This study evaluates the performance of various machine learning based regression models, including Lasso, Ridge, Elastic Net, Non-Parametric, and Linear Regression, in predicting compressive strength (CS). The models were assessed using multiple performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R²), and others. CS trends highlight the influence of curing time and the fly ash-to-waste plastic ratio. Early curing periods (3 and 7 days) show lower strength, which improves significantly over longer durations (28, 56, and 90 days) due to hydration and pozzolanic reactions. Non-Parametric model consistently outperformed the parametric models, achieving the lowest MAE (2.791 during training and 2.436 during testing) and the highest R² value (0.859 in training and 0.863 in testing), highlighting its superior predictive capability. Monotonicity analysis of the Non-Parametric model revealed that CS exhibits a strictly decreasing relationship with

This research introduces a robust machine learning framework for estimating the compressive strength of concrete, utilizing a Light Gradient Boosting Machine (LightGBM) regression algorithm. The model was developed using a diverse dataset that included different mix proportions of fly ash, silica fume, cement, fine and coarse aggregates, along with varying curing durations. After a thorough hyperparameter optimization process, the final model incorporated a learning rate of 0.1, 200 boosting iterations, an unrestricted tree depth, and 31 maximum leaf nodes. The model demonstrated strong predictive accuracy, achieving an R² value of 0.99 on the training set and 0.97 on the testing set, with corresponding Mean Absolute Errors (MAE) of 0.70 MPa and 1.35 MPa. Feature importance derived from SHAP values highlighted curing duration, silica fume percentage, and cement content as primary contributors to strength outcomes. Additional interpretation through partial dependence plots and monotonicity analysis showed that the model’s predictions aligned with expected trends in concrete behavior. Sensitivity testing indicated that changes in silica fume content and coarse aggregate proportion produced the most significant fluctuations in predicted strength.

This study investigates the effects of incorporating fly ash (FA) and silica fume (SF) into concrete and evaluates the predictive accuracy of machine learning models such as Backpropagation Neural Network (BPNN), Random Forest Regressor (RFR), and Gradient Boosting Regressor (GBR), on compressive strength. Optimal performance was achieved with 50–60

PurposeThe paper analyses the strategic management strategies of UK Construction Contracting Firms (CCFs) and their impact on the industry, highlighting their fragmentation, high-risk, low-profit nature and low profit returns. It suggests proactive strategies for sustainable growth and explores the potential of corporate diversification.Design/methodology/approachUtilising a thematic critical literature review, specific inclusion/exclusion criteria are used to select relevant literature together with a thematic qualitative synthesis data analysis approach to identify trends and challenges.FindingsUK CCFs primarily use reactive and project-based strategic management, which may not align with long-term success due to market volatility, reactive supply, fragmentation, high competition and lack of differentiation. The short-term business cycle makes medium- to long-term strategy development difficult. It is recommended that CCFs adopt proactive strategic management and consider corporate diversification for enhanced competitiveness, stability and performance. Although there are conflicting findings on the impact of diversification on firm performance, this research suggests that it is a viable strategy for achieving enhanced firm performance and competitive advantage.Research limitations/implicationsThe importance of proactive corporate strategies for CCFs is emphasised to overcome industry challenges, promote sustainable growth and enhance competitiveness. Corporate diversification, cultural transformation, management qualifications promotion and talent development collaboration are advocated, providing valuable insights for practitioners, policymakers and researchers.Practical implicationsThe practical implications of this research involve fostering a shift towards proactive and dynamic strategic management in the UK construction industry, addressing the historical neglect of broader strategic perspectives and empowering practitioners and academics to drive positive change and innovation within the sector.Social implicationsThe social implications of this research encompass the potential to enhance the overall efficiency, sustainability and collaborative dynamics within the UK construction industry, which can ultimately contribute to improved infrastructure development and the well-being of communities.Originality/valueProject-driven strategic management in the UK construction industry is explored, questioning the reactive approach used by CCFs. It provides insights, best practices and improvement areas, emphasising diversification, proactive corporate strategies, cultural shifts and industry improvement, spanning theory, practice and theory.

