multiple discriminant analysis (MDA), and logistic regression (LR). The accuracy of the models on test data surprisingly showed ANN to ha...

The need to use Building Information Modelling (BIM) for Construction and Demolition Waste (CDW) minimisation is well documented but most of the existing CDW management tools still lack BIM functionality. This study therefore assesses the expectations of stakeholders on how BIM could be employed for CDW management. After a review of extant literatu...

This study explored use of big data analytics (BDA) to analyse data of a large number of construction firms to develop a construction business failure prediction model (CB-FPM). Careful analysis of literature revealed financial ratios as the best form of variable for this problem. Because of MapReduce’s unsuitability for iteration problems involved...

The aim of this study is to develop a BIM-based Whole-life Performance Estimator (BWPE) for appraising the salvage performance of structural components of buildings right from the design stage. A review of the extant literature was carried out to identify factors that influence salvage performance of structural components of buildings during their...

The bankruptcy prediction research domain continues to evolve with many new different predictive models developed using various tools. Yet many of the tools are used with the wrong data conditions or for the wrong situation. Using the Web of Science, Business Source Complete and Engineering Village databases, a systematic review of 49 journal artic...

Despite the consensus that waste efficient design is important for reducing waste generated by construction and demolition activities, design strategies for actual waste mitigation remain unclear. In addition, decisive roles required of designers in designing out waste remains inadequately addressed. As such, this study aims to map out attributes o...

This study discusses the future directions of effective Design for Deconstruction (DfD) using BIM-based approach to design coordination. After a review of extant literatures on existing DfD practices and tools, it became evident that none of the tools is BIM compliant and that BIM implementation has been ignored for end-of-life activities. To under...

Although construction waste occurs during the actual construction activities, there is an understanding that it is caused by activities and actions at design, materials procurement and construction stages of project delivery processes. This study investigates the material procurement and logistics measures for mitigating waste generated by construc...

Predicting and designing out construction waste in real time is complex during building waste analysis (BWA) since it involves a large number of analyses for investigating multiple waste-efficient design strategies. These analyses require highly specific data of materials that are scattered across different data sources. A repository that facilitat...

Construction industry insolvency studies have failed to stem the industry’s high insolvency tide because many focus on big civil engineering firms (CEF) when over 90% of firms in the industry are small or micro (S&M). This study thus set out to uncover insolvency criteria of S&M CEFs and the underlying factors using mixed methods. Using convenience...

As a result of increasing recognition of effective site management as the strategic approach for achieving the required performance in construction projects, this study seeks to identify the key site management practices that are requisite for construction waste minimization. A mixed methods approach, involving field study and survey research were...

The aim of this paper is to identify Critical Success Factors (CSF) needed for effective material recovery through Design for Deconstruction (DfD). The research approach employed in this paper is based on a sequential exploratory mixed method strategy. After a thorough review of literature and conducting four Focus Group Discussion (FGDs), 43 DfD f...

Performance of bankruptcy prediction models (BPM), which partly depends on the methodological approach used to develop it, has virtually stagnated over the years. The methodological positions of BPM studies were thus investigated. Systematic review was used to search and retrieve 70 journal articles and doctoral theses. Their “general methods” and...

Purpose Competency-based measure is increasingly evident as an effective approach to tailoring training and development for organisational change and development. With design stage widely reckoned as being decisive for construction waste minimization, this study aims at identifying designers’ competencies for designing out waste. Design/methodolog...

Many construction industry insolvency prediction model (CI-IPM) studies have arbitrarily employed or simply adopted from previous studies different insolvency factors, without justification, leading to poorly performing CI-IPMs. This is due to the absence of a framework for selection of relevant factors. To identify the most important insolvency fa...

This study identifies evaluation criteria with the goal of appraising the performance of existing construction waste management tools and employing the results in the development of a holistic building information modelling (BIM) framework for construction waste management. Based on the literature, this paper identifies 32 construction waste manage...

In recent times, construction industry is enduring pressure to take drastic steps to minimise waste. Waste intelligence advocates retrospective measures to manage waste after it is produced. Existing waste intelligence based waste management software are fundamentally limited and cannot facilitate stakeholders in controlling wasteful activities. Pa...

The overall aim of this study is to investigate the potential of Building Information Modelling (BIM) for construction waste minimization. We evaluated the leading BIM design software products and concluded that none of them currently support construction waste minimization. This motivates the development of a plug-in for predicting and minimizing...

