Point cloud registration plays a crucial role in processing large-scale building point cloud data. However, existing registration algorithms face challenges in effectively handling outliers in descriptor-based correspondence. This paper presents an automatic registration method for large-scale building point clouds that is capable of achieving swift and accurate registration without the need for initial

The integration of smart technologies is set to revolutionize pavement data collection and analysis, leading to more efficient decision-making in Pavement Management Systems (PMS). Smart pavements, featuring embedded sensors, offer continuous streams of high-quality real-time data, enhancing the PMS data analysis process. This paper provides a detailed examination of these embedded smart systems, discussing

In this paper, a terrain-adaptive motion planner is developed specifically for articulated construction vehicles (ACVs) to address instability issues caused by elevation changes on unstructured construction sites—challenges that traditional 2D motion planners struggle to manage effectively. The proposed planner adopts a modular framework, incorporating a terrain elevation model, an articulated vehicle

Building sustainable habitats on the moon has been planned for decades. However, applying fully automated construction systems is still challenging in altered environments. Teleoperation, which is the remote control of the machine, can serve as an intermediate phase before achieving fully autonomous systems. Since the teleoperation between operators on the earth-ground and robots on the lunar surface

The inherent complexity of historic buildings, particularly their internal structures, presents significant challenges to the efficiency of digital model creation. This paper aims to enhance modeling efficiency by automating the creation of timber frames using a procedural modeling method. It translates the architectural rules used by local carpenters into modeling rules for procedural modeling, allowing

Construction safety standards are in unstructured formats like text and images, complicating their effective use in daily tasks. This paper compares the performance of Retrieval-Augmented Generation (RAG) and fine-tuned Large Language Model (LLM) for the construction safety knowledge retrieval. The RAG model was created by integrating GPT-4 with a knowledge graph derived from construction safety guidelines

Infrastructure projects often encounter performance challenges, such as cost overruns and safety issues, due to complex risk interactions and systemic risks. Existing literature treats risk interactions and systemic risks separately and relies on models that struggle with nonlinearities, adaptability, and practical applications, leading to suboptimal risk management. To address this gap, this paper

Visually monitoring progress and geometric quality on site using Mixed Reality (MR) and overlaid Building Information Model (BIM model) is challenging, particularly in complex contexts like complex mechanical, electrical, and plumbing (MEP) systems. This paper proposes and evaluates four individual methods and three combined ones for automated object recognition and deviation evaluation, based on the

The dusty environments of autonomous excavation can affect the performance of the sensors onboard the vehicle. Specifically, airborne dust clouds can be perceived as solid objects if not addressed appropriately, which can lead to irrational movements that risk safety. In this article, a light detection and ranging (LiDAR) and red-green-blue (RGB) image sensor fusion model was developed to filter airborne

In claim contracts, project owners and contractors set negotiated prices and exemption amounts for price adjustments to deal with the uncertainty of material prices, which is often overlooked in the optimization of procurement strategies. Therefore, considering contract claims, this paper constructs an optimization model for contractors’ traceable procurement strategies to address the multi-stage,

Rutting is a type of structural road damage that seriously affects traffic safety, and rutting conditions are typically analyzed only from a two-dimensional cross-sectional perspective. Rutting detection currently lacks directional features and trends along the traveling direction. To address this issue, this paper develops a rutting extraction methodology from vehicle-borne laser point clouds to reflect

Estimating construction scene depth from a single image is crucial for various downstream tasks. Self-supervised monocular depth estimation methods have recently achieved impressive results and demonstrated state-of-the-art performance. However, the low-light conditions and dynamic scenes on construction sites pose significant challenges to these methods, hindering their practical deployment. Therefore

The advancement of emerging 3D concrete printing (3DCP) has been hindered by two significant challenges: the weak tensile properties of conventional concrete and the difficulty of simultaneously placing reinforcement during printing. In this paper, engineered cementitious composites (ECC) with superior tensile properties along with an in-process reinforcement technique through laying CFRP meshes between

Despite rapid advancements in track condition assessment technologies, current railway track facility management (FM) often results in cost-ineffectiveness as well as maintenance- and operation-inefficient outcomes. However, the challenges in current practice and the requirements for enhancing track FM decision-making processes have not been identified in a comprehensive and structured manner by any

Quantification and optimisation of Construction Waste (CW) in the design stages are vital to implementing preventive CW management measures. Previous ICT-integrated CW models are not efficiently upscaled to achieve an interoperable and automated workflow. Therefore, this paper presents a BIM-integrated semantic framework for CW quantification and optimisation from the early design stages. A CW data

