This study proposes a deep line segment detection model named DLSD, for identifying four ubiquitous line segments on concrete pavements: joint, sealed joint, bridge expansion joint, and roadway boundary. DLSD associates a category with the triple‐point representation to encode a line segment. Its network employs a localization head and a classification head, attaching several auxiliary branches to

To overcome the limitations of fragility analysis in the assessment of partition walls, specifically data shortage, general uncertainties, and subjective criteria, this study proposes a probabilistic method to evaluate seismic damage of partition walls. A proposed multi‐spring numerical model balances the damage representation and computational efficiency in simulations, thus avoiding extensive experimental

As a common defect in geotechnical environments, the fracture response characteristics of holes under complex loading directly affect the catastrophic evolution mode of subsurface rock engineering. In this study, the effect mechanism of different defects on the mechanical properties and fracture damage behavior of rocks was investigated by uniaxial compression test, and the rocks fracture evolution

Named Entity Recognition (NER) is a fundamental task for automatically processing and reusing documents. In traditional methods, machine learning has been used relying on costly high-quality datasets. This paper proposed an NER method based on fine-tuning Large Language Models (LLMs) with low-quality datasets for construction documents. Firstly, low-quality datasets were semi-automatically generated

In recent years, the advancement of digital technologies such as building information modeling (BIM), internet of things (IoT), and geographic information system (GIS) has had many impacts on the construction industry. However, limited research has been conducted on the integration of BIM, IoT, and GIS technologies, especially in construction resource monitoring. Therefore, this paper presents a state-of-the-art

Current expert-based approaches to determining the weights of different safety management elements during contractor safety performance are time-consuming and potentially biased.Hence, this paper evaluates analytics-based approaches, i.e., supervised learning, cluster-then-predict and two-level variable weighting K-Means (TWKM) (an extension of the traditional K-Means clustering algorithm), against

Climbing robots present transformative potential for automated structural inspections, yet their deployment remains limited by the reliance on manual control due to the absence of effective environment perception and path‐planning solutions. The critical bottleneck lies in the difficulty of generating accurate planning maps solely through onboard sensors due to the challenge of capturing open, large‐scale

This paper proposes a method for bridge damage identification using a small amount of damage labeling data. This method first trains a deep neural network (DNN) with undamaged bridge inclination responses as inputs and bridge equivalent loads as labels. The ratio curve related to the bridge damage state can be obtained by quantifying the change in the DNN prediction error before and after bridge damage

To systematically explore the seismic performance of shear walls and frame-shear wall structures under multi-directional seismic actions, three shear wall specimens and one frame-shear wall specimen are fabricated and the quasi-static loading tests are carried out. The shear wall specimens are loaded in oblique direction, in-plane direction, and out-of-plane direction, while the frame-shear wall specimen

Building natural ventilation is a sustainable approach to reducing energy use and emissions from buildings by minimizing reliance on energy-intensive systems. Computational Fluid Dynamics (CFD) simulations are often used to predict natural ventilation, enhance building design, and improve indoor air quality. However, CFD simulations are time-consuming and computationally resource-intensive due to the

The complexity of spiral tunnels exacerbates driving safety risks, making it essential to elucidate the mechanisms of various factors to conduct effective research in this area. This study, based on the “road-driver-vehicle” feedback process, analyzed the impact mechanisms of three dimensions—road alignment, lighting environment, and traffic flow—on driver behavior and vehicle dynamics in spiral tunnels

Bridges are susceptible to severe damage from natural disasters, heavy traffic loads, and material degradation, necessitating timely and accurate information for effective emergency response. Current bridge disaster management systems often fail to meet real-time requirements due to interoperability challenges and fragmented functionalities across different phases. This paper systematically reviews

The 3D wireframe model provides concise structural information for building reconstruction. Traditional geometry-based methods are prone to noise or missing data in 3D data. To address these issues, this paper introduces Edge-NeRF, a 3D wireframe reconstruction pipeline using neural implicit fields. By leveraging 2D multi-view images and their edge maps as supervision, it enables self-supervised extraction

Reasonable shape and cross-section design of free-form Single-Layer Reticulated Shells (SLRSs) are crucial for their superior static performance and material efficiency. However, traditional metaheuristics face high computational costs and are prone to converging to local optima when optimizing these factors simultaneously, often leading to necessity of carrying out decoupled design processes. This

Large-scale earthwork transportation encounters queuing congestion and dynamic uncertainties, while existing methods ignore complex traffic behaviors and exhibit limited responsiveness and generalization. This paper proposes a multi-task Deep Reinforcement Learning (DRL) framework for the dynamic scheduling of large fleets across supply sites and traffic networks. In the framework, multiple agents