Owing to its contribution of largest portion of landfill wastes and consumption of about half of mineral resources excavated from nature, construction industry has been pressed to improve its sustainability. Despite an adoption of several waste management strategies, and introduction of various legislative measures, reducing waste generated by the...

The overall aim of this study is to develop a Building Information Modelling based Deconstructability Assessment Score (BIM-DAS) for determining the extent to which a building could be deconstructed right from the design stage. To achieve this, a review of extant literature was carried out to identify critical design principles influencing effectua...

Owing to its contribution of largest portion of landfill wastes and consumption of about half of mineral resources excavated from nature, construction industry has remained a major target for achieving global sustainability. Despite an adoption of several waste management strategies, and introduction of various legislative measures, reducing waste...

While many studies are still focusing on sustainability in construction in terms of greening or resource consumption reduction, the construction industry (CI) has been struggling more with the sustainability of construction firms in terms of survival. For example, more than 1,500 construction firms went bust at the start of 2012 alone, with the ind...

The significance of public sector guarantees in stimulating PFI/PPP development has considerably increased since the recent global financial crisis in 2007-08. A Major reason for this is the drastic decline in availability of bank lending, bonds and capital markets finances for long term PFI infrastructure projects. Despite the introduction of the...

Construction industry contributes a large portion of waste to landfill, which in turns results in environmental pollution and CO2 emission. Despite the adoption of several waste management strategies, waste reduction to landfill continues seeming an insurmountable challenge. This paper explores factors impeding the effectiveness of existing waste m...

In an attempt to stem the tide of mass failure of construction businesses, or support financiers and clients in identifying healthy construction firms for loans and contracts respectively, many researchers have developed construction industry bankruptcy prediction models (CI-BPMs). The effectiveness of such CI-BPMs is partly dependent on the method...

We are at the cusp of a technological revolution driven mainly by advances in hardware technology, network architectural support, and the ability to process big data. The hardware industry, driven by Moore's law, continues to provide steadily increasing computing capability with diminishing costs. With the support of hardware advances, platforms fo...

Bankruptcy prediction models (BPMs) are needed by financiers like banks in order to check the credit worthiness of companies. A very robust model needs a very large amount of data with periodic updates (i.e. appending new data). Such size of data cannot be processed directly by the tools used in building BPMs, however Big Data Analytics offers the...

Apart from various fiscal and legislative measures, several research efforts have been made towards ensuring adequate diversion of construction waste from landfill. However, despite these joint efforts, waste landfilling continues being a popular waste management approach, suggesting ineffectiveness of the existing waste management strategies. As s...

Various physical, chemical and biological hazards that affect human health arise in the built environment. There is need for more awareness by both the designers and building occupants, so that necessary preventive measures would be incorporated in the design of new builds, and proper remedies would be applied in case of dealing with existing ones....

Various physical, chemical and biological hazards that affect human health arise in the built environment. There is need for more awareness by both the designers and building occupants, so that necessary preventive measures would be incorporated in the design of new builds, and proper remedies would be applied in case of dealing with existing ones....

Apart from various fiscal and legislative measures, several research efforts have been made towards ensuring adequate diversion of construction waste from landfill. However, despite these joint efforts, waste landfilling continues being a popular waste management approach, suggesting ineffectiveness of the existing waste management strategies. As s...

Apart from various fiscal and legislative measures, several research efforts have been made towards ensuring adequate diversion of construction waste from landfill. However, despite these joint efforts, waste landfilling continues being a popular waste management approach, suggesting ineffectiveness of the existing waste management strategies. As s...

Usage control model (UCON) is one of the emerging and comprehensive attribute based access control model that has the ability of monitoring the continuous updates in a system making it better than the other models of access control. UCON is suitable for the distributed environment of grid and cloud computing platforms however the proper formulation...

Selecting a relevant data source among the available ones in a data integration system plays vital role in optimizing query performance. The sources are heterogeneous and autonomous and can join and leave an integration system arbitrarily. Some sources may not contribute significantly to a user query because they are not relevant to it. Executing a...

In order to execute a userpsilas query in a data integration system, the query execution process needs to be optimized. Before executing a query at real time, relevant and effective data sources must be identified. In this paper we propose an ontology-driven relevance reasoning architecture for future data integration techniques that will improve t...