The quality and efficiency of road maintenance greatly impact on transportation performance. There is an urgent need to automate the process of decision-making for road maintenance, especially for reactive maintenance. However, automating this process is challenging because (1) the expert knowledge required in this process is implicit and difficult to formalize and (2) the defect reports from frontline inspectors are in unstructured human language. This paper proposes a method based on natural language processing (NLP) and knowledge graph reasoning. This method combines the semantic understanding capability of NLP with the structural reasoning capability of the knowledge graph, enabling nuanced and logically rigorous road maintenance decisions. Real-world highway defect reports are used for validation. The experimental results demonstrate a significant improvement of the proposed model over baseline models, with up to 13.5% higher accuracy on the prediction of key tasks. This approach enables a traceable decision-making process, avoiding a black-box model.

The use of graph-based asset information is growing in the Architecture, Engineering, Construction, and Operation (AECO) domain. However, there has been no comprehensive investigation into its internal structure, which hinders the efficiency of information querying and knowledge discovery. This study aims to reveal the internal structure of graph-based IFC (Industry Foundation Classes) information, referred to as IFC-Graph, and to assess the impact of graph structure on the efficiency of information querying and knowledge discovery. First, through a close examination of the IFC standard, five levels of detail were identified and proposed for IFC-Graph. Second, methods for generating graphs at each level were developed. Lastly, the impact of graph structure on information querying and knowledge discovery was assessed. The results show that: (1) relations constitute the main framework of graph-based IFC information and are key to efficiently extracting relational information from IFC-Graph; and (2) the proposed five-level framework makes graph-based asset information more practical to use, by increasing flexibility in graph generation and improving the efficiency of graph queries and knowledge discovery.

Proactive road maintenance extends asset lifespan, enhances safety and reduces downtime. However, costly reactive maintenance becomes necessary without up-to-date and well-structured data. While Geometric Digital Twins (GDT) offer digital replicas of physical structures that can be used to automate maintenance activities, no automated tools are currently available for GDT’s upkeep. This paper addresses this issue and proposes a method with a multi-step pipeline for detecting changes, matching instances, identifying newly added objects and applying these changes to the GDT 3D model using point clouds. Our methods, namely Iterative Change Refinement using a single labelled scan and Dual Instance Aware Change Retrieval using two scans, achieve a 0.89-0.97 F1 score in change detection, and our holistic pipeline results in less than 0.1° rotation angle and 0.01 m translation mean squared errors. This pipeline automates the process of GDT maintenance, making such digital twins viable and practically applicable to the industries.

Building permitting is essential for ensuring the safety, sustainability, and societal alignment of construction projects. Despite interest from both practitioners and researchers, the process remains largely manual and fragmented. Ontologies offer a promising solution by managing complexity and enabling automation through semantic information, though current ontologies in the building permit domain are limited to specific aspects like building code checking. On the process level, the OntoBPR framework integrates multiple domain-specific ontologies for a seamless digital permitting process and provides a workflow to automate the lifecycle of the permit review. Therefore, it suggests integrating the submitted building application using standardized information containers. The paper explores how digital applications can be submitted, reviewed, verified for completeness, and forwarded to authorities, and how permit review results can be gathered to support decision-making and automate notification issuance, and it provides a demonstration in a case study. In conclusion, OntoBPR formalizes a multi-layered ontology that advances and aligns the partitioned building permit process and provides an adaptable framework to harmonize diverse legal, informatics, and procedural aspects.

Road maintenance involves manually classifying a large volume of textual data necessary for downstream applications such as raising a maintenance job order. Automation can not only bring significant time and cost savings, but it can also facilitate digitalization efforts like the Road Digital Twin (DT). However, as is the case with many Architecture, Engineering and Construction (AEC) applications, annotated data availability is low, which demands exploration of specialized techniques for resource-constrained settings that have not been focused on in engineering. This work bridges this gap by proposing a data-efficient similarity-based text classifier that aims at effectively utilizing existing domain knowledge and pre-training knowledge of Large Language Models (LLMs) to enable rapid domain adaptation. It reformulates text classification as a similarity comparison task, using semantics directly as a classification criterion. Through a case study on classifying road inspection comments, the proposed classifier outperformed both traditionally fine-tuned and few-shot learning approaches. It attained an f 1 score of 0.46 with just one example per class, equivalent to the value for Sentence Transformer Fine-Tuning (SetFit) with 4 examples and Llama3 with 10. Additionally, it is able to keep up with traditional fine-tuning methods when trained with more than 300,000 total examples, achieving an a c c u r a c y of more than 95% and f 1 of around 0.9. These results indicate that the proposal is competitive against traditionally fine-tuned and few-shot models across all levels of data availability. This versatility significantly elevates the feasibility of deploying an automated text classification pipeline in a complex engineering field like road maintenance.

The CAMHighways dataset is presented, built from mobile mapping data that surveyed over 40 km of UK Highways. The dataset consists of textured meshes for road assets (including the pavement, traffic signs, and road furniture), segmented and classified point clouds, orthomosaics generated from pavement images, defect label annotations and shapefiles, and ground penetrating radar point clouds. All modalities are georeferenced and can be integrated into game engines and/or GIS software. The main aim of this work is to facilitate and automate the building of a Digital Twin (DT), a digital representation of the highway, in order to streamline inspection and maintenance through virtual reality, robotics simulation, and DT- and AI-driven data analysis. It also serves as a valuable source for other applications, such as training semantic scene understanding and defect detection algorithms. This paper introduces the dataset and outlines the data preparation process, including novel automation methods developed for this purpose, as well as integration guidelines and possible applications. • The new CAMHighways dataset is presented, spanning 42.8 km of UK highways. • Mobile mapping data is prepared for building a road DT for inspection & maintenance. • 3D meshes, point clouds, pavement orthomosaics, labels, and GPR data are included. • Several aspects of the DT generation process are automated. • The dataset is integrated into game engines and GIS software.

Building Information Models (BIMs) are becoming the official standard in the construction industry for encoding, reusing, and exchanging information about structural assets. Automatically generating such representations for existing assets stirs up the interest of various industrial, academic, and governmental parties, as it is expected to have a high economic impact. The purpose of this paper is to provide a general overview of the as-built modelling process, with focus on the geometric modelling side. Relevant works from the Computer Vision, Geometry Processing, and Civil Engineering communities are presented and compared in terms of their potential to lead to automatic as-built modelling.