Article
Engineering, Industrial
Fang-Jye Shiue, Hsin-Yun Lee, Meng-Cong Zheng, Akhmad F. K. Khitam, Sintayehu Assefa
Summary: The study presents an integrated model for project segmentation by combining simulation and multiple attribute decision-making methods, providing valuable insights for planning the size of contract packages in large projects.
ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT
(2021)
Article
Construction & Building Technology
Sou-Sen Leu, Yanni Liu, Pei-Lin Wu
Summary: Construction project cost overrun is a common problem in the industry, with an average increase of approximately 33% in project costs. Previous research on cost overruns relies on historical data, but real-time project cost data that consider project characteristics and trends are more reliable for forecasting. This paper proposes a real-time hidden Markov chain (HMM) model to predict cost overrun risk based on project-owned cost performance data and corrective actions. The model assumes a Poisson arrival pattern for cost overrun events and uses a particle filter to run simulations. Validation results using a building project in Taiwan show that the model's posterior probabilities are highly consistent with real construction project cost overrun ratios. The proposed model can provide early alerts of cost overruns and effective cost management plans to mitigate the frequency of cost overruns.
Article
Management
Mehdi Rajabi Asadabadi, Ofer Zwikael
Summary: Estimating time and cost for project activities is a challenging task due to high level of risk and uncertainty. This study proposes using the concept of stratification to improve reliability by considering a set of states and system transitions. By applying this concept, estimations for project activities can be computed more accurately, increasing reliability in completion time and cost estimations.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Public, Environmental & Occupational Health
Maryam Alkaissy, Mehrdad Arashpour, Ron Wakefield, Reza Hosseini, Peter Gill
Summary: The construction sector is vulnerable to safety risk incidents, with a strong correlation between middle-aged workers and project cost overruns. Tools such as discrete event simulation can quantitatively measure the impact of safety risks on project performance.
Article
Management
Jie Song, Annelies Martens, Mario Vanhoucke
Summary: Schedule Risk Analysis (SRA) provides reliable activity sensitivity information for corrective actions during project control. This study extends traditional SRA metrics with RC-SRA metrics for resource constrained projects, and introduces new resource-based sensitivity metrics. Computational experiments show that the RC-SRA metrics can identify activities with higher sensitivity values. Activity selection strategies and corrective actions are proposed, with findings suggesting that reducing activity durations is more effective than reducing resource demand, especially when updated dynamically during project execution.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Gozde Bilgin, Irem Dikmen, M. Talat Birgonul, Beliz Ozorhon
Summary: Project portfolio management is a systematic process that involves assessing portfolio risk and profitability, as well as the alignment of projects with company objectives. This study developed a tool called COPPMAN to support decision-making in construction companies, which was found to be effective in practice. The research design and findings can be applied to the development of similar tools in other project-based industries.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2022)
Article
Business
Magne Jorgensen, Morten Welde, Torleif Halkjelsvik
Summary: Evaluation of cost estimates should be fair and incentivize accuracy. However, current evaluation measures fail to reward the most accurate cost estimates. To address this issue, we propose the use of probabilistic cost estimates and provide guidelines for evaluating such estimates. An analysis of 69 large Norwegian governmental projects demonstrates the accuracy and lack of bias in the P50 estimates, as well as the calibration of prediction intervals. However, the cost prediction intervals do not provide informative differences in cost uncertainty, thus limiting their usefulness.
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
(2023)
Article
Forestry
David E. Calkin, Christopher D. O'Connor, Matthew P. Thompson, Richard Stratton
Summary: The USDA Forest Service initiated the Risk Management Assistance (RMA) program in 2016 to improve strategic decision-making on large and complex wildfire events. RMA involves personnel from various disciplines to produce actionable science, aligning with best practices in risk assessment and decision-making. Over the years, RMA has evolved in content, structure, and application domain, expanding from large incident support to pre-event assessment and organizational change.
Article
Automation & Control Systems
Jonathan P. Epperlein, Roman Overko, Sergiy Zhuk, Christopher King, Djallel Bouneffouf, Andrew Cullen, Robert Shorten
Summary: Reinforcement learning aims to learn optimal decisions in unknown environments through actions and reward observations. Depending on whether the environment is influenced by RL agent actions, the problem can be modeled as a contextual multi-armed bandit or a Markov decision process. This work proposes a probabilistic structure estimation method based on likelihood-ratio test for algorithm selection, and provides a regret bound for the framework.
Article
Engineering, Multidisciplinary
Zhengran Lu, Chao Guo
Summary: The fastener, as a core component of the formwork support system, has a significant influence on the system's bearing capacity. The increase in failure probability of the fastener leads to a significant decrease in the magnitude and probability of the decrease in the bearing capacity. Deterministic analysis based on the integrity of all fasteners is impractical and unsafe.
AIN SHAMS ENGINEERING JOURNAL
(2023)
Article
Management
Rongda Chen, Ze Wang, Liu Yang, Chi To Ng, T. C. E. Cheng
Summary: This article uses a new structural credit model integrated with data analytics to estimate and analyze the risk of a credit portfolio. The results show that the model can assist decision makers in making optimal operational decisions with total provision for risk.
Article
Engineering, Aerospace
Oliver Pohling, Sebastian Schier-Morgenthal, Sandro Lorenz
Summary: In this paper, different prediction engines for airport management are evaluated and compared based on accuracy and calculation speed. The results show that accuracy and calculation speed are opposed, with different models having varying levels of accuracy and calculation times.
Article
Environmental Sciences
Emanuela Bianchi Janetti, Monica Riva, Alberto Guadagnini
Summary: This study presents a comprehensive framework for the protection of natural springs and probabilistic risk assessment in uncertain groundwater system characterizations. The research focuses on a regional-scale hydrogeological setting in Northern Italy, with high-quality springs forming a unique system of critical importance. By considering uncertainties in model parameters and conceptual models, the study quantifies the risk of system failure due to various groundwater extraction strategies and identifies the most vulnerable springs.
SCIENCE OF THE TOTAL ENVIRONMENT
(2021)
Article
Computer Science, Interdisciplinary Applications
Lei Wang, Yuxuan Song, Ronggui Ding, Mark Goh
Summary: This research focuses on accurately assessing risk criticality and proposing effective risk response solutions by constructing a risk delay network and introducing an integrated approach.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Engineering, Aerospace
Dooyoul Lee, Hwanjeong Cho, Min-Saeng Kim, Kybeom Kwon
Summary: This study proposes a framework for the probabilistic risk analysis of aircraft self-collisions, focusing on military aircraft with external stores. A case study involving an ejected gun shell is analyzed, taking into account uncertain factors such as random shell rotation. The probability of collision and corresponding risks are evaluated using a Monte Carlo simulation and probabilