Construction Inspection Research Articles

Estimation of random incidence sound transmission loss of panels based on normal incidence sound transmission loss measurements

The performance of building sound insulation is an important parameter for evaluating the quality of construction projects. So far, the sound insulation performance testing of building walls requires the installation of large-dimensioned wall samples in specialized sound insulation laboratories, which is time-consuming and costly. If small-dimensioned components of the wall panels can be applied and quickly tested at the project site, it could significantly improve efficiency and reduce costs. To address this issue, this paper investigates a method for estimating the random incidence sound transmission loss of wall panels based on the measurement of the normal incidence sound transmission loss of corresponding small samples. Initially, the sound transmission loss of single-layer and composite structures is theoretically analyzed based on the theories of Equivalent Single Layer method and Layer-Wise method. Subsequently, the random incidence sound transmission loss is estimated by the normal incidence sound transmission loss. The methodology for estimating the random incidence sound transmission loss of wall panels proposed in this study offers both a practical approach and theoretical support for the rapid assessment of the sound insulation performance of building walls. This study is of great significance for improving the quality of construction projects and inspection efficiency.

Depth-informed point cloud-to-BIM registration for construction inspection using augmented reality

Augmented reality (AR) is increasingly being used to assist construction inspection onsite. Underpinning this AR-assisted inspection is a technique called registration, which aims to align the physical world with a digital building information model (BIM) so that the as-built can be intuitively compared with the as-designed. Despite its importance, how to precisely and efficiently register BIM to the physical world still remains a challenge. This paper contributes to tackling the challenge by proposing a novel depth-informed point cloud-to-BIM registration (D-PC2BIM) algorithm. The idea is to enhance registration performance by estimating the depth of a sparse point cloud to inform interpolation of the missing points and to extract the endpoints that matter most in a registration. A novel integration algorithm is proposed to improve the success rate of final registration. Experiments demonstrate the effectiveness of the proposed algorithm, which outperformed existing approaches with higher accuracy and faster speed. The contribution of the study resides in the development of the D-PC2BIM algorithm and a demonstration of its applicability in enabling construction inspection using AR.

Development of Risk-Based Methodologies for Highway Pavement Construction Inspection

Construction inspection plays a critical role in Portland cement concrete pavement (PCCP) and hot mix asphalt (HMA) pavement projects. To ensure the quality of pavement projects, state departments of transportation (DOTs) in the U.S. typically conduct construction material tests and inspect workmanship processes using quality assurance (QA) programs. Given today’s reality of reduced QA inspection resources such as funding, staff ability, and availability, several state DOTs have used a risk-based inspection (RBI) approach to optimize their QA processes. A thorough review of existing literature revealed limited studies examining RBI methodologies in highway pavement construction projects. The aim of this study is to investigate the use of RBI for pavement construction projects. It employed a triangulation research methodology, including an extensive literature review, a survey questionnaire of 50 state DOTs, focus groups, and a vetting workshop. A list of 16 core items in field inspection for both PCCP and HMA pavement projects was developed and verified. A generic risk rating of these critical inspection activities was determined. Additionally, inspection frequency, documentation effort, and inspector experience associated with these critical inspection activities identified through focus groups and the vetting workshop served as a reference point when conducting RBI for PCCP and HMA pavement projects. This study contributes to the body of knowledge and the highway construction industry by identifying and prioritizing 16 core inspection items for PCCP and HMA pavement construction. Construction project managers and inspectors may benefit from this study by analyzing the risks of core inspection items and implementing appropriate risk mitigation plans.

Condition assessment of concrete structures using automated crack detection method for different concrete surface types based on image processing

In the inspection and diagnosis of concrete construction, crack detection is highly recommended in the earliest phases to prevent any potential risks later. However, the flaws in concrete surfaces cannot be reliably and effectively identified using traditional crack detection techniques. The suggested algorithm is a supportive tool for agents or authorities to use in crack detection mechanisms to monitor and assess the current condition of buildings or bridges. The researchers aim to establish an intelligent model for automatic crack detection on different concrete surfaces based on image processing technology. Three different concrete surfaces—bridge decks, walls, and concrete cubes—are used to test the model. A subset of the public dataset of bridge decks and walls from SDNET (2018) and 150*150*150 mm of concrete cubes taken from the material laboratory of the faculty of engineering at Ain Shams University are applied to the model. The model F1-score measures are 98.87%, 97.43%, and 74.11% for detecting cracks in bridges, walls, and concrete cubes, respectively. The validation of the applicability of the suggested novel approach is based on a comparison with recent methods for crack recognition. The contribution of this study is that it could be applied to detect cracks efficiently on three different types of concrete surfaces, including uneven concrete surfaces, random noise, voids, dents, colour changes, and stain marks. The proposed method is transparent in its workflow and has a lower computational cost compared with deep learning frameworks. Thus, the outcomes of this model demonstrate its effectiveness in concrete defect field investigation.

