Construction Automation Research Articles

Precast concrete project image dataset for deep learning object detection

Computer vision technology in precast concrete (PC) projects has the potential to enhance site management but faces challenges due to the lack of specialized datasets for this field. This study developed a PC components dataset for object detection (PCCODA) in four steps: component range selection, image collection, image preprocessing, and labeling. The performance of PCCODA was assessed using multiple algorithms, including You Only Look Once (YOLO), YOLO X, faster region-based convolutional neural networks (Faster R-CNN), and Double Head R-CNN. The dataset resulted in average accuracies of 0.88–0.97, average robustness of 0.84–0.96, and average detection speed of 3.64–23.3 frames per second. This performance fulfills the level required by most studies on construction automation. The applicability of the dataset was tested at a real site. This study contributes to the off-site construction project management theory and enhances productivity in PC projects by automating object detection-related tasks.

Rapid and restricted swing control via adaptive output feedback for 5-DOF tower crane systems

Crane swing & relevant vibration are critical safety and efficiency issues to be addressed in construction automation, involving various disturbance and uncertainties. This paper presents for the first time a controller designed for 5-DOF tower crane systems with no velocity signals, and its effectiveness is experimentally verified. Compared to previous controllers, the new design utilizes an adaptive output feedback control method with accurate online gravity compensation, which solves the problems of unavailability velocity, noise amplification by numerical difference, and steady state error of cable in tower crane control. Additionally, the saturated control inputs are guaranteed by employing bounded functions. The results of this study unveil that the controller proposed in this paper can simultaneously achieve accurate trolley/jib/cable positioning, rapid payload swing suppression and elimination, as well as precise payload mass estimation, and guarantee control inputs constraints. This study provides a new approach for accurate control of tower crane systems.

ORGANIZATIONAL AND TECHNOLOGICAL DESIGN OF AUTOMATION SYSTEMS

Formulation of the problem. Automation in construction shows the potential to improve productivity after years of technical development and experimentation in the field. However, current research does not provide a clear understanding of exactly how this happens, as well as what contradictions and problems accompany construction automation. At the moment, the practice of using construction robotic equipment is not widespread in Ukraine, mainly due to the high costs of equipment compared to the wages of workers. However, as evidenced by European and American experience, the introduction of modern automated technologies into the construction process leads to increased productivity, improved accuracy and quality of work, and increased safety in construction. Despite the high cost of equipment and additional costs for training specialists and service personnel, as well as the limitations of its application, the use of construction robotics can have positive results, such as accelerating the pace of work, which will lead to large-scale changes in organizational and technological solutions and organizational and technological design in general. The purpose of the article is to analyze the use of modern automated systems and identify technical innovations and their practical potential. Conclusions. In order to gain a deeper understanding of the full potential of construction automation to increase productivity and the associated possible consequences, an objective review of information on automated technologies in construction was conducted. This review was carried out using text analysis techniques in publicly available written documents covering a wide range of relevant information sources, including scientific publications and official databases of equipment suppliers. The main vectors of worldwide research and existing technologies tested in practice are defined. The purpose, objects, tasks and goals of future research are defined.

Towards full automation in 3D concrete printing construction: Development of an automated and inline sensor-printer integrated instrument for in-situ assessment of structural build-up and quality of concrete

The realization of full automation in 3D Concrete Printing (3DCP) encounters numerous obstacles, notably the absence of automated and inline testing methodologies to measure the fresh properties at the construction site. To address this challenge, this study presents an automated sensor-printer integrated instrument with multifunctional capabilities of conducting penetration, compression, shear, and tensile tests in inline mode. The developed system is integrated with a 3D printer and controlled from the main station. To validate the instrument performance, it was used for inline penetromtery testing to measure the structural build-up of printed layers, and its results were correlated with the conventional compressive and shear strength tests. Additionally, the responsiveness of the instrument to the material variation was validated. This research represents a substantial advancement for in-situ characterization of structural build-up and quality control, while also showcasing its potential utility in mix design development.

