Robot arm with hot wire cutting is one of the emerging techniques used to carve foam in architectural fabrication. A literature review revealed that there is very little research information related to procedures or calibration techniques. This study aimed to present a pedagogical procedure for using the robotic arm with hot wire cutting. The procedure was tested and demonstrated through various conditions

Construction is an industry that could benefit significantly from automation, yet still relies heavily on manual human labor. Thus, we investigate how a robotic arm can be used to assemble a structure from predefined discrete building blocks autonomously. Since assembling structures is a challenging task that involves complex contact dynamics, we propose to use a combination of reinforcement learning

In recent years, monitoring the health condition of existing bridges has become a common requirement. By providing an information management system, Bridge Information Model (BrIM) can highly improve the efficiency of health inspection and the reliability of condition evaluation. However, the current modeling processes still largely rely on manual work, where the cost outweighs the benefits. The main

The detection of delamination and rebar debonding is among the most frequently encountered issues for inspecting concrete for quality control and assurance. This paper presents a novel application of a 2D full-waveform inversion of ultrasonic SH-waves (SH-FWI) for detection of delamination and rebar debonding in concrete structures. The ultrasonic data are collected by a commercial shear-wave tomography

Current as-built Building Information Model (BIM) deliverables generated following design and construction generally do not fulfill facility management needs. To provide total value to the facility owner, as-built models should be configured to provide needed critical operation and maintenance information, solve operational challenges, and improve safety and efficiency in various ways. In addition

Different surface coatings were applied for graphene/cement-based sensors with self-sensing capacity to achieve an effective way for superhydrophobicity to mitigate piezoresistive instability due to various humidity in this paper. The results show that the cement-based sensors treated by the twice coating can reduce the water absorption by half and exhibit the highest water contact angle of 163.4°

With the development of signal processing and deep learning, vision-based methods for loosening detection have progressed in recent years. However, existing visual inspection methods are mainly used to measure the loosening angle, which requires prior knowledge of the initial tightening states, and is only applicable to hexagon bolts and nuts with clear edges. This paper proposes a method to quantitatively

This research addresses the problem of assembly scheduling in crane-assisted precast construction while considering issues of building layout interference and optimal crane lifting. Traditionally, assembly scheduling and lifting path planning are treated as two separate issues due to their distinct natures. The current work introduces an approach that combines them for precast construction planning

The existence of multiple optimum solutions in project Time-Cost Tradeoff (TCT) optimization has not been seriously researched, while the demand on computing time and resources considering project network models of practical size has not been benchmarked. To a certain degree, this has accounted for (1) the lack of commercial software in the market to enable TCT optimization in project management and

The connection between precast concrete (PC) components is vital during the construction of prefabricated structures. However, PC components are generally inspected and measured manually, which is laborious and error-prone. Therefore, a novel approach was developed for automated recognition and measurement of rebars, concrete, and sleeves of PC components based on three-dimensional point clouds. The

Remote and descriptive visualization of spatio-temporal information of excavator activities may increase awareness about jobsite hazards and operational performance in earthwork operations. One of the emerging approaches to collect this information is to extract the 3D pose of an excavator from the video frames using a convolutional neural network (CNN). However, this method requires labeling the training

Current delay analysis techniques assume that there is only one logical sequence of activities in a project. However, in a real construction project, the relationship of activities can be demonstrated by both fixed and soft logic. Fixed logic requires activities to be executed in a specific order, whereas soft logic allows activities to be executed in various orders. Due to the existence of soft logic

Convolutional Neural Networks (CNN) have immense potential to solve a broad range of computer vision problems. It has achieved encouraging results in numerous applications of engineering, medical, and other research fields due to the advancement in hardware, data collection procedures, and efficient algorithms. These innovations have changed the way how specific problems are solved as compared to conventional

To increase productivity, quality, and safety, heavy industrial construction projects are increasingly adopting prefabrication and modularization techniques. This shift, in turn, has resulted in fabrication shops fabricating more complex assemblies with tighter tolerances. However, most measurement in these shops is conducted using manual hand measurement tools, which can be costly and is known to

Mechanical, electrical and plumbing (MEP) system provides various services and creates comfortable environments to residents in cities. To enhance the management efficiency of the highly complex MEP system, as-built building information modeling (BIM) is increasingly adopted in the world. Currently, as-built BIMs are mostly drawn manually by modelers in BIM modeling software referring to point clouds

Servitization business models can use performance-based contracts to consider overall life-cycle costs rather than just production costs. However, practical implementation of performance contracts has been limited due to challenges with performance evaluation, accountability, and financial concepts. As a solution, this paper proposes the connection of the digital building twin with blockchain-based

Image processing, as an efficient and accurate technology, has been widely applied to characterize granular object morphology in many fields, such as construction engineering, material science, agriculture, etc. Traditional static image processing is not autonomous because it cannot automatically segment contacting particles. In contrast, the current deep-learning-based algorithms can achieve high