DESIGN TASK IN THE MODERN CONDITIONS OF INVESTMENT AND CONSTRUCTION PROJECTS

The article is devoted to the organizational aspect of solving the urgent problem of shortening the terms of implementation of investment and construction projects in modern conditions at the stage design The article examines the procedure for drawing up a design task by the customer at the stage of preparing tender documentation for construction objects in Ukraine, the financing of which is carried out with the involvement of budget funds, funds of state and communal enterprises, institutions and organizations, loans provided under state guarantees and defined and disadvantages The provisions of regulatory and technical documents regulating the procedure for drawing up a design assignment and its content have been reviewed. The list of problems that arise at the design and construction stage during the implementation of investment and construction projects due to the uncertainty and variability of data for project development, provided by the customer in the design task, is defined. The analysis of open sources of information regarding the procurement of design works showed that customers fill out the design task form, defined by the regulatory framework, on a formal basis, without actually expressing their real requirements for the future construction inspection. In "design and build" projects, the design task occupies a special place, since the contractor, on the basis of the customer's technical task, can determine not only the cost of the design work, but also the price of the construction work for which he is willing to perform it, while this price depends on technical and economic parameters of the building, which has not yet been designed, from the applied finishing materials, engineering systems, structural solutions, etc. An analysis of completed contracts for the purchase of construction works on "design and build" schemes was carried out, and they were divided into groups depending on the sufficiency of information in the design task. The foreign experience of organizing design works was analyzed, in particular, when implementing the "design and build" approach. It is proposed to improve the organization of the design process by separating the process of drawing up the design task into a separate procedure with a toolkit based on digital technologies, formulating the customer's requirements for design solutions and checking the validity of the customer's requirements. The feasibility of developing a toolkit based on digital technologies, which can be applied by the customer for drawing up a design task in investment and construction projects.

Mixed Reality in Building Construction Inspection and Monitoring: A Systematic Review

Mixed reality (MR) technology has the potential to enhance building construction inspection and monitoring processes, improving efficiency, accuracy, and safety. This systematic review intends to investigate the present research status on MR in building construction inspection and monitoring. The review covers existing literature and practical case studies that scrutinize current technologies, their applications, challenges, and future trends in this rapidly evolving field. This article follows a methodology known as Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) to enhance the credibility and reliability of research. The study includes articles published between 2018 and 2023, identified through a comprehensive search of Scopus and Google Scholar databases. Findings indicate that MR technology has the potential to enhance visualization, communication, and collaboration between stakeholders, as well as increase efficiency and accuracy in inspection and monitoring tasks by providing real-time interactable data and quick decision-making among the project team members. The adoption of MR technology in the construction industry will not only boost its effectiveness but also improve its productivity. However, limitations such as high costs, technical issues, and user acceptance pose challenges to the widespread adoption of MR in building construction. Future research should address these limitations and investigate MR’s long-term impact on building construction inspection and monitoring.

Application of Drone Technology in Construction Industry

Abstract: There are various fields in which Drone Technology is becoming significant. Surveying and Mapping, Construction Industry, Inspection and Surveillance, Military and Agriculture are the various areas where drone technology playing an important role of reducing time for this tedious work. Drone technology is the most effective among those technologies which helps in efficient project management by addressing the challenges in a construction project like surveying, monitoring activities, safety of labours, quality and cost control and getting timely on-site progress reports.

AutoRepo: A general framework for multimodal LLM-based automated construction reporting

Ensuring the safety, quality, and timely completion of construction projects is critical, and construction inspections play an important role in achieving these objectives. Nevertheless, the predominantly manual approach to inspections frequently results in inefficiencies and inadequate information management. These methods often fail to provide a comprehensive and detailed assessment, resulting in regulatory oversight and potential safety hazards. To address this issue, this paper presents a novel framework named AutoRepo for automated generation of construction inspection reports. In AutoRepo, the unmanned vehicles efficiently perform construction inspections and collect scene information, while the multimodal large language model (LLM) is used to automatically generate the inspection reports. The framework was applied and tested on a real-world construction site, demonstrating its potential to speed up the inspection process, significantly reduce waste of resource, and generate high-quality inspection reports that meet regulatory standards. Thus, this research highlights the great potential of multimodal LLMs in revolutionizing construction inspection practices, signaling a significant leap forward towards a more efficient and safer construction management paradigm.