Purpose Traditional paper-based contracts are document-intensive, insecure, susceptible to forgery and errors, detrimental to productivity improvement and require multiple intermediaries. Addressing these challenges requires computerised construction to modernise the way modern construction projects are procured with blockchain-enabled smart contracts. Smart contracts could replace paper-based contracts by improving transparency and security and automating contractual terms, processes and transacting activities. However, smart contracts are an emerging technology with limited adoption in construction projects, and the issues influencing its widespread adoption remain unclear and unexplored. Hence, this study aims at exploring and understanding the important obstacles to adoption of smart contracts in construction projects.Design/methodology/approachUsing an international questionnaire survey, the study draws on experienced construction practitioners with direct involvement and knowledge in blockchain technology and smart contract initiatives and activities. Descriptive statistics and fuzzy logic techniques were used to analyse and model the quantitative survey data to establish the critical barriers to smart contracts adoption.Findings Organisational and external characteristics, personal characteristics and technology characteristics constitute major obstacles to the successful adoption of smart contracts. Construction practitioners’ limited knowledge of smart contracts, resistance to technology change, insufficiently digitalised construction industry and lack of or weak governmental support are critical to smart contracts adoption.Originality/value The research contributes to the body of knowledge on diffusing cutting-edge technology by advancing the understanding of practitioners’ perspectives on the primary obstacles to smart contracts adoption. Understanding the obstacles provides industry stakeholders (policymakers, leaders and practitioners) with underpinning knowledge with which to develop and implement corrective actions.

Research domain and Problem: HBIM modelling from point cloud data has become a crucial research topic in the last decade since it is potentially considered as the central data model paving the way for the digital heritage practice beyond digitization. Reality Capture technologies such as terrestrial laser scanning, drone-mounted LiDAR sensors and photogrammetry enable the reality capture with a sub-millimetre accurate point cloud file that can be used as a reference file for Heritage Building Information Modelling (HBIM). However, HBIM modelling from the point cloud data of heritage buildings is mainly manual, error-prone, and time-consuming. Furthermore, image processing techniques are insufficient for classification and segmentation of point cloud data to speed up and enhance the current workflow for HBIM modelling. Due to the challenges and bottlenecks in the scan-to-HBIM process, which is commonly criticized as complex with its bespoke requirements, semantic segmentation of point clouds is gaining popularity in the literature. Research Aim and Methodology: Therefore, this paper aims to provide a thorough critical review of Machine Learning and Deep Learning methods for point cloud segmentation, classification, and BIM geometry automation for cultural heritage case study applications. Research findings: This paper files the challenges of HBIM practice and the opportunities for semantic point cloud segmentation found across academic literature in the last decade. Beyond definitions and basic occurrence statistics, this paper discusses the success rates and implementation challenges of machine and deep learning classification methods. Research value and contribution: This paper provides a holistic review of point cloud segmentation and its potential for further development and application in the Cultural Heritage sector. The critical analysis provides insight into the current state-of-the-art methods and advises on their suitability for HBIM projects. The review has identified highly original threads of research, which hold the potential to significantly influence practice and further applied research.

This paper promotes critical realism as a suitable and fruitful philosophical foundation for the development and implementation of urban digital twins. The proliferation of a-theoretical digital twin research and practices, not declaring their philosophical positions, is threatening the scientific soundness of this new paradigm and offers little evidence for reflecting on the knowledge it produces. To address this issue, first, this paper uses focus group discussions to explore digital twin experts' perceptions of digital twin best practices for urban management and uncover the philosophical worldviews underlying these perceptions. A philosophical worldview is a general orientation about the world that is described in terms of ontological and epistemological assumptions and views on human nature. The inferred philosophical worldviews are then compared with critical realism principles, supporting the argument that critical realism provides a suitable philosophical foundation for digital twin practices in urban management envisaged by participating experts, as well as enhancing current forms of digital twin practice.

Large Language Models (LLMs) such as GPT-4.0 have shown significant promise in addressing the semantic complexities of regulatory documents, particularly in detecting inconsistencies and contradictions. This study evaluates GPT-4.0's ability to identify conflicts within regulatory requirements by analyzing a curated corpus with artificially injected ambiguities and contradictions, designed in collaboration with architects and compliance engineers. Using metrics such as precision, recall, and F1 score, the experiment demonstrates GPT-4.0's effectiveness in detecting inconsistencies, with findings validated by human experts. The results highlight the potential of LLMs to enhance regulatory compliance processes, though further testing with larger datasets and domain-specific fine-tuning is needed to maximize accuracy and practical applicability. Future work will explore automated conflict resolution and real-world implementation through pilot projects with industry partners.

