3D laser scanning can serve the geometric deformation detection of steel structures. However, the process of handling large-scale point clouds remains labor-intensive and time-consuming. This paper presents an automated approach to extracting the precise axes from point clouds and updating the associated BIM model for steel structures. The strategy involves the initial geometry extraction from IFC

While Generative Pre-Trained Transformers (GPT)-based models offer high potential for context-specific information generation, inaccurate numerical responses, a lack of detailed information, and hallucination problems remain as the main challenges for their use in assisting safety engineering and management tasks. To address the challenges, this paper systematically evaluates the effectiveness of the

The assessment of structural safety and a thorough understanding of buildings' structural behavior are critical to enhancing the resilience of the built environment. Cultural Heritage (CH) buildings present unique diagnosis challenges due to their diverse designs and construction techniques, often requiring attention during maintenance or disaster relief efforts. However, collaboration across CH and

The performance of existing structures is often compromised by damage and condition changes, challenging current evaluation methods in accurately assessing their service status. This paper introduces a structural performance evaluation method via digital-physical twin and multi-parameter identification. Key features include: (1) a digital twin framework that integrates non-contact sensing data with

Cable-in-duct installation is one of the most challenging contact-rich interior finishing tasks for construction robots. Such precise robotic cable manipulation skills are expected to be endowed with high adaptability towards unstructured on-site construction activities via Sim2Real transfer. This paper presents a Sim2Real transferable reinforcement learning (RL) policy learning method for multi-stage

The application of machine learning (ML) in the building domain has rapidly evolved due to developments in ML algorithms. Abundant studies have reviewed the use of ML algorithms to address building-domain-related challenges, but some research questions remain unclear: (i) what is the landscape of ML application topics in building domain, (ii) what are the preferences among different ML application

Construction sites are high-risk environments owing to the dynamic changes and improper placement of temporary facilities, requiring comprehensive safety management and spatial hazard analyses. Existing construction site layout planning (CSLP) studies have limitations in identifying hazardous zones and accommodating the flexibility stakeholders require. This paper introduces a site information model

Computer vision models have shown great potential in pavement distress detection. There is still challenge of low robustness under different scenarios. The model robustness is enhanced with more annotated data. However, this approach is labor-intensive and not a sustainable long-term solution. This paper proposes a semi-supervised instance segmentation method for road distress detection based on deep

While existing floor tiling robots provide automated tiling for small tiles, robots designed for large and heavy tiles are rare. This paper develops a six-degree-of-freedom Stewart platform-based floor tiling robot for automated tiling of heavy tiles. The key contributions of this paper are: 1) establishing mechanical and kinematic models for a parallel robot to enhance the payload capacity of existing

The adoption of Building Information Modeling (BIM) in construction site layout planning and activity scheduling faces challenges due to the lack of standardized approaches for digitally reproducing and organizing site elements that meet information requirements of diverse regulatory frameworks and stakeholders' use cases. This paper addresses the question of how to streamline the development of BIM

Traditional construction management methodologies often fail to address unforeseen challenges and uncertainties. This paper highlights that projects can exist in different states, often unidentified by project managers. These varying states necessitate different approaches, indicating that one-size-fits-all methods are insufficient. Using project data, entropy calculations, and simulations within a

Construction cost forecasting is vital for tendering processes, enabling the evaluation of bidding offers to maximize revenues and avoid losses. In recent years, the automation of this forecasting process has gained attention due to the limitations of traditional approaches that rely on human experts, which can lead to subjective judgments. This paper introduces an ensemble learning decision-support

Concealed pipeline maintenance in aging residential areas faces a key challenge of discrepancies between existing data and reality. Ground-penetrating radar with dense, high-speed 3D monitoring capabilities can provide massive data, but effective analysis is difficult due to the presence of irrelevant information. To accurately extract target information, this paper first proposes a 3D data array block

The inadequate visual environment reduces the accuracy of underwater bridge pier fracture detection. Consequently, this paper suggests enhancing the backbone of the Real-Time Detection Transformer(RT-DETR) model to serve as the backbone of the YOLOv8 model. This will be achieved by substituting the Faster Implementation of CSP Bottleneck with 2 convolutions(C2f) module with the Poly Kernel Inception(PKI)