RoBétArmé Project: Human-robot collaborative construction system for shotcrete digitization and automation through advanced perception, cognition, mobility and additive manufacturing skills.

The importance of construction automation has grown worldwide, aiming to deliver new machineries for the automation of roads, tunnels, bridges, buildings and earth-work construction. This need is mainly driven by (i) the shortage and rising costs of skilled workers, (ii) the tremendous increased needs for new infrastructures to serve the daily activities and (iii) the immense demand for maintenance of ageing infrastructure. Shotcrete (sprayed concrete) is increasingly becoming popular technology among contractors and builders, as its application is extremely economical and flexible as the growth in construction repairs in developed countries demand excessive automation of concrete placement. Even if shotcrete technology is heavily mechanized, the actual application is still performed manually at a large extend. RoBétArméEuropean project targets the Construction 4.0 transformation of the construction with shotcrete with the adoption of breakthrough technologies such as sensors, augmented reality systems, high-performance computing, additive manufacturing, advanced materials, autonomous robots and simulation systems, technologies that have already been studied and applied so far in Industry 4.0. The paper at hand showcases the development of a novel robotic system with advanced perception, cognition and digitization capabilities for the automation of all phases of shotcrete application. In particular, the challenges and barriers in shotcrete automation are presented and the RoBétArmésuggested solutions are outlined. We introduce a basic conceptual architecture of the system to be developed and we demonstrate the four application scenarios on which the system is designated to operate.

Role of Robotics and Automation in Construction Sector

Construction is one of the major industries in the world. The construction industry is labor-intensive and performed in hazardous situations. Hence, the importance of Construction robots has developed rapidly. The research discovers the latest advances in robotics and sensor-based technologies that are leading developments in the construction sector to attain improvements in construction quality, efficiency, and safety. Although automation in construction was first introduced in the year 1980. It has seen little advancement in the level of application. The present study attempts to identify the recent developments in automated robotic systems and their scope in the construction sectors, namely with respect to construction robots. The application of robotic technologies in the construction industry has been known as one of the most challenging tasks. Automation in the construction industry can help to ensure production without compromising the quality of the construction products. This may be due to the fact that workers can become tired over time.

CPBA-CLIM: An entity-relation extraction model for ontology-based knowledge graph construction in hazardous chemical incident management.

In recent years, hazardous chemical incidents have occurred frequently, resulting in significant human casualties, property damage, and environmental pollution due to human or natural factors. Accurately mining the lessons learned from accumulating incident reports and constructing the knowledge graph for hazardous chemical incident management can assist managers in identifying patterns and analyzing common attributes, thereby preventing the recurrence of similar incidents. This article addresses the challenges of dispersed textual information, specialized vocabulary, and data formats in hazardous chemical incidents. We propose a novel entity-relation extraction model called CPBA-CLIM (content-position-based attention-cross-label intersect matching) to provide an accurate data foundation for constructing the hazardous chemical incident knowledge graph. The content-position-based attention module, based on content-position attention, incorporates contextual semantic information into the combined encoding of bidirectional encoder representations from the transformer's content and position to obtain dynamic word vectors that align with the thematic context of the text. Additionally, the cross-label intersect matching strategy evaluates the rationality of entity-relation interactions in sets containing potential overlaps, reducing the impact of entity-relation overlap on triplet extraction accuracy. Comparative experimental results on public datasets demonstrate the model's outstanding performance in overlapping triplets. Qualitative experiments on a self-constructed dataset integrate our model with ontology construction techniques, successfully establishing a knowledge graph for managing hazardous chemical incidents. This research effectively enhances the degree of automation and efficiency in knowledge graph construction, thus offering support and decision-making foundations for hazardous chemical safety management.

A Review on Barriers in Implementation of Robotics Technology in the Construction Projects

One of the oldest and most important sectors of a country's economy is the construction sector. Through infrastructure projects, amenities, and real estate development, the Indian construction industry contributes significantly to the country's economic growth and nation-building. India is trailing behind the west in the application of robotics and automation in construction, while the west is seeing a rise in this field. An examination of the particular reasons limiting adoption in Indian building projects is presented in this research. The objective of the review is to identify and categorise the most critical challenges that are limiting the adoption of robotics in construction projects. The information provided will help those working in construction come up with mitigation plans. The results show that, while there is awareness of the benefits of adaptability and the use of robots in construction, there are a number of resistance factors that must be overcome for robotics to be successfully adopted in India. Generally speaking, professionals in the construction industry consider automation and robotics to be synonymous, and they believe that robotics is appropriate for a developing nation like India

Enhanced adaptive sequential Monte Carlo method for Bayesian model class selection by quantifying data fit and information gain

For complex engineering structures, it is important to systematically select a model class among various choices and search the complicated parameter space, while also quantifying uncertainties. This paper develops an enhanced adaptive sequential Monte Carlo (ASMC) method to solve Bayesian model updating and model class selection in a unified manner. Main contributions are: (1) Analysis of the sequential sampling process reveals difficulties of sampling from complex probability density functions (PDFs), which naturally leads to the PDF approximation using incremental weights of samples, and then the adaptive sampling scheme with this approximation. (2) Following ASMC framework, new formulations are derived to calculate model class evidence; these formulations enable the separate quantification of data fit and information gain of a model class. A simulated and a full-scale structure were used to demonstrate the proposed method. This work lays good foundation for downstream research such as automation in construction and structural health monitoring.

Data-driven joint multi-objective prediction and optimization for advanced control during tunnel construction

This research develops a hybrid approach that integrates light gradient boosting machine (LightGBM) and non-dominated sorting genetic algorithm II (NSGA-II) to optimize the tunnel boring machine (TBM) performance during excavation. The TBM operational data are first extracted and meta-models are established to estimate the key TBM performance, including the penetration rate, over/under excavation ratio, and energy consumption. An optimization process is proposed by adopting NSGA-II and the technique for order preference by similarity to an ideal solution (TOPSIS) analysis to determine ideal operational parameters. The developed approach acts as a useful tool that assists tunnel construction automation and improves TBM performance under different in-situ conditions. Real data from a tunnel project in Singapore is utilized as a case study to examine the applicability and efficiency of the proposed approach. The results indicate that (1) The proposed meta-model provides reliable estimation with an average RMSE and MAE of 2.604mm/min and 3.402mm/min for TBM’s penetration rate(O1), 0.0211 and 0.0324 for over/under excavation ratio (O2), and 15.512kwh and 23.088kwh for energy consumption (O3), respectively. The prediction accuracy is better than the state-of-the-art methods; (2) The TBM’s performance can be optimized by the proposed approach with an average improvement of 33.14 %, 1.32 %, and 17.95 % for O1 to O3, respectively, and an overall improvement of 39.60 %; (3) The overall reliability of TBM operation improved after optimization with a significant reduction in data variance by 91.16 %, 76.92 %, and 97.35 % for O1 to O3, respectively. This paper contributes to proposing a novel method that integrates LightGBM with NSGA-II in resolving the complex TBM operation problem by considering the major performance indexes including excavation efficiency, safety, and energy consumption during TBM operation.