The digitalisation of the regulatory compliance process has been an active area of research for several decades. However, more recently the level of activities in this area has increased considerably. In the UK, the tragic incident of Grenfell fire in 2017 has been a major catalyst for this as a result of the Hackitt report's recommendations pointing a lot of the blame on the broken regulatory regime in the country. The Hackitt report emphasises the need to overhaul the building regulations, but the approach to do so remains an open research question. Existing work in this space tends to overlook the processing of actual regulatory documents, or limits their scope to solving a relatively small subtask. This paper presents a new comprehensive platform approach to the digitalisation of the regulatory compliance processing. We present i-ReC (intelligent Regulatory Compliance), a platform approach to digitalisation of regulatory compliance that takes into consideration the enormous diversity of all the stakeholders' activities. A historical perspective on research in this area is first presented to put things in perspective which identifies the challenges in such an endeavour and identifies the gaps in state-of-the-art. After enumerating all the challenges in implementing a platform-based approach to digitalising the regulatory compliance process, the implementation of some parts of the platform is described. Our research demonstrates that the identification and extraction of all relevant requirements from the corpus of several hundred regulatory documents is a key part of the whole process which underlies the entire process from authoring to eventually compliance checking of designs. Some of the issues that need addressing in this endeavour include ambiguous language, inconsistent use of terms, contradicting requirements and handling multi-word expressions. The implementation of these tools is driven by NLP, ML and Semantic Web technologies. A semantic search engine was developed and validated against other popular and comparable engines with a corpus of 420 (out of about 800) documents used in the UK for compliance checking of building designs. In every search scenario, our search engine performed better on all objective criteria. Limitations of the approach are discussed which includes the challenges around licensing for all the documents in the corpus. Further work includes improving the performance of SPaR.txt (the tool created to identify multi-word expressions) as well as the information retrieval engine by increasing the dataset and providing the model with examples from more diverse formats of regulations. There is also a need to develop and align strategies to collect a comprehensive set of domain vocabularies to be combined in a Knowledge Graph.

Construction projects are premised upon contractual arrangements, and contracts constitute the basis of their success. A contract enables execution of work and transfer of payments, tracking of key performance indicators, and facilitation of collaboration among project stakeholders. Historically, construction projects have faced critical challenges due to poor alignment between clients’ expectations, contract terms, and contractor performance. The advent of advanced digital technologies under the concept of Industry 4.0 has the potential to benefit construction projects through application of blockchain-enabled smart contracts. However, the adoption of smart contracts in construction projects is in its early stages, and the factors that will influence its adoption remain unclear. Therefore, this study aimed to explore and establish the critical factors influencing adoption of smart contracts in construction contractual arrangements. This study administered an international questionnaire survey among experienced construction practitioners with involvement in smart contract initiatives and activities. The results obtained from descriptive statistics and fuzzy set-based analysis show that trialability, relative advantage, competitive advantage, and compatibility of smart contracts are the important predictors of the adoption of such contracts. The findings suggest that practitioners share a view that technological characteristics of blockchain-enabled smart contracts are critical to their adoption, regarding the technology’s perceived practicality in improving effectiveness and efficiency of construction projects. This study contributes to technology diffusion research in construction and highlights drivers that require practitioners’ and industry leaders’ attention to ensure successful adoption of smart contracts for cost-effective delivery of construction projects.

Digital twins have great potential for improving urban management. However, the way that they are formulated seems to vary according to the aims of the urban management taking place. For instance, digital twins are made sophisticated and innovative when the urban manager wants to demonstrate technological prowess; they contribute to generating useful interventionist strategies if social engineering is occurring; they emphasize exploratory and collaborative mechanisms if the urban manager wants to uncover people's attitudes; and they tend to focus upon citizen engagement and mechanisms for social improvements when societal reform is the main aim. Yet those who build digital twins seldom declare their worldviews or specify why they are doing so, and this leads to two problems. Firstly, it becomes difficult to evaluate and compare different digital twins. Secondly, since urban management projects often have several contrasting aims, many researchers construct seemingly pluralistic digital twins which are, in fact, severely afflicted with inconsistency and poorly measured priorities as to what needs to be included and addressed. In order to clarify the situation, this paper comprehensively analyses the research literature to conceptualize different approaches to implementing digital twins. It then assesses three alternative, theoretical paradigms upon which a pluralistic digital twin might be grounded and evaluated, and it concludes that ''critical realism'', rather than ''post-modernism'' or ''ontological flexibility' is the most appropriate.

The idea of a Digital Twin [DT] has recently diffused beyond largely technical fields, like manufacturing and aerospace, to socio-technical realms including urban management. To provide rigour and produce trustworthy findings while conducting research, one needs to consider the philosophical and theoretical underpinnings of the study. The purpose of this paper is to identify a philosophical paradigm that can underpin research and, more importantly, be of use to practitioners. The paper conceptualizes four existing distinct approaches to DT practices ( tech-driven , disruptive , cognitive, humanistic ) and argues that they resonate with the four incommensurable philosophical paradigms (functionalism, radical structuralism, interpretivism, radical humanism), respectively. It then shows how DT practitioners may demonstrate an oxymoronic form of DT practice as a result of mixing and matching parts of these different approaches rooted in incommensurable paradigms along with their justifiable pursuit of practical pluralism needed to deal with complex wicked urban problems. To offer a theoretical ground that is more useful to practitioners, enables and protects pluralism, three competing theoretical propositions (post-modernism; critical realism; ontological flexibility) are proposed. The paper argues for the robustness of critical realism. Implications of the study and potential routes for future research are put forward.

