Editorial

Digitisation in civil engineering is no longer confined to drafting boards replaced by CAD or paper records converted to PDFs; it is reshaping how we conceive, deliver and operate the systems that support modern life. Over the past three decades, the industry has progressed from isolated design tools to connected information management, with building information modelling (BIM), geospatial platforms and common data environments helping teams coordinate across disciplines and organisations. Yet the pace of change is accelerating. Data volumes are rising, asset networks are becoming more interdependent and expectations around safety, resilience and decarbonisation are intensifying – often in parallel with skills shortages and constrained delivery windows.

Looking back at the wider civil engineering industry, many digital initiatives were adopted to improve efficiency and reduce rework. Looking forward, their importance is increasingly becoming societal. Infrastructure must be planned and maintained amid climate volatility, rapid urbanisation and ageing assets, while remaining affordable and inclusive. Digital methods – ranging from computational design to decision support assisted by artificial intelligence (AI) and non-contact monitoring – offer the possibility of making evidence travel faster than risk and of turning lessons from one project into dependable practice for the next. However, progress will depend not only on technology but on governance, assurance and trust – the ability to explain decisions, protect data and embed new workflows in everyday delivery. When digitisation is aligned with these fundamentals, it can help the sector deliver safer, cleaner and more resilient outcomes – ultimately improving the lives of the people who rely on civil engineering every day.

The editorial panel are extremely pleased and happy to introduce the eight papers in this special edition. The diverse spectrum of these papers covering digitisation in civil engineering is a true representation of the future and the art of possible in civil engineering. The papers are sequenced to give the reader a sense of the depth and breadth of the civil engineering profession and how the industry is shaping up towards an interesting future where digital innovations led by human experience brings value propositions to both clients and end users – us.

Maheshwari (2026) explores the current landscape, key applications, emerging trends and challenges associated with AI and ML technologies across structural, geotechnical, transportation and environmental engineering domains. While acknowledging rapid technological advancements, the author critically addresses persistent methodological vulnerabilities in the current literature, including an over-reliance on small, location-specific datasets, inconsistent benchmarking protocols and the frequent omission of uncertainty quantification. To overcome the limitations of ‘black-box’ algorithms and ensure deployment in safety-critical applications, the paper strongly advocates for a shift towards explainable AI and hybrid physics-informed neural networks that blend data-driven learning with established physical constraints. The study concludes that AI-driven solutions should transition from experimental tools to reliable, deployable technologies for resilient and sustainable infrastructure development.

Ghazy et al. (2026) highlights the ongoing transformation in civil engineering from fragmented and analogue practices towards digitally integrated, data-driven infrastructure management systems. The study demonstrates how non-destructive testing (NDT) data, including ground-penetrating radar, ultrasonic tomography, LiDAR scans and visual inspections, can be integrated into a unified three-dimensional digital environment for bridge asset management. This approach improves defect visualisation, inspection validation and long-term infrastructure resilience, while supporting predictive maintenance and future digital twin applications.

A key strength of the paper lies in its practical implementation framework. Rather than discussing digital twins conceptually, the authors present standardised workflows for harmonising diverse datasets within Autodesk Revit using coordinated metadata and interoperable digital environments. The paper also addresses industry challenges such as proprietary data formats, interoperability constraints and the reliance on specialist software expertise.

The importance of integrated geotechnical and digital investigation techniques strongly resonates with the professional experience of one of the authors of this editorial. During ground investigations in Doha, Qatar in 2006 for proposed high-rise developments, multiple subsurface cavities and voids were identified through a combination of borehole drilling, standard penetration tests, core recovery, seismic refraction surveys, ground-penetrating radar, plate load tests, permeability tests and trial pits. Early identification of these hidden caverns enabled timely mitigation through pressure grouting and cavity treatment, allowing piling works to proceed safely and without delays. Such experiences reinforce the importance of integrated investigation methodologies and digitally coordinated datasets in reducing construction risks and improving project delivery.