Accurate estimation of construction costs and schedules is crucial for optimizing project planning and resource allocation. Most current approaches utilize traditional statistical analysis and machine learning techniques to process the vast amounts of data regularly generated in construction environments. However, these approaches do not adequately capture the intricate patterns in either time-dependent

This paper introduces a bucket fill factor estimation method for earthmoving machinery aimed at solving sensor field-of-view blindness in measurements. Utilizing a point cloud repair technique, the method accurately reconstructs the 3D morphology of materials inside the bucket, even under occlusion conditions. The process begins by merging multiple frames of point cloud data to enhance information

The visual sensing method is an effective way to address long-term health monitoring of bridges. However, bridge defect detection based on visible light imaging mainly relies on grayscale and regional edge gradient information, which brings challenges such as limited information dimensions and complex background. This paper introduces a bridge defect detection method that leverages hyperspectral imaging

Despite significant research attention on Augmented Reality (AR) in construction, there is a lack of literature on its application trends and future prospects in the U.S. construction industry. The objective of this paper is to investigate the current state of AR in construction, benefits, and drivers and offers actionable suggestions for enhancing AR applications. A systematic critical review and

Deep learning-based structural crack detection utilizing fully supervised methods requires laborious labeling of training data. Moreover, models trained on one dataset often experience significant performance drops when applied to others due to domain shifts prompted by diverse structures, materials, and environmental conditions. This paper addresses the issues by introducing a robust self-training

This paper addresses the diverse scenarios of bridge crack segmentation, proposing a method for detecting cracks on land and underwater using the Segment Anything Model (SAM) prompted by a fractal dimension matrix. The proposed method does not require additional training and obtains fractal feature information of cracks through fractal dimension matrix calculation. These feature information serve as

This paper introduces a hybrid optimization algorithm, Pilgrimage Walk Optimization - Differential Evolution (PWO-DE), inspired by Taiwan's cultural traditions, to fine-tune large language models (LLMs) for government procurement legal consulting. Addressing the unique requirements of Traditional Chinese, this research develops two tailored LLMs, Llama3-TAIDE and Taiwan-LLM, which significantly enhance

Microscale environmental components, such as street furniture, sidewalks, and green spaces, significantly enhance street quality when properly identified and managed. Traditional in-person audits are time-consuming, so virtual audits using streetscape images and computer vision have been explored as alternatives. However, these often lack a comprehensive range of microscale components and do not consider

Image processing and Convolution Neural Networks (CNN) are widely used for structural damage assessment. Datasets with damages on similar backgrounds are commonly used in past studies for training and testing of CNN models. These models will often fail to detect damage in images of real infrastructure. A dataset is created which consists of 3750 real images along with its annotations, having diverse

The absorption and refraction of light by water made high-precision 3D (three-dimensional) reconstruction of underwater concrete a challenging task. This paper proposed a 3D reconstruction method combining line structured light and stereo vision. To improve the reconstruction accuracy, the epipolar constraint was introduced in the light plane calibration process to limit the fringe noise data during

Cracks are a prevalent disease on pavement concrete materials. Timely assessment and repair of concrete materials can significantly extend their service life. However, accurate segmentation has always been difficult due to their random distribution, tortuous geometry, and varying degrees of severity. To address these challenges, a Multi-stage Supervised Dual-encoder network for Crack segmentation on

Occupational health risks such as falls from height, electrocution, object strikes, mechanical injuries, and collapses have plagued the construction industry. Deep learning algorithms are exploding due to their outstanding analytical capabilities and are believed to improve safety management significantly. Therefore, this paper systematically reviewed the literature on DL algorithms from 2015 to 2024

New sustainable technologies, such as additive manufacturing (AM), have recently been adopted in the construction industry, significantly reducing construction completion times and effectively repairing and remanufacturing components. While AM's implementation in construction is still diffuse, this review contributes to a better understanding of how this technology is interpreted and utilized, exploring

Much has been exhorted to pursue participatory building design (PBD) but little has been done to enhance it owing to difficulties such as participant inclusion and clarity of expression. An opportunity is enabled by metaverse and generative AI technologies. This paper aims to explore this by developing a framework that integrates metaverse and generative AI. To start, a desktop study is conducted to