Purpose Facilitating the information exchange and interoperability between stakeholders during the life-cycle of an asset can be one of the fundamental necessities for developing an enhanced information exchange framework. Such a framework can also improve the successful accomplishment of building projects. This paper aims to use Semantic Web technologies for facilitating information exchange within existing building projects. Design/methodology/approach In real-world building projects, the construction industry’s information supply chain may initiate from near scratch when new building projects are started resulting in diverse data structures represented in unstructured data sources, like Excel spreadsheets and documents. Large-scale data generated throughout a building's life-cycle requires exchanging and processing during an asset's Operation and Maintenance (O&M) phase. Building information modelling (BIM) processes and related technologies can address some of the challenges and limitations of information exchange and interoperability within new building projects. However, the use of BIM in existing and retrofit assets has been hampered by the challenges surrounding the limitations of existing technologies. Findings The aim of this paper is twofold. Firstly, it briefly outlines the framework previously developed for generating semantically enriched 3D retrofit models. Secondly, a framework is proposed focussing on facilitating the information exchange and interoperability for existing buildings. Semantic Web technologies and standards, such as Web Ontology Language and existing AEC domain ontologies are used to enhance and improve the proposed framework. Originality/value The proposed framework is evaluated by implementing an example application and the Resource Description Framework data produced by the previously developed framework. The proposed approach makes a valuable contribution to the asset/facilities management (AM/FM) domain. It should be of interest to various FM practices for existing assets, such as the building information/knowledge management for design, construction and O&M stages of an asset’s life-cycle.

Building Information Modelling (BIM) ‘systems’ have taken the centre stage in AEC for several years now and is considered the system of choice for asset procurement in many parts of the world. By ‘systems’, we mean the set of technologies, processes, protocols, standards and policies taken together as one packaged solution. This chapter presents the concept of BIM Ecosystem which expands the ‘system’ to include the client and service organisations working in tandem for effective implementation of BIM-based asset procurement. Like natural ecosystems, the BIM ecosystem needs its components to interact and ‘feed’ each other to sustain itself and survive in the long run. Natural ecosystems have systems and services for provisioning, regulating and supporting. Besides, the historical evolution of different technologies and their impact on various business sectors is also examined to draw a parallel with evolution of BIM and its impact on the construction sector. A reflective approach by qualitative review of available relevant literature in addition to the qualitative action research-based material from author’s earlier research has been used in developing this chapter. Based on analogies with the natural ecosystem and combining it with a historical perspective on technological evolution, the services required for the BIM ecosystem to thrive and survive will be outlined. Examples from different AEC markets will be used to illustrate the consequential impacts of such an ecosystem and lessons drawn from them. A number of elements of BIM ecosystem have been identified and it is suggested that out of all these elements, government-based client organisations are the key element of this ecosystem.

The idea of a Digital Twin [DT] has been gaining increasing attention in the field of urban management. Several case studies, pilot projects and proof-of-concepts are carried out to demonstrate the value of DTs which also generates ‘DT knowledge’ about how DTs are developed. However, there appears to be no Knowledge Management strategy to guide capturing and retrieval of DT knowledge. This paper proposes a Knowledge-based System for DT Design and knowledge Transfer that facilitates capturing, sharing and reuse of DT knowledge. This can help reproduce DT best practices and thus, support thriving of DT market at a large scale.

Formation of micro-cracks due to shrinkage, lower tensile strength and higher brittleness results in reduced durability. Literature suggests that steel fibers (SF) reduce brittleness of cement concrete, and increases tensile strength. Polypropylene fibers (PF), on the other hand, is observed to provide high shrinkage crack resistance. In this study, experiments were conducted to determine change in durability by utilizing the benefits of both of these fibers. Lower fraction (1% by weight) of fibre were used for that purpose and four different combinations were used to check the durability as compared to normal concrete. The combinations of fibers used were 1% SF, 0.80% SF and 0.20% PF, 0.70% SF and 0.30% PF, 0.60% SF and 0.40% PF. Rapid chloride penetration test, water permeability test and water absorption test were used to assess the durability. Results suggest that although 1% SF provide satisfactory results. However, considering all the tests, combination of 0.80% SF and 0.20% PF yields better results in terms of durability. Thus, the above-mentioned combination of fibers is recommended for improving the durability of concrete mixes.

Outlining state-of-the-art developments in the area of complexity and design, this book collates them into a unique and authoritative resource for both the design and complex systems communities. The book is based on research which focuses on a variety of different themes and domains, including architecture, engineering, environmental design, art, fashion and management. A ground-breaking publication marking a new era of appreciation of the import of complexity on design, this book is essential reading for those studying complexity or design.