Cerek et al. (2026) investigate the deployment of hybrid surrogate modelling techniques to support the lifecycle management and real-time structural assessment of anchored quay walls. The authors compared a baseline feedforward neural network (FNN) using static input features against a hybrid architecture (BiLSTM-FNN) that processes sequential horizontal displacement data alongside static parameters. The study demonstrated that the hybrid BiLSTM–FNN model offers vastly superior predictive accuracy for maximum bending moments, achieving an R² of 0.999 compared with 0.885 for the baseline model. The paper also shows that, while the hybrid approach demands higher computational costs during training, it provides a highly efficient tool for estimating internal structural forces, establishing a critical pathway for the integration of digital twins as an essential tool for infrastructure management.

Sundaram and John Varghese (2026) demonstrate how algorithmic thinking and agile delivery can translate digitisation into measurable programme outcomes on linear infrastructure. Through the Middle Level System River Management Scheme, parametric models linked to geospatial data and visual programming automate repeatable assessments, accelerate option development and provide earlier cost and stakeholder narratives across 178 km. Beyond speed, the approach strengthens assurance by making assumptions explicit and changes traceable as requirements evolve. Its broader implication is clear: when engineering intent is encoded into reusable workflows and governed through iterative checkpoints, teams can reduce risk and improve decision quality under uncertainty.

Winkler et al. (2026) focus on how better evidence can unlock safer and more efficient rules in operational rail environments. By applying DIC to measure OLE uplift remotely, it shows a credible pathway away from sensor-heavy monitoring towards non-contact methods that reduce disruption, exposure to hazards and embedded carbon dioxide. For designers, such datasets can support more proportionate clearance requirements – potentially avoiding unnecessary interventions – provided that calibration, uncertainty and data governance satisfy assurance expectations. The message resonates across the sector: smarter measurement, coupled with transparent decision pathways, is essential if digital innovation is to translate into reliable, socially beneficial outcomes.

Vasudeva and Tracey (2026) examine the governance and ethical dimensions of digital transformation within the construction industry. Focusing on blockchain-enabled smart contracts and their interaction with the JCT 2024 suite, the paper critically evaluates the balance between automation and professional judgement. While smart contracts can improve transparency, auditability, payment efficiency and regulatory compliance, the authors also highlight concerns related to rigidity, reduced contextual judgement, digital exclusion and legal uncertainty.

Importantly, the paper positions smart contracts not merely as technological tools, but as socio-legal mechanisms capable of reshaping contractual relationships, governance structures and professional accountability. The proposed hybrid model – where smart contracts complement rather than replace traditional contractual frameworks – provides a balanced and pragmatic pathway for digital adoption.

Goldsmith and Rees (2026) convey that today’s CDE must evolve from a shared filing system into an intelligent coordination layer for the supply chain. As standards and tooling mature, it is the sheer growth in data, formats and contractual interfaces that now constrains the adoption of information-intensive innovations such as robotics and agentic AI. The proposed neuro-symbolic approach – combining AI-driven knowledge extraction with explicit semantic structures – seeks to reconcile scale with assurance, enabling context-aware retrieval and exchange that remains traceable and security conscious. If proven in practice, this shift could reduce manual governance effort while making inter-organisational collaboration more responsive and auditable.

All the papers in this special issue demonstrate that the future of civil engineering digitalisation extends beyond technological advancement alone. The papers show that digitalisation in civil engineering is no longer simply a matter of technical innovation, but a central component of contemporary practice aided with the insights of future possibilities. The collective highlight of these papers is that progress will depend not only on advances in data, automation and computational methods, but also on sound governance, transparency and professional judgement. Meaningful civil engineering transformation will require integrated digital ecosystems alongside human-centred decision making throughout the infrastructure lifecycle. It is hoped that this issue will encourage further research and informed application in the civil engineering profession, supporting a more resilient, responsible and adaptive future for the profession.