In 3D printing, selecting appropriate printing parameters based on material rheology is critical for achieving compatible filaments with optimal performance. However, the process of aligning printing parameters with rheological properties lacks a robust theoretical foundation. This study investigates the influence of printing and rheological parameters on the relative printing length of molded filaments

Human error significantly contributes to construction accidents, exacerbated by the lack of a digital tool for assessing and improving workers' safety performance. This paper addresses this gap by developing: 1) a Real-Time Safety Performance Assessment and Report System; 2) a Safety Behavior Self-Assessment and Improvement System; and 3) a Sensor-Based Safety Performance Analytic Mobile System (SBSPAMS)

The noise produced in demolition sites can mask safety-critical sounds that inform operators about task conditions and hazards. These problems are exacerbated in teleoperated demolition, where the separation between operator and site compromises operators' situation awareness and cognitive loads. This paper assessed the effects of environmental sounds with and without attenuation on the operators'

This paper presents a digital twin (DT) platform integrated with an online optimization algorithm that combines Bayesian Optimization (BO), Categorical Boosting (CatBoost), and the Nondominated Sorting Genetic Algorithm (NSGA)-III. The platform enables multi-objective dynamic optimization of shield parameters under varying geological conditions. Using the Wuhan Metro as a case study, the effectiveness

A key challenge in terrestrial laser scanning (TLS) based as-built survey is the presence of data voids, reflection artifacts, and hotspot effect on glasses. This paper investigates the effects of scanning range, illumination condition, instrument height, and spatial offset of an occluded object between the scanner and glasses with respect to these data artifacts. Experimental results show that ordinary

Traditional safety inspections require significant human effort and time to capture site photos and textual descriptions. While standardized forms and image captioning techniques have been explored to improve inspection efficiency, compiling reports with both visual and text data remains challenging due to the multiplicity of safety-related knowledge. To assist inspectors in evaluating violations more

Physics-based simulations are essential for designing autonomous construction equipment, but preparing models is time-consuming, requiring the integration of mechanical and geometric data. Current automatic modeling methods for modular robots are inadequate for construction equipment. This paper explores automating the modeling process by integrating mechanical data into 3D computer-aided design (CAD)

Daily reports are important in construction management, informing project teams about status, enabling timely resolutions of delays and budget issues, and serving as official records for disputes and litigation. However, current practices are manual and time-consuming, requiring engineers to physically visit sites for observations. To fill this gap, this paper proposes an automated framework to generate

The construction industry records more hazards compared to any other sector. Protective equipment, such as guardrail systems, is essential for protecting workers from deadly falls but may quickly become incompliant after installation. Yet, many construction projects do not have the resources to dedicate personnel to perform the inspection as frequently as needed. Therefore, this paper proposes an automated

Recent advancements in structural health monitoring have highlighted the necessity for accurate three-dimensional (3D) damage mapping on digital twins, moving beyond traditional methods such as photogrammetry, which frequently struggle to capture intricate planar surfaces. To address this limitation, this paper proposes a new advanced 3D reconstruction method and its integration with 3D damage mapping

The uncontrollable nature of international construction projects requires continuous monitoring of issues in the host country. News articles can provide relevant information to monitor the issues, but the manual investigation of substantial news text is impractical. This paper proposes a framework to automatically collect information related to the host country's business environments from news text

With the increasing demand for water conservancy engineering (WCE), the number of safety accidents during construction has continued to rise, requiring an urgent improvement in construction safety. The existing safety management regulations for water conservancy construction engineering (WCCE) comprise a considerable amount of text, with cross-references between different standards severely reducing

Structural adaptivity and readiness for change are some of the key enablers of resilient and sustainable architecture. This paper presents an approach to the design and construction of reconfigurable timber slabs, termed ReconWood Slabs, which integrate a stress-driven design approach and cyber-physical construction processes to enhance data-informed circularity. Using advanced computational design

Asset tracking is crucial for managing prefabricated construction projects, as delayed deliveries might disrupt interdependent offsite and onsite activities, causing economic losses and disputes. To clarify liabilities, tamperproof asset tracking and delay propagation analysis are necessary. To achieve this, a BIM-blockchain integrated framework via smart contracts is proposed given rich information

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