Identifying Emerging Technologies and Skills Required for Construction 4.0

Connectivity, digitization, and emerging Industry 4.0 technology such as AI, automation, robotics, IoT, and cyber-physical space have transformed social relations, the means of production, and the world of labor. The construction industry has also been transformed by Industry 4.0 technologies, mainly through Building Information Modeling (BIM)-based digitization. This revolution in the construction industry has been called Construction 4.0 and is based on the use of innovative technologies that promote digitalization and automation of design, construction, and management processes. Furthermore, the workforce for the construction industry in the context of Construction 4.0 must have adequate skills for this new scenario. Previous literature reviews have analyzed the idea of transitioning the construction industry to Construction 4.0 and implementing specific technologies in the industry. However, there has been limited exploration of the skills necessary for the Construction 4.0 workforce. This article aims to address this gap by identifying the technologies and skills that have been scientifically researched and applied in the construction industry, specifically related to the concepts of Industry 4.0 and Construction 4.0. Utilizing a scoping literature review in the Scopus database, this study seeks to identify: (i) emergent C4.0 technologies in the AEC industry; and (ii) which skills or competencies are demanded when using these technologies. All of our bibliographical searches are inserted and restricted to the concept of Construction 4.0. A total of 108 articles related to technologies and 15 related to skills in the Construction 4.0 context were selected for analysis. Twenty-one technologies were identified and grouped into five sets according to their similarities and applications: AI-assisted technologies, Advanced manufacture, Smart tools, Digital simulation/Visualization, and Data acquisition/detection. The skills were identified and grouped into soft and hard skills. However, the findings point out that the new skills needed by the workforce are described in a dispersed way, without a central theme of research in the context of Construction 4.0. Our findings contribute to a better understanding of the transformations towards Construction 4.0 and provide data for industry and universities to respond appropriately to the new demands of the construction industry.

Printability and hardening performance of three-dimensionally-printed geopolymer based on lunar regolith simulant for automated construction of lunar infrastructure

Using an in situ lunar regolith as a construction material in combination with 3D printing not only reduces the weight of materials carried from the Earth but also improves the automation of lunar infrastructure construction. This study aims to improve the printability of a geopolymer based on a BH-1 lunar regolith simulant, including the extrudability, open time, and buildability, by controlling the temperature and adding admixtures. Rheological parameters were used to represent printability with different water-to-binder ratios, printing temperatures, and contents of additives. The mechanical properties of the hardening geopolymer with different filling paths and loading directions were tested. The results show that heating the printed filaments with a water-to-binder ratio of 0.32 at 80 °C can adjust the printability without adding any additive, which can reduce the construction cost of lunar infrastructure. The printability of the BH-1 geopolymer can also be improved by adding 0.3% Attagel-50 and 0.5% polypropylene fiber by mass at a temperature of 20 °C to cope with the changeable environmental conditions on the Moon. After curing under a simulated lunar environment, the 72-h flexural and compressive strengths of the geopolymer specimens reach 4.1 and 48.1 MPa, respectively, which are promising considering that the acceleration of gravity on the Moon is 1/6 of that on the Earth.

Автоматизация мониторинга строительных работ на основе лазерного сканирования с беспилотных воздушных судов

Автоматизация мониторинга строительных работ на основе лазерного сканирования с беспилотных воздушных судов

SPECIAL ISSUE EDITORIAL: The future of construction in the context of digital transformation (CONVR 2022)

ary This special issue of Information Technology in Construction is dedicated to exploring the future of construction in the context of digital transformation. The inspiration for this edition stemmed from the 22nd International Conference on Construction Applications of Virtual Reality (CONVR 2022), which was held at Chung-Ang University in Seoul, South Korea. The overarching theme for CONVR 2022 was “The future of construction in the context of digital transformation and decarbonization”. With over 200 participants attending in person and many more joining online, the conference served as a platform for researchers to present innovative work on VR/AR, BIM, digital twins, IoT, artificial intelligence, construction automation, circular economy, and sustainability in the construction industry. The wealth of knowledge shared during the conference prompted us to invite the authors of outstanding papers to expand their contributions for publication in this special issue. The editors hope that this collection of articles will offer a glimpse into the current state of research on digitalization in the construction industry, and that it will serve as a valuable reference source for further research.

