The selection of construction methods is a critical factor in linear infrastructure projects, yet current practice remains largely experience-driven. As is evident from the extant literature, multi-criteria decision-making, rule-based and algorithmic approaches are applied to support method selection. The present systematic literature review analyses these approaches with respect to input parameters, output structures and validation strategies. The findings indicate a predominance of project-level ranking models and limited spatial resolution. Key deficiencies identified include inadequate scalability, deficient integration of costs and restricted applicability to corridor-scale decision-making. The review identifies these gaps as central challenges for future data-driven decision-support systems.
Asset maintenance and infrastructure resilience are directly related. However, most resilience assessments overlook pre-disaster asset condition. This study develops a probabilistic lifecycle framework integrating stochastic condition-index (CI) evolution, performance degradation, recovery modeling, and explicit maintenance strategies within a multi-hazard simulation environment. The framework quantifies how pre-event maintenance shapes resilience trajectories. Application to a representative bridge portfolio demonstrates that preventive maintenance consistently improves lifecycle performance and the Composite Resilience Performance Index (CRPI), while rehabilitation provides substantial but time-dependent recovery gains. The proposed framework offers a quantitative decision-support tool linking maintenance actions to resilience outcomes, supporting resilience-informed infrastructure planning and management.
Investigations into compliance checking have paid little attention to how process-oriented regulations can be made accessible and operable. This paper explores ways that these expectations can be met. The study considers the macro level requirements enforced by a government agency, as exemplified by the UK Construction (Design and Management) Regulations (CDM 2015) which assigns responsibilities and duties clients, designers, and contractors, many of whom lack confidence in fulfilling these obligations or in evidencing their discharge. The findings highlight an opportunity for guidance that is firmly grounded in regulatory requirements yet tailored to the distinct roles of industry participants.
Examining contextual factors shaping the success of 5D BIM implementation in Europe, results reveal that while 91.5% utilize the technology for QTO and deep adoption remains limited, satisfaction increases significantly when a critical value threshold is exceeded, moving from single-use tasks to integrated workflows. Contractors are less satisfied by 5D BIM due to commercial risk, while medium-sized organizations and moderately complex projects exhibit a transition trap, resulting in lower satisfaction. Conversely, high national BIM maturity and Integrated Project Delivery amplify success. 5D BIM provides optimal value only when implemented as a comprehensive system, not merely as a standalone tool.
Earthwork operations are among the most hazardous construction activities due to frequent interactions between heavy machinery and workers in dynamic, constrained environments. Traditional safety practices remain largely reactive and insufficient for real-time accident prevention. This paper presents an IoT-driven real-time risk analysis framework for proactive safety management in earthwork operations. The system integrates machine-mounted telematics, spatial analytics, and dynamic heat-map visualization to assess collision risks at machine and zone levels. A three-week deployment on a construction site identified spatiotemporal risk hotspots and recurring near-miss patterns using meter-level positioning accuracy, demonstrating a shift from reactive control to proactive, data-driven safety management
Wearable sensors are increasingly being used to monitor worker activity and safety. While Human Activity Recognition (HAR) studies predominantly rely on kinematic data, physiological signals remain mostly underexplored due to the delayed nature of physiological responses. This study investigates the fusion of physiological and kinematic modalities by systematically evaluating temporal offsets to compensate for physiological latency. Results show that classification performance is sensitive to delay magnitude, with optimal performance achieved using an 8-second window and a 4-second time lag. Based on these findings, a latency-aware fusion protocol is proposed to enable robust multimodal HAR in wearable-based worker monitoring systems.
Automating regulatory compliance in the Architecture, Engineering, and Construction industry remains a challenge due to the semantic gap between natural language regulations and machine-interpretable code. Large Language Models (LLMs) are promising to bridge this gap, however, they often suffer from the lack of domain knowledge to correctly interpret and digitize a regulatory statement. This paper proposes a neuro-symbolic AI pipeline that utilizes GraphRAG (Retrieval-Augmented Generation) to ground LLMs in domain ontologies and SHACL templates. A multi-stage prompting technique is applied, that first maps regulatory terms to their equivalents in the ontology, then introduces First-Order-Logic as an intermediate representation to ensure isomorphic translation from text to code, and finally converts the text to a SHACL shape. The results demonstrate that this method reduces hallucinations and improves the precision of digital rule generation, contributing to a scalable methodology for transforming complex building regulations into machine-interpretable code.
In non-GNSS environments, such as mountain tunnel construction sites, high-precision position estimation is challenging. This research proposes a real-time tracking method using LiDAR to capture reference points as a positioning technique for non-GNSS environments. Using this to acquire backhoe coordinates during invert excavation enabled the automation of rough excavation for the invert. Furthermore, by providing an overview visualization of the processes before and after automated construction, the surrounding conditions, and related tasks on a digital twin, it demonstrated safe automated construction management from remote locations. This research contributes to the automation and unmanned operation of invert excavation in mountain tunnels.
Energy poverty measurement at the household level is sensitive to indicator choice and data availability. This study evaluates energy poverty in Dublin using a set of expenditure-based, income-sensitive, and needs-based indicators with a Building Energy Rating (EPC) dataset. Household income uncertainty is addressed through scenario-based income assumptions, enabling estimation of indicator-specific thresholds and escape income levels. Results reveal divergence in household classification across indicators, driven largely by structurally high energy costs linked to dwelling characteristics. The findings highlight the limitations of single-indicator approaches and demonstrate the value of comparative, household-level frameworks for informing targeted energy poverty interventions in data-constrained contexts.
Soviet-era apartment blocks in Eastern Europe are being demolished, generating mixed waste and limiting circular reuse. Existing assessments rely on generic archetypes and lack element-level material detail. This study develops an automated algorithm to detect standardised Soviet building series for scalable material stock analysis. A distance-based classification using apartment layouts, floor area, height, and staircases was validated on 337 buildings and applied to Estonia’s building stock. The model achieved 70% series-level accuracy, with dominant series represent 61% of the stock. This element-level quantification enables targeted deconstruction and higher material recovery for circular economy planning.