This study investigated the structural mechanical properties and failure modes of large-scale three-dimensional (3D) printed concrete walls under axial compression loads. Eight large-scale 3D printed concrete wall specimens with varying ratios of height to thickness were tested under axial compression, including two specimens without horizontal steel reinforcement and six specimens with horizontal

Pavement distress detection is crucial in the decision-making for maintenance planning. Unmanned aerial vehicles (UAVs) are helpful in collecting pavement images. This paper proposes the collection of pavement distress information using a UAV with a high-resolution camera. A UAV platform for pavement image collection was assembled, and the flight settings were studied for optimal image quality. The

Visual Programming Language (VPL) is utilized with increasing frequency in Architectural, Engineering and Construction (AEC) industries to accomplish tasks relating to advanced geometry and complex designs. In recent developments, VPLs have gained traction in robotic research, where they are being explored as a good tool for robotic programming. While visual programming holds several key advantages

In recent years, with the increase of large-scale and complex architecture projects, the architecture, engineering and construction (AEC) industries are changing rapidly towards intelligent and databased direction. As the by-product, 3D modeling event logs could provide quantitative and traceable data for early design stages that can produce butterfly-effect on the later construction stages. In this

“BeaM!” is a production management system that aims at supporting a BIM-integrated application of Lean Construction Management methods on construction sites. In particular, an advanced variant of the Last Planner System is integrated with BIM information on data processing level. This paper transfers the theoretical concepts of the “BeaM!” production management system into functional requirements for

This study presents a novel learning-guided point cloud vectorization to form the vector models of building components. To this end, two learning-based models are modified to realize feature detection and vectorization. The learning-guided scheme can comprehend the definition and mutual relationships of object vertices by learning through existing BIM models and thus predict the vector model of newly

The exploration and conquest of the Moon and Mars is very likely in the near future. Therefore the erection of lunar and Martian structures, suitable for permanent human presence is increasingly discussed by the scientific community. Various concepts have been proposed for creation of lunar and Martian permanent bases. Techniques ranging from 3D printing of concrete, through utilization of natural

This study investigated frequency-difference electrical resistance tomography (ERT) using a 16-channel ERT system to detect rebar embedded in cement mortar. Two different data collecting methods (i.e., reference-difference and frequency-difference) were used for ERT image reconstruction, and the reconstructed ERT images were validated with binarized micro-CT images. As a result, the reference-difference

The time-space conflicts may have severe negative impacts on the productivity and safety level in a construction site. However, little attention is paid to propose an automated, integrated system for managing potential time-space conflicts, considering the site space system's dynamic nature. This study contributes to the body of knowledge by proposing a novel integrated approach to manage time-space

Improving construction workers' safety is one of the most critical issues in the construction industry. Methods have been developed to better identify construction hazards on a jobsite by analyzing workers' physical and physiological responses collected from the wearable devices. Among them, electroencephalogram (EEG) holds unique potential since it shows immediate abnormal responses when a hazard

The paper presents an automated procedure, named META-FORMA (MEchanical-Typological Approach FOR Masonry Aggregates), to investigate the global seismic performance of masonry aggregates according to a simplified approach. The proposed procedure faces the challenge of using few mechanical and geometrical parameters of individual buildings belonging to an existing masonry aggregate as the input of a

Unmanned aerial vehicles (UAV) are increasingly being used in water supply infrastructure inspection, resulting in a large volume of aerial visual assets (static photographs or videos). How to efficiently extract water channels from such assets for the benefits of hazard detection remains a challenge. This paper provides an aerial video processing approach for the extraction of various water channel

Mitigation of seismic risk is a challenge for 70+ countries in the world. Screening the building stock for potential structural defects is one way to locate structures that are vulnerable to strong ground motion. Often in developing countries, masonry buildings are not reinforced or confined to withstand earthquake loads. It has been observed that such buildings cannot withstand the lateral loads imposed

This study aims to solve the problem of material detection and shovel difficulty judgment of earth moving machinery and characterize the material shovel difficulty by using radar chart method comprehensively. YOLOv4 object detection network is used for material detection, and the average detection accuracy reaches 93.03%. We fuse deep learning technology and radar chart method information and explore

This paper presents a framework for segmentation of imaging probe corrosion sensor data using a deep learning algorithm and estimation of the remaining service life of the structure using the segmented data. The sensor consists of a sacrificial metal foil that is imaged using the optical probe and the changes in the images as corrosion develops can be used as a proxy to monitor the condition of the

Cracks undermine the structural health of transportation infrastructure. Machine vision-based surface crack analysis is to process infrastructure inspection data collected by imaging devices for identifying the presence, location, and extent of cracks, classifying the corresponding severity levels, and eventually predicting their growth. Unlike the fragmented qualitative discussions on machine vision-based