Integrating BIM–IoT and Autonomous Mobile Robots for Construction Site Layout Printing

The traditional methods of marking construction site layouts using manual techniques such as chalk lines are prone to human errors, resulting in discrepancies between blueprints and actual layouts. This has serious implications for project delivery, construction, costs and, eventually, project success. However, this issue can be resolved through autonomous robots and construction automation in line with Industry 4.0 and 5.0 goals. Construction automation enables workers to concentrate on the construction phase and not worry about manual site markups. This leads to an enhancement in their productivity. This study aims to improve the floor layout printing technique by introducing a framework that integrates building information modeling (BIM) and the Internet of Things (IoT), i.e., BIM–IoT and autonomous mobile robots (AMR). The development process focuses on three key components: a marking tool, an IoT-based AMR and BIM. The BIM-based tools extract and store coordinates on the cloud platform. The AMR, developed using ESP32 and connected to the Google Firestore cloud platform, leverages IoT technology to retrieve the data and draw site layout lines accordingly. Further, this research presents a prototype of an automated robot capable of accurately printing construction site layouts. A design science research (DSR) method is employed in this study that includes a comprehensive review of the existing literature and usage of AMRs in construction layout printing. Subsequently building upon the extant literature, an AMR is developed and experiments are conducted to evaluate the system’s performance. The experiment reveals that the system’s precision falls within a range of ±15 mm and its angle accuracy is within ±4 degrees. Integrating robotic automation, IoT and BIM technologies enhances the efficiency and precision of construction layout printing. The findings provide insights into the potential benefits of deploying AMRs in construction projects, reducing site layout errors and improving construction productivity. This study also adds to the body of knowledge around construction automation in line with Industry 4.0 and 5.0 endeavors.

Review of Construction Automation and Robotics Practices in Indonesian Construction State-Owned Enterprises: Position in Project Life Cycle, Gap to Best Practice and Potential Uses

Review of Construction Automation and Robotics Practices in Indonesian Construction State-Owned Enterprises: Position in Project Life Cycle, Gap to Best Practice and Potential Uses

CONSTRUCTION LABOR SHORTAGE, CHALLENGES, AND SOLUTIONS: A SURVEY-BASED APPROACH

The global construction industry contributes about 13% of the world’s GDP and employs over 7% of the world’s working population. Literature showed that the world’s construction industry is highly dependent on skilled labor, labor productivity, and workmanship quality. According to industry experts, there is a significant shortage of labor for construction activities due to various reasons that delay construction activities and reduce construction efficiency. Although this was discussed in various forums in diverse communities, a sustainable solution for this challenge is not straightforward. Labor shortages are mainly due to unpredicted market fluctuations, a lack of specialized skill development programs and a lack of career progression paths. Implementing state-of-the-art technologies and construction automation can be considered potential solutions to reduce the need for labor forces in the construction industry. However, the initial investment required for automation and the technical know-how are key barriers to deploying construction automation in small to medium-scale construction companies. The aim of this study is to develop survey-based solutions to an existing labor shortage and provide a scientific framework to attract and retain construction labor. The findings of this research will benefit builders, contractors, and policymakers to observe labor market behaviors and proactively adapt to market fluctuations to attract and retain needed labor and also for the municipal, provincial, and national level decision-making on sustaining labor and developing new talents.