Engineered geopolymer composites (EGC) have emerged as promising alternatives to engineered cementitious composites (ECC), largely owing to their low carbon emission potential. However, typical alkaline activators used in geopolymer often contribute to significant environmental impact. Sodium carbonate (SC), also known as natural alkali, is derived from abundant natural deposits and offers a potential

Texturing Building Information Model (BIM) with up-to-date Unmanned Aerial Vehicle (UAV) images has brought substantial benefits to building façade inspection. However, current image-to-BIM registration methods are sensitive to UAV positioning accuracy and façade features. Additionally, perspective and geometry distortions on UAV images hinder the texturing of curved façades. To address these issues

The acquisition of pavement distress images using UAVs presents unique challenges compared to ground-based methods due to differences in camera configurations, flight parameters, and lighting conditions. These factors introduce domain shifts that undermine the generalizability of segmentation models. To address these limitations, an interactive segmentation model, CDCR-ISeg, is proposed to bridge the

Accurate monitoring and visualization of urban road substructure and targets are impeded by challenges in inverting Ground Penetrating Radar (GPR) data, especially under multiple inversion objectives and complex road conditions. To address this challenge, a deep learning-based multi-scale inversion approach, termed MSInv-GPR, is proposed, which builds on the Pix2pix Generative Adversarial Network (Pix2pixGAN)

Generative design has its roots in the 1990s and has become an intense research topic for bringing the power of artificial intelligence to various aspects of architecture practices. The recent advancements in artificial intelligence have made a methodological shift in innovative approaches to generative design, fueled by the proliferation of big data. This paper provides a comprehensive review of emerging

Despite advancements in computer vision techniques like object detection and segmentation, a significant gap remains in leveraging these technologies for hazard recognition through natural language processing. To address this gap, this paper proposes VQA-RESCon, an approach that combines Visual Question Answering (VQA) and Referring Expression Segmentation (RES) to enhance construction safety analysis

Wind-driven rain (WDR) changes the rain distribution on the building and rain intrusion amount into the damaged building. Severe wind-included damage and rainwater intrusion usually happen in low-rise buildings, and WDR distribution influences the vulnerability induced by wind and WDR of low-rise buildings. However, wind interference effects among buildings change the WDR distribution. This study used

Determining the optimal damping value of the isolation system in tall structures is challenging as it requires parametric studies and time-consuming nonlinear time-history analyses. Consequently, the influence of different parameters, such as displacement limitation, on the optimal damping of isolators in tall structures remains unclear. This study aims to investigate the optimal damping of isolators

The double steel plate concrete-composite shear wall has the advantages of high bearing capacity, strong ductility and reducing the amount of formwork, and has a wide application prospect. To research the seismic performance of the composite shear wall, a three-dimensional solid-shell model of composite shear wall was constructed based on the triaxial plastic-damage constitutive model of confined concrete

The brittle behavior of concrete often necessitates fiber reinforcement to improve load distribution and crack resistance. This study examines fiber orientation in steel fiber-reinforced concrete, achieved by strategically aligning fibers within the fresh composite. B-scan Ground Penetrating Radar (GPR) with full polarimetric imaging is utilized for non-destructive assessment of fiber alignment in

This research explores integrating artificial intelligence (AI) in energy optimization for smart homes and buildings, specifically focusing on using Aczel-Alsina aggregation operators within an interval-valued Fermatean fuzzy (IVFF) decision-making framework. The primary goal of this study is to develop a robust method for managing uncertainty and imprecision in energy optimization tasks. Using IVFF

The building sector accounts for 39.7% of global energy consumption and 42% of carbon emissions, highlighting the need for improved energy efficiency. While data-driven energy benchmarking is vital for conservation, current approaches face key challenges: limited datasets, suboptimal prediction algorithms, and inadequate scoring systems. This study proposes an AI-driven benchmarking framework using

In deep mining, the traditional technology of gob-side entry retaining by backfilling (GERB) would result in an increased degree of floor damage due to high stress and existence of pillar filling bodies. Especially when the GERB is implemented above confined water, due to long continuous excavation distance and large range of mining disturbance, there is usually a higher risk of floor water inrush

Understanding metro development patterns is crucial for cities to design efficient and well-connected transit networks. However, the heterogeneity in network scale and variations in development timelines pose challenges in identifying universal evolutionary patterns. This study introduces a hybrid clustering approach that integrates K-means with Dynamic Time Warping (DTW) Barycenter Averaging to analyze

Worker-robot collaboration (WRC) has emerged as a transformative approach to augmenting the productivity of the construction industry. However, the development of a safety monitoring method or system for stopping robot operations in emergency is imperative, especially for seamless WRC on site. This paper presents a digital twin-enabled safety monitoring system for seamless WRC on site, characterized