Large-scale earthmoving operations, common in mining excavation, contribute significantly to Greenhouse Gas (GHG) emissions. This paper introduces a simulation-based system aimed at quantifying these emissions and identifying practical and achievable steps for reducing them. The system we developed considers site-specific factors, including equipment specifications, topography, route, and weather conditions

To address the challenges associated with continuous compaction control (CCC), this paper investigates a CCC framework that incorporates dynamic cone penetration (DCP) and lightweight deflectometer (LWD). Field tests were conducted on 12 strip-shaped and two rectangular embankments. The compaction meter value (CMV) exhibited a linear correlation with the DCP and LWD test results (DPI and ELWD). The

Construction tower cranes exhibit significant nonlinear characteristics and high flexibility due to limited control input, posing major challenges for controller design and stability analysis. To achieve anti-sway control while constraining system variables within a safe range, a new given-performance anti-sway control strategy has been successfully developed by combining composite sliding mode control

In recent years, the concept of Digital Twins (DT) has emerged as a promising solution for real-time monitoring and proactive maintenance of complex engineering systems. This systematic review paper provides a comprehensive overview of the current state-of-the-art in DT technology for road pavement. The paper aims to bridge the gap between the theoretical physic-based model and DT and its practical

Vehicle and drone-mounted surveillance equipment face severe computational constraints, posing significant challenges for real-time, accurate crack segmentation. This paper introduces the crack location segmentation transformer (CLST) to address these issues. Images are processed to better resemble patches associated with cracks, enabling precise segmentation while significantly reducing the model’s

Distributed photovoltaic power systems, typically deployed in complex scenarios like irregular rooftops, present a challenging detailed cable routing problem (DCRP). This involves grouping solar modules and routing cables to connect each group, traditionally addressed through manual design. This paper presents a variable-depth large neighborhood search (VDLNS) algorithm to address the DCRP, which is

Accelerated bridge construction (ABC) demands precise alignment of prefabricated members to prevent assembly failure. Conventional methods struggle to localize shear connectors from point cloud data (PCD) generated by structure-from-motion due to its sparsity. This paper introduces a robust method for shear connector localization using PCD generated by a neural radiance field and a three-step narrowing-down

As the use of predictive models in construction rapidly increases, the need for preprocessing raw construction data has become more critical. This systematic review investigates data preprocessing techniques for machine learning (ML), deep learning (DL), and reinforcement learning (RL) models in the construction domain. Through a comprehensive analysis of 457 studies, the prevalence of six data types

Road work zones pose significant safety risks to both vehicles passing by and the construction workers moving within the work zones. Over recent years, significant research efforts have been dedicated to work zone safety, particularly by leveraging emerging technologies. This paper aims to review the literature on performance evaluation of safety technologies designed to mitigate struck-by hazards

CrackMamba, a Mamba-based model, is designed for efficient and accurate crack segmentation for monitoring the structural health of infrastructure. Traditional Convolutional Neural Network (CNN) models struggle with limited receptive fields, and while Vision Transformers (ViT) improve segmentation accuracy, they are computationally intensive. CrackMamba addresses these challenges by utilizing the VMambaV2

Digital twins are evolving to oversee the entire construction life cycle, with a strong emphasis on sustainability across environmental, financial, regulatory, and administrative dimensions. This paper introduces a methodology for managing existing bridges through an adaptable digital twin. The aim of this research is to develop a framework for constructing digital twins that, by enabling structural

A virtual model that mathematically represents operational behaviors is essential for implementing the concepts of digital twins (DTs) and building information modeling (BIM) to achieve intelligent, optimal building operations. However, current research lacks an approach to reliably construct virtual models. This paper introduces a concept named virtual in-situ modeling (VIM), designed to comprehensively

Semantic segmentation with deep learning plays a crucial role in various fields, including civil engineering, particularly in tasks such as damage assessment and urban planning. This paper addresses the challenge of efficiently training deep learning models for semantic segmentation with a limited set of annotated data, thus reducing the burden of ground truth labeling. An active learning strategy

Understanding how workers perceive risk is essential to construction safety management. Firstly, an event-related potential (ERP) experiment was conducted to investigate the relationship between risk, likelihood, and severity. Then, a linear model was developed to predict workers' risk perception based on ERP components and quantify the relative importance of severity to likelihood. Finally, an additive

It is challenging to distinguish the defective areas using infrared thermography to automatically analyze external post-tensioned tendon duct grouting defects. To achieve efficient and stable automated detection, a lightweight real-time grouting defects detection method based on YOLO deep learning is proposed. Firstly, the Cutpaste data augmentation method was used to effectively alleviate the problem

Virtual reality-based experiments were conducted to assess the impacts of environmental pollutants (i.e., noise, vibration, and dust) on work performance. In these experiments, concrete chipping work was performed in eight different exposure environments based on exposure to three environmental pollutants to measure data related to work performance: (i) work performance metrics, including work duration

There has been an increasing demand for shield tunnel construction due to the extensive utilization and limited land in metropolitan cities. However, the behaviors of soils and rocks exhibit a high level of uncertainty in material modeling. Artificial Intelligence (AI) techniques exhibit huge potential in addressing geotechnical issues that involve non-linear soil-structure interaction. This paper

Construction sites with deep excavation in urban areas can induce ground deformation, potentially harming nearby infrastructure. Therefore, monitoring construction sites is crucial. Typically, a sidewalk is located adjacent to the construction site, and ground deformation can cause the displacement of paving blocks. Accurate measurement of paving block displacement and cracks is essential. This paper

Point cloud semantic segmentation is significant for managing and protecting architectural heritage. Currently, fully supervised methods require a large amount of annotated data, while weakly supervised methods are difficult to transfer directly to architectural heritage. This paper proposes an end-to-end teacher-guided consistency and contrastive learning weakly supervised (TCCWS) framework for architectural

In land development projects, effective control of the engineering progress is crucial for managing construction quality and costs. However, the conventional approach to monitoring progress is inadequate for large-scale projects. This paper proposes a technique that utilizes UAV images and machine learning techniques to monitor land development projects. The object detection and image segmentation

An image-based methodology is presented for the automatic generation of geometric building models at LOD4, incorporating both interior and exterior geometrical information. Existing approaches often focus on simplified geometries for either exteriors or interiors, leading to integration challenges due to data complexity and processing demands. This methodology addresses these challenges by utilizing

Digitization is an important part of efficient infrastructure maintenance. Means to achieve a digital asset database include precise 3D surveys of the physical assets and advanced automated recognition of objects of interest for documenting, maintenance and further analysis purposes. To this end, fast data collection of railway infrastructure environments can be obtained using a mobile laser scanner

Quantitative assessment of cracks in concrete bridges is crucial for structural health monitoring and digital twin. However, the training of crack segmentation models relies heavily on annotation resources, and their segmentation capabilities are often unsatisfactory in terms of the accuracy of boundary location of thin cracks encountered in practice. In this paper, an active-learning-integrated crack

Prefabricated construction has become an increasingly important focus area in the development of the construction industry. Determining an optimal construction process scheduling program is an urgent challenge during the project execution stage. This paper presents a multi-objective optimization problem with the objective function of minimizing the total construction time and maximizing the coordinated

Three-dimensional (3D) models, characterized by their visualization, accuracy, and interactive information presentation, effectively facilitate collaboration and optimize management throughout the construction process. However, existing 3D reconstruction methods frequently fail to simultaneously satisfy the requirements for onsite applicability and fast performance. To address this challenge, this

The on-site assembly process in modular construction (MC) requires precise placement of bulky modules, which involves dangerous and labor-intensive manual work in the current practice. This study aims to automate the process by designing a hybrid pose adjustment (HyPA) robot to achieve complete pose control of the module. To this end, this paper presents the mechanism design and working principle of

Effective management of near-miss data is essential for proactive safety practices in construction. Traditional reporting and management methods face challenges such as data loss, susceptibility to manipulation, and poor traceability, which undermine their reliability and collaborative efforts. Blockchain technology can enhance data integrity, security, transparency, and reliability in safety data

Generative design has been instrumental in expanding designers' ability to create diverse alternatives. However, the current generative building morphology design presents two broad weaknesses. Firstly, it fails to consider the interaction between a design and its backdrop context, particularly in high-density metropolitan areas. Secondly, it fails to harness existing design knowledge embedded in existing

Transforming building code information into a machine-processable format is essential for automated compliance checking, yet it presents significant challenges. A prompt-based framework was developed to automate the conversion into a logic programming language. Its effectiveness was assessed by testing the framework on 51 requirements from the International Building Code (IBC) 2015, achieving 97.37 %