Preface

Proceedings of the 4th International Conference on Transportation Infrastructure and Sustainable Development TISDIC 2023 Editors Rafat Siddique, Phan H. Nam, Nguyen M. Hai, Nguyen P. Q. Duy, Pham N. Phuong Hoang P. Tung, Nguyen V. Te Ron The 4th International Conference on Transportation Infrastructure and Sustainable Development (TISDIC 2023) is scheduled to take place from 26th to 28th August 2023 at the Da Nang Administrative Centre in the captivating city of Da Nang, Vietnam.Renowned as one of the most distinguished international conferences in the field of transportation infrastructures within Vietnam, TISDIC has garnered widespread acclaim. It stands as a pivotal platform where leading academics, government entities, civil society representatives, and the private sector converge to exchange research insights and innovative solutions, propelling sustainable development goals in transportation infrastructures.TISDIC 2023 includes five primary topics, offering a comprehensive exploration of Advanced Structural and Bridge Engineering, Road and Transport Engineering, AI, IoT and Automation in Transportation Systems, Construction and Building Materials for Sustainable Development, and Geotechnical and Geoenvironmental Engineering. By focusing on these areas, the conference aims to highlight the latest advancements and address the pressing challenges facing the field.The overwhelming response to TISDIC 2023 is a testament to its significance, with a remarkable total of 142 papers submitted for consideration. Through a rigorous evaluation process, 106 exceptional papers were selected for publication in this proceeding, boasting an impressive acceptance rate of 74.6%.We extend our heartfelt appreciation to the authors for their invaluable contributions, which have enriched the conference proceeding. We also express our deepest gratitude to the dedicated reviewers, whose expertise and meticulous evaluation ensured the inclusion of high–quality papers. Their contributions are instrumental in fostering intellectual growth and pushing the boundaries of knowledge in transportation infrastructures.Finally, we would like to express our gratitude for the support provided by the Vingroup Innovation Foundation (VINIF – VINBIGDATA) towards the publication of this proceedings.The EditorsList of Reviewers, Scientific Committee and Organizing Committees are available in this pdf.

Application of digital intelligent construction in the field of water conservancy and hydropower engineering

The application of intelligent construction technology is gradually penetrating the field of water conservancy and hydropower engineering. This article systematically summarizes the application research of intelligent construction in water conservancy and hydropower engineering, including intelligent design, intelligent construction, and intelligent monitoring. In terms of intelligent design, the use of BIM technology to establish three-dimensional modeling of water conservancy and hydropower projects can achieve collaboration, communication, and management of multiple aspects of information and improve design efficiency and quality in terms of intelligent construction. The use of robot technology and automation equipment can achieve construction automation and digitization, reduce human errors and safety risks, and improve construction efficiency and quality. In terms of intelligent monitoring, real-time monitoring, and intelligent management of water conservancy and hydropower projects can be achieved through the Internet of Things and big data technology, timely detection of problems, and early warning processing, ensuring the safe and stable operation of the project. A sound management system is established with standard specifications to promote the widespread application of intelligent construction in the field of water conservancy and hydropower engineering.

Seismic performance of large-scale rectangular reinforced concrete bridge columns with multi-spiral reinforcement

Multi-spiral rectangular column is composed of two central interlocking large spirals that are interlocked with four small spirals in the corners to match a rectangular cross-section. Compared to conventional tied column, the multi-spiral column can provide better confinement and its spiral cage can be automatically fabricated and pre-assembled in a factory. Two large-scale rectangular bridge columns were constructed and tested in this study to examine the constructability of multi-spiral reinforcement and the cyclic performance of columns subjected to strong-axis cyclic loading. The test program was also served as a pioneering demonstration of the new bridge column system to boost construction automation that was recently applied to a highway bridge construction project in Taiwan. Test results indicate that the multi-spiral column exhibits superior ductile behavior compared to the conventional tied column, even though the former has only 73% of the total weight of transverse reinforcement of the latter. The parameters of the smooth hysteresis model identified from the test results further indicate that the multi-spiral column can very closely imitate the hysteresis behavior of the conventional tied column despite its slightly greater stiffness degradation. In addition, incremental dynamic analysis of two idealized bridges with conventional tied column and multi-spiral column show that, when the former bridge reached damage index, DI = 1.0 (i.e. having 20% strength deterioration) at Sa( Tn) = 39.4 m/s2 for far-field ground motions and at Sa( Tn) = 16.4 m/s2 for near-fault ground motions, the corresponding DIs of the latter bridge are approximately 0.74 and 0.71, respectively, even without strength deterioration.