A Comprehensive Review on Water Permeability Testing and Sealing Techniques in Concrete Construction

The durability of concrete is determined by the permeability resistance to a large extent. The permeability resistance of concrete is an important index of concrete durability. This paper introduces the water permeability test process of concrete in China, Europe and the United States, and explains the sealing method of specimens in the water permeability test of concrete according to the Chinese standard, so that the construction inspection industry personnel can choose according to the actual situation of the project and the evaluation needs.

A Systematic Review of Automated Construction Inspection and Progress Monitoring (ACIPM): Applications, Challenges, and Future Directions

Despite the subjective and error-prone nature of manual visual inspection procedures, this type of inspection is still a common process in most construction projects. However, Automated Construction Inspection and Progress Monitoring (ACIPM) has the potential to improve inspection processes. The objective of this paper is to examine the applications, challenges, and future directions of ACIPM in a systematic review. It explores various application areas of ACIPM in two domains of (a) transportation construction inspection, and (b) building construction inspection. The review identifies key ACIPM tools and techniques including Laser Scanning (LS), Uncrewed Aerial Systems (UAS), Robots, Radio Frequency Identification (RFID), Augmented Reality (AR), Virtual Reality (VR), Computer Vision (CV), Deep Learning, and Building Information Modeling (BIM). It also explores the challenges in implementing ACIPM, including limited generalization, data quality and validity, data integration, and real-time considerations. Studying legal implications and ethical and social impacts are among the future directions in ACIPM that are pinpointed in this paper. As the main contribution, this paper provides a comprehensive understanding of ACIPM for academic researchers and industry professionals.

Integration of Augmented Reality and Building Information Modeling for Enhanced Construction Inspection—A Case Study

This research addresses a significant challenge in the construction industry: the traditional reliance on CAD drawings and manual methods for building inspection and monitoring. This article presents a transformative proposal utilizing Augmented Reality (AR) to revolutionize these processes. The proposed model is based on an innovative integration of AR technology with Building Information Modeling (BIM), aiming to enhance data life-cycle management and improve the efficiency of construction management practices. The main goal of the article is to demonstrate the practical feasibility and benefits of this AR-BIM integration in construction management, particularly in the realms of inspection and monitoring. This involves developing and testing an accessible, user-friendly, and affordable AR application prototype for mobile devices, employing multiple markers, as a potential replacement for traditional methods. The research methodology comprises five phases, starting with the conversion of 2D CAD drawings into 3D BIM, followed by the simulation of AR-BIM integration at a construction site using a commercial AR application. This is succeeded by assessing the applicability of the commercial AR app and developing a multiple-marker AR application prototype suitable for general platforms. The final phase encompasses the testing and evaluation of this prototype. The findings suggest that the integration of 3D BIM with AR technology is not only feasible, but also beneficial in replacing paper-based processes, thereby enhancing information sharing, communication, and overall project execution efficiency. However, the accuracy of the superimposition between virtual and actual objects needs improvement to reduce discrepancies between as-built and as-planned scenarios. These results hold significant potential for transforming construction project execution by replacing traditional methods with more efficient digital solutions. Future research directions include extensive testing in various construction scenarios, improving model complexity handling, exploring the application of machine learning algorithms for data analysis, and expanding the study to encompass other stages of the construction lifecycle.

Construction and ultrasonic inspection of the high-capacity Li-ion battery based on the MnO2 decorated by Au nanoparticles anode

Lithium (Li)-ion batteries have become one of the main energy sources for electric vehicles and energy storage systems, which puts forward higher requirements for the detection of battery state of health (SOH). The SOH of batteries is crucial for areas such as battery management and renewable energy storage. Accurately evaluating the SOH of batteries can optimize charging and discharging strategies and extend battery life. Therefore, accurately and effectively monitoring the SOH of Li batteries is of great significance. An ultrasonic testing technology has been proposed that can non-destructively test the Li battery SOH, enabling accurate judgment of batteries in poor or damaged conditions. Firstly, the hetero-structured MnO2-Au has been constructed as the anode for Li-ion batteries. MnO2-Au heterojunction enhances electronic conductivity and ion conductivity. The MnO2-Au has exhibited high specific capacity and superior rate performances, which can well satisfy the ultrasonic inspection of the battery. Then, the ultrasonic testing has been conducted on batteries with different ages. The results suggest that batteries with short circuits have the highest nonlinear coefficient, while batteries with short circuits after long cycles have the lowest nonlinear coefficient. The nonlinear coefficient of batteries with different charging and discharging states is in the middle.

Application of Unmanned Aerial Vehicles in Construction Industry

Technological advances in the field of electronics, such as miniature electromechanical devices and small powerful electric motors, have made it possible to develop small and light devices, such as unmanned aerial vehicles (UAVs). Recently, civilian UAVs are rapidly gaining popularity. Undoubtedly, UAVs will be used for many services in the future. There is already a growing demand for such fields of application of unmanned aerial vehicles as agriculture, emergency services, energy, fuel, mining, construction, geodesy (cartography), transportation, etc. Thanks to modern technologies it possible to produce light and low-power but accurate sensors that can be used by controllers with high computing power and low energy consumption. This makes it possible to develop complex control systems for UAVs that can be implemented on board. Today’s quadcopters are used for design, surveillance, search, construction inspections, and a variety of other applications.

AN EXTENSIVE INSPECTION OF THE FORKLIFT FB25-77 IN THE MANUFACTURING SECTOR BASED ON INDONESIAN MINISTRY OF LABOUR REGULATION

Workplace accidents can arise from various factors such as human elements, environmental conditions, equipment, machinery, work processes, the nature of tasks, and work methodologies. Forklifts which is classified under lifting and transporting equipment, are frequently utilized in industrial settings for material movement. To mitigate the risk of work-related accidents and ensure the safety and suitability of a forklift for operational use, a systematic inspection and testing process are essential. Typically conducted at regular intervals, every two years, and annually for regular use, this process assesses various aspects of lift and transport equipment, including forklifts. This study aims to evaluate the conformity of the examination results for a FB25-77 type forklift with the standards outlined in Minister of Manpower Regulation No. 8 of 2020. Employing a descriptive analysis method, the study involves comparing the actual condition of forklifts in the field against all the specified requirements in Permenaker No. 8 of 2020, encompassing visual inspection of construction, dimensional measurements, non-destructive testing (NDT), functional testing, and load testing. Based on the comprehensive inspection and testing procedures, it was ascertained that the equipment meets the Occupational Health and Safety (OHS) requirements, thereby warranting the issuance of a certificate by the Provincial Manpower Service.

A paradigm shift toward the application of blockchain in enhancing quality information management

PurposeLack of trust and poor quality of construction deliverables have become a serious matter nowadays. This is due to the absence of a uniform and decentralized system for managing quality information. In Kuwait’s industry, many incidents have been recorded as a lack of confidence in the authenticity and integrity of the documented data in the system. This paper aims to shed the light on a framework that would tackle this matter.Design/methodology/approachA designed framework using Blockchain technology (Hyperledger Fabric) has been used to create a transparent and decentralized environment between the parties. A digitalized informative checklist referred to as “Smart Construction Inspection Checklist (SCIC)” has been initiated to enhance the poor information recorded between the parties.FindingsThe framework has provided a transparent, immutable, traceable and decentralized environment in which all parties are involved in transactions. In addition, the integration of the SCIC in the blockchain environment provided an advantage in which all the necessary criteria of inspection will be stated, checked by the consultant and validated by the client to approve the transaction. A preliminary testing has been conducted to support the proposed framework.Originality/valueThis study fulfils the gap in the state of art for further studies to practically apply the framework that will enhance the quality of information management in Kuwait’s industry.

Investigating the Impact of Augmented Reality and BIM on Retrofitting Training for Non-experts.

Augmented Reality (AR) tools have shown significant potential in providing on-site visualization of Building Information Modeling (BIM) data and models for supporting construction evaluation, inspection, and guidance. Retrofitting existing buildings, however, remains a challenging task requiring more innovative solutions to successfully integrate AR and BIM. This study aims to investigate the impact of AR+BIM technology on the retrofitting training process and assess the potential for future on-site usage. We conducted a study with 64 non-expert participants, who were asked to perform a common retrofitting procedure of an electrical outlet installation using either an AR+BIM system or a standard printed blueprint documentation set. Our findings indicate that AR+BIM reduced task time significantly and improved performance consistency across participants, while also decreasing the physical and cognitive demands of the training. This study provides a foundation for augmenting future retrofitting construction research that can extend the use of AR+BIM technology, thus facilitating more efficient retrofitting of existing buildings. A video presentation of this article and all supplemental materials are available at https://github.com/DesignLabUCF/SENSEable/_RetrofittingTraining.