The intelligence of electric shovels plays a critical role in improving excavation efficiency and safety. A key challenge in intelligent excavation is generating an optimal excavation trajectory while considering material uncertainty. Therefore, an Unmanned mining Electric Shovel Trajectory Planning method based on the Point-by-point Incremental B-spline Curve under Uncertainty (UESTP-PIBCU) is proposed

Pavement preventive maintenance is one of the most fundamental use cases when deploying digital twins (DTs) for highway infrastructure management. To achieve this, it is essential to accurately predict the pavement conditions in future years. This paper developed a Spatial-Temporal Graph Attention network (STGAT) that can effectively capitalize on both spatial and temporal dependencies while addressing

Unbonded prestressed slab-column (UBPS-C) structures are prevalent in civil engineering due to their advantages, including simplified construction, reduced deformation, flexible arrangement, and cost savings. However, the absence of beams in UBPS-C structures may result in weakened resistance to progressive collapse, particularly when considering the potential negative effects of material, geometric

The hospitals play an indispensable role in daily life, serving critical functions such as treating injuries and providing medical services in disaster-affected areas. In recent years, there has been increasing attention to the seismic strengthening and reconstruction of hospital buildings. However, even if the hospital buildings meet seismic safety standards through seismic retrofitting, damage to

In order to investigate the vertical coherence of the along-wind aerodynamic fluctuating force on rectangular high-rise buildings with different side ratios, synchronized pressure tests were conducted for rectangular models with thirteen side ratios (1/8-8) under two turbulent boundary layer flows in the wind tunnel. The spatial correlation of along-wind fluctuating wind loads was investigated. Results

The wind and thermal microenvironment around PV arrays play an important role on their structural safety and power output. Most existing research predominantly focuses on numerical simulation methods, which usually involve various idealized assumptions that may lead to distortions in the simulation results. This study employs field tests to investigate the wind and thermal microenvironment of distributed

Incorporating supplementary cementitious materials (SCMs) like ground granulated blast furnace slag (GGBS) into gypsum-based blocks enhances their compressive strength. However, over-reliance on GGBS is a concern due to its extensive use in the construction industry. To address this, basic oxygen furnace slag (BOFS) was introduced as a substitute for GGBS in producing gypsum-based blocks. Three mixture

The pore structure plays a crucial role in the thermal conductivity of porous materials. Foam geopolymers, with their inherently low thermal conductivity, show strong potential for building insulation. However, a quantitative model correlating their multi-scale pore structure with thermal conductivity is still lacking. This study prepared six types of ground granulated blast furnace slag-based foam

The Time Constant RC is a key thermal characteristic of a building, combining the thermal resistance of the envelope (R) and the thermal mass (C). It serves as a useful indicator for ranking and prioritizing building retrofits by assessing the thermal performance of the building envelope through temperature measurements. However, accurately determining RC, especially on a large scale, poses significant

The anti-splitting property and durability are important indexes for developing structural crack damage and the bearing capacity of structures. Incorporating fiber can significantly improve the safety performance of reinforced concrete (RC) structures. However, the enhancement effect of different fiber content on the properties is different under severe corrosion environments. Therefore, durability

Traditional active-passive solar heating system face the problem of unsatisfactory installing space in densely built urban areas. Promoting solar louver for improving solar energy utilization has attracted enormous interest owing to its convenience and high efficiency. However, our understanding of thermal performance of solar louver as a passive heating method to enhance solar heat gain is limited

Engineered/Strain-Hardening Cementitious Composites (ECC/SHCC) are highly attractive for tensile/shear-critical regions in reinforced concrete structures due to their high tensile ductility and excellent crack control capacity. To ensure effective stress transfer, a sufficient bond between steel bars and SHCC is crucial, particularly for high-strength SHCC. This study experimentally and numerically

Recognition of household electricity consumption pattern aims to identify household electricity consumption preferences, facilitating household electricity consumption behavioral understanding and carbon emission reduction. In this study, a novel Largest Triangle Three Buckets-Dynamic Time Warping-Kmedoids (LTTB-DTW-Kmediods) model is proposed, combining Largest Triangle Three Buckets (LTTB) for dimensionality

The degradation of interlaminar shear strength (ILSS) of fiber-reinforced polymer (FRP) bars exhibits highly nonlinear characteristics when exposed to marine concrete environments. To address this phenomenon, a novel machine learning approach utilizing XGBoost algorithm was developed to predict ILSS retention (ILSSR). A comprehensive dataset was compiled from experimental results and existing literature

The growing demand for concrete, driven by population and infrastructure growth, contributes significantly to environmental degradation, as each ton of cement produced releases about one ton of carbon dioxide. Sustainable solutions are crucial, and biochar, derived from carbon-rich biomass through pyrolysis, offers a promising approach. Its carbon sequestration potential helps reduce emissions while