Existing automated compliance checking (ACC) systems require the extraction of requirements from regulatory documents into computer-processable representations. These information extraction (IE) processes are either fully manual, semi-automated, or automated. Semi-automated and manual approaches typically use manual annotations or predefined IE rules, which lack sufficient flexibility and scalability;

This paper presents a framework for automated defect inspection of the concrete structures, made up of data collection, defect detection, scene reconstruction, defect assessment and data integration stages. A mobile data collection system, comprising a 360° camera and a digital Light Detection and Ranging (LiDAR), is developed to render high flexibility of data acquisition of image and three-dimensional

This paper aims to demonstrate how knowledge acquired from occupants' manual window operations can be implemented into BMS automated window operation algorithms. Ten single-occupant offices were selected in a university building in the UK. More than 28,000 hourly data points on indoor and outdoor temperature and open window area (OWA) were analysed from 2015 to 2020. The BMS had adopted nine different

The stable and safe operation of Trailing suction hopper dredger (TSHD) is one of the most crucial considerations for ensuring its high dredging productivity. However, the instability and sudden failure of physical sensors pose challenges to the monitoring of dredging process. To address these issues, we propose a structure of digital twin-driven virtual sensor (DTDVS) for the construction safety of

Fatigue of operators due to intensive workloads and long working time is a significant constraint that leads to inefficient crane operations and increased risk of safety issues. It can be potentially prevented through early warnings of fatigue for further appropriate work shift arrangements. Many deep neural networks have recently been developed for the fatigue detection of vehicle drivers through

With ongoing advancements in information and communication technologies (ICTs) in all stages of the construction lifecycle, information from entities related to construction workflow (CW) can now be automatically collected. These implementations are point solutions, which require systematic integration to combine their information to enable a holistic picture of CW. The major barrier to such integration

Artificial intelligence is reshaping building design processes to be smarter and automated. Considering the increasingly wide application of shear wall systems in high-rise buildings and envisioning the massive benefit of automated structural design, this paper proposes a generative adversarial network (GAN)-based shear wall design method, which learns from existing shear wall design documents and

To transfer heavy payloads at construction sites, tower cranes are essential. However, tower cranes are a complex underactuated system due to the strong coupling between the jib, trolley and payload. In addition, the rope length is usually time-varying in order to accomplish the transportation task in a wide three-dimensional space, which results in more complex dynamic characteristics. Hence, through

The contribution of distributed ledger technology (DLT) (e.g. blockchain) and smart contracts to the digitalisation and digital transformation of the construction sector is nascent but rapidly gaining traction. ‘Systematic reviews’ of DLT and smart contract applications that are specific to the construction sector are missing. This paper performs an extensive systematic review of 153 DLT and smart

The inclusion of sensors in railway track components will permit the automated and real-time monitoring of track behaviour and traffic conditions; necessary for adopting preventive maintenance strategies. In this sense, this paper assesses the applicability of three different sensor types, commonly used for engineering applications, to be included into rail pads to thus provide smart components with

Slurry pressure balance shield, a kind of tunnel boring machine, is significantly affected by the operation parameters such as advance speed and torque. In this study, a dynamic regulation model is established based on wavelet transform and bidirectional long short-term memory method (Bi-LSTM) to predict advance speed and torque. For comparison, twenty parameters of the shield machine are input to

Operation safety and efficiency of high-speed trains are prone to strong winds. This study proposes a wind hazard warning system to predict wind speed and provide warning for safe and efficient operation of trains, and shows the implementation to the Lanzhou-Xinjiang High-Speed Railway. The system is composed of monitoring, remote and central processing, and execution modules. The system uses field

Eye tracking glance analyses have been used to study the behavior of motor vehicle drivers for at least two decades and eye tracking is now emerging as an experimental protocol in construction research. Previous studies have identified relationships between driver glance behavior and performance, so existing motor vehicle driver glance analysis methods were applied to search for analogous relationships

Many municipalities and road authorities seek to implement automated evaluation of road damage. However, they often lack technology, know-how, and funds to afford state-of-the-art equipment for data collection and analysis of road damages. Although some countries, like Japan, have developed less expensive and readily available Smartphone-based methods for automatic road condition monitoring, other

Visual damage inspection of steel frames by eyes alone is time-consuming and cumbersome; therefore, it produces inconsistent results. Existing computer vision-based methods for inspecting civil structures using deep learning algorithms have not reached full maturity in exactly locating the damage. This paper presents a deep convolutional neural network-based damage locating (DCNN-DL) method that classifies

This research provides a spatio-temporal approach to perform real-time forecasting for the tunnel boring machine (TBM) operating parameters. By extracting the real-time TBM operational data from the data acquisition system, a Long Short-Term Memory (LSTM) based deep learning model is trained for accurate prediction. A global sensitivity analysis (GSA) by adopting the Sobol method is performed for the