Litigation among stakeholders in construction projects has a significantly negative impact on successful project completion and overall performance. Prompt decision-making in relation to litigation is crucial, but the manual review of extensive document sets is time-consuming. In this paper, the natural language processing (NLP) technique was applied to litigation data to develop a model for case summarization

Modular construction is recognized as a promising solution to the pressing housing demands of densely populated cities. However, temporary storage of modules in urban environments and the risk of damage during transportation present significant supply chain challenges. Some governments have begun investing in integrated operation centers (IOCs) to provide module storage and repair services. However

This paper addresses the challenge of dispersed accident-related information on construction sites, which hinders consensus among employers, workers, supervisors, and society. A robust NLP-based framework is presented to analyze and structure accident-related textual data into a comprehensive knowledge base that reveals accident patterns and risk information. Accident scenarios, including frequency

To address the increasing issue of cracks in metro shield tunnels, this paper proposes the YOLOv8-GSD model, which integrates DySnakeConv, BiLevelRoutingAttention, and the Gather-and-Distribute Mechanism with the YOLOv8 algorithm. This model is designed for detecting and segmenting cracks in tunnel linings and employs a pixel grouping method to measure crack length and width. Using a real crack dataset

In the field of curtain wall construction, manual installation presents significant safety hazards and suffers from low efficiency, while automated installation is constrained by the limited localization capabilities of curtain wall installation robots. In this paper, an automated installation solution based on machine vision is proposed, and a detailed discussion of several steps involved is provided

Cost overruns in maintenance projects should be monitored and effectively managed by construction professionals using proactive systems. To establish more effective proactive systems for addressing cost overruns in maintenance projects, this paper presents a topological approach for machine learning-based prediction, integrated into various machine learning models to enhance the feature selection process

Targeted to the challenge of indoor scene understanding for intelligent devices, this paper question focuses on enhancing accuracy in semantic information extraction. A framework including a dual hierarchical attention network, transfer learning, interpretability analysis, and modeling module is applied to segment and reconstruct the indoor scene. A high-rise as-built building case is used to verify

An efficient method for predicting occupants' thermal comfort is crucial for developing optimal environmental control strategies while minimizing energy consumption in buildings. This paper presents a non-invasive vision-based personal comfort model that integrates thermographic images and deep learning. Unlike previous studies, the entire thermographic image of the upper body is directly used during

Semantic segmentation is crucial for interpreting point cloud data and plays a fundamental role in automating the creation of as-built BIM. Existing neural network models for semantic segmentation often heavily rely on the training dataset, resulting in a significant performance drop when applied to new datasets. This paper presents AttTransNet, a neural network model for automated point cloud semantic

To effectively mitigate heat risks, it is crucial to pinpoint areas of high vulnerability and assess the severity of heat-related threats to construction workers. This paper advances the understanding of heat risks in construction by mapping the associated risks across time and space to support informed decision-making. This paper presents a framework for heat risk monitoring, enabled by a construction

Reinforcement Learning (RL) requires many interactions with the environment to converge to an optimal strategy, which makes it unfeasible to apply to wheel loaders and the bucket filling problem without using simulators. However, it is difficult to model the pile dynamics in the simulator because of unknown parameters, which results in poor transferability from the simulation to the real environment

Prefabricated construction is increasingly replacing traditional methods due to its higher productivity, superior quality, and shorter construction time. This paper aims to optimize production and logistics collaboration within a three-tier prefabricated supply chain network to reduce overall costs and enhance response efficiency. A decision model was developed that integrates factory and logistics

The electrification of construction machinery has created a perceptible future trend of the development of electric construction machinery collaboration systems (ECMCSs). However, there is a lack of research on energy-efficient operation of ECMCS. This paper proposes a theoretical configuration and scheduling framework promoting the applications of ECMCSs. In the configuration stage, this paper considers

Organisations increasingly rely on data-driven strategies, utilising analytics to achieve competitive advantages. This paper systematically investigates the integration of big data into Building Information Modeling (BIM) within the Architecture, Engineering, and Construction (AEC) sectors, named “big BIM data.” Employing mixed methods of systematic and bibliometric analysis, it synthesises findings

District heating systems offer means to transport heat to end-energy users through underground pipelines. When leakages occur, a lack of reliable monitoring makes pinpointing their locations a difficult and costly task for network operators. In recent years, aerial thermography has emerged as a means to find leakages as hot-spots, with several papers proposing image analysis algorithms for their detection