Transfer Learning-Based Object Detection Model for Steel Structure Bolt Fastening Inspection

As improper inspection of construction works can cause an increase in project costs and a decrease in project quality, construction inspection is considered a critical factor for project success. While traditional inspection tasks are still mainly labor-intensive and time-consuming, computer vision has the potential to revolutionize the construction inspection process by providing more efficient and effective ways to monitor the progress and quality of construction projects. However, previous studies have also indicated that the performance of vision-based site monitoring heavily relies on the volume of training data. To address the issues of challenging data collection at construction sites, this study developed models using transfer learning-based object detection models incorporating data augmentation and transfer learning. The performance of three object detection algorithms was compared based on average precision and inference time for detecting T/S bolt fastening of steel structure. Despite the limited training data available, the model’s performance was improved through data augmentation and transfer learning. The proposed inspection model can increase the efficiency of quality control works for building construction projects and the safety of inspectors.

A new method for evaluating the uniformity of steel slag distribution in steel slag asphalt mixture based on deep learning

A new evaluation method has been developed to assess the uniformity of steel slag asphalt mixture. The original aggregate in the method is replaced by steel slag in the range of 4.75 mm to 9.5 mm. Deep learning techniques are used in this method to select the optimal network as the segmentation network of the framework by comparing DeepLabV3+, U-Net, and PSPNet networks. Pixel level watershed algorithm is used to solve the steel slag adhesion problem for predicted images. Finally, the horizontal distribution coefficient UI and vertical distribution coefficient Pv were used to evaluate the uniformity of steel slag distribution. The results show that the U-Net network performs the best in identifying and segmenting steel slag, the training is stable without overfitting problem and the accuracy of training reaches 98.62 %, the true positive is the highist and the average accuracy is 96.98 %, UI and Pv can effectively reflect the distribution uniformity of steel slag. The end-to-end system provides a more convenient and intelligent way to evaluate the distribution uniformity of steel slag. The method can be used to quickly and accurately analyze the dispersion uniformity of aggregates in asphalt mixtures and to help in the construction inspection of road projects.

Automated Pavement Construction Inspection Using Uncrewed Aerial Systems (UAS)—Hot Mixed Asphalt (HMA) Temperature Segregation

Temperature segregation in Hot Mixed Asphalt (HMA) pavement construction leads to performance problems, such as reduced fatigue life. During construction, Quality Assurance (QA) inspection procedures are required to evaluate the pavement condition and detect the segregated areas. In traditional HMA highway construction inspection processes, temperature differences are investigated manually, by sampling the HMA behind the paver. In these processes, inspectors are required to work adjacent to traffic and alongside moving or backing equipment. These processes do not provide a complete temperature profile of the mat, endanger the inspectors’ safety, and require on-site experienced inspectors. An Uncrewed Aerial System (UAS) enables HMA pavement construction inspection to be conducted within a remote, non-destructive, safe, and efficient framework. The objective of this research is to design an automated UAS imaging workflow for HMA pavement construction inspection, mainly locating temperature segregation. The primary contribution of this paper is to provide Departments of Transportations (DOTs) and contractors with workflows for creating enhanced remote inspection procedures and detailed thermal profiles of the placed HMA mat. The application of the proposed workflow is illustrated using an HMA construction project in Michigan.

PHM-IRNET: Self-training thermal segmentation approach for thermographic inspection of industrial components

Automating thermographic analysis for industrial and construction inspection is one of the essential aspects of NDT 4.0 in industrial transformation. Thermal segmentation is a preliminary step for characterizing a component’s heat signatures by detecting and extracting thermal patterns. Manual segmentation of areas with distinctive thermal patterns is a time-consuming task that consequently can increase the operation cost. Thus, automatic thermal segmentation can significantly assist companies in avoiding possible misinterpretation and decrease the operation cost. Due to the lack of enough labeled thermal images to train deep neural networks and the fact that, by nature, thermal abnormalities may have non-uniform patterns and form in different shapes and sizes, training a deep neural network can be a challenging task. Moreover, selecting common features can cause possible convergence onto dependency toward specific shapes or patterns. Thus, this paper proposes a self-training thermal segmentation method involving a novel process pipeline for using labeled and unlabeled data for training the presented deep-learning model. The method uses an unsupervised approach for predicting the ground truth in case of unlabeled data before passing the data to the secondary model for segmentation. For this paper, three experiments were conducted to evaluate the method’s performance and provide a comparative analysis with the most recent techniques. The first experiment used a simulated inspection created by COMSOL software. The second experiment employed several plate inspections in several isolated environments. Finally, the third experiment used thermal images acquired during an actual inspection of the piping structure and aerospace components.