The shield tunnel with the circular cross section can be built without destroying the surrounding rock and ground buildings. Oil fires are particularly dangerous commonly existing from oil leaks caused by vehicle collision accident in circular cross section tunnel. The structural strength of tunnel’s ceiling and side walls should be significantly reduced by the fire. In addition, once a tunnel fire

The rapid expansion of underground infrastructure has driven the increased adoption of large-diameter shield tunneling, exposing limitations in traditional design methods for small-diameter tunnels under complex geological conditions. This paper presents an analytical method based on a thin curved beam model, incorporating multi-layered soil strata effects and compression-induced soil reactions on

Machine vision sensors face challenges in automating the monitoring of internal structural damage and deformation, with limited lifespan and resolution accuracy. This paper develops a high-durable machine vision strain sensor, MISS-Silica. The sensor's durability is enhanced through materials, processes, and algorithms, ensuring its lifespan aligns with that of the structure. It combines an endoscope

Strain is one of the key indicators for structural health monitoring. In this study, we developed a low‐cost microscopic vision‐based real‐time strain sensor using Raspberry Pi (called MISS‐Dym). By strategies for image processing accelerated and the specific running logic, the strain can be outputted at a frequency of more than 30 Hz in real time. The MISS‐Dym integrates multiple functions including

Poor workability is a significant issue that hinders the widespread application of geopolymers, while the effectiveness of the commonly used superplasticizers and retarders in geopolymers is often limited. To identify effective admixtures that can enhance the workability of geopolymers and promote their application, it is essential to elucidate the mechanisms by which these admixtures function within

Due to relative poverty, a low education level, and high population vulnerability, China's rural areas face challenges in preventing and controlling infectious diseases. Close contact data are essential for understanding the spread of infections, however, they are currently unavailable. This study provides the first data of close-contact behaviours in six types of indoor environment in China's rural

The Z-shaped combined bend is a critical component in pipeline systems and significantly impacts building energy consumption. Most existing research on its resistance characteristics focuses on establishing quantitative relationships between energy loss and various flow parameters, with limited attention given to the underlying flow mechanisms. Based on the resistance distribution, this study classifies

The sulfate-rich sewage sludge ash (SRSSA) could be recycling used as expansive agents in self-leveling cement mortars, and superabsorbent polymers (SAP) can effectively reduce the shrinkage of cement-based materials. Therefore, SRSSA and SAP were used as admixtures to prepare self-leveling cement mortars. The influences of SRSSA and SAP on the fluidity, strength, drying shrinkage, crack resistance

The long-term durability of textile-reinforced mortar (TRM) composites is essential for their effectiveness in masonry strengthening applications. Despite growing research efforts, the impact of prolonged environmental exposure, particularly in alkaline conditions, on TRM mechanical performance remains inadequately understood. This study evaluates the durability of TRMs subjected to dry, water-immersed

Recently, extreme weather events linked to global warming have increasingly impacted the construction industry. Global warming is primarily driven by carbon dioxide (CO2), methane, and nitrous oxide, with CO2 being the most significant contributor owing to fossil fuel combustion. Carbon capture, utilization, and storage (CCUS) has emerged as a key strategy in reducing CO2 emissions. Although CO2 has

This study explores the integration of bio-inspired adaptive strategies by combining a kinetic façade with electrochromic glazing to optimize daylight performance and glare control. While existing research on kinetic façades typically focuses on the design of individual modules or the overall façade mechanism, the potential of glazing material as an adaptive element is often overlooked. As a result

Leakage-induced erosion in underground engineering has emerged as a critical issue, leading to frequent accidents and posing significant threats to structural stability and safety. While extensive research has explored the seepage behavior of individual structures, the interactions between the defects, particularly following the implementation of preventive or remedial measures, are not fully understood

Shield tunnels are typical underground structures assembled from segmental rings and bolts. The relatively small stiffness of the ring joints compared to segmental rings can easily lead to the discontinuous deformation, and even damage or spalling of the segments, which poses a serious threat to safe tunnel operation. How to consider the discontinuous stiffness and deformation of longitudinal structure

As the importance of urban underground infrastructure in disaster prevention and mitigation becomes increasingly evident, rock tunnels face complex geological and hazard risks. Prestressed support systems are widely regarded as an effective solution to enhance the disaster resilience of shallow-buried urban rock tunnels. However, traditional stress arch theory, which is designed for deeper tunnels

Worker safety has become an increasing concern in human–robot collaboration (HRC) due to potential hazards and risks introduced by robots. Deep Reinforcement Learning (DRL) has demonstrated to be efficient in training robots to acquire complex construction skills. However, neural network policies for collision avoidance lack theoretical safety guarantees and face challenges with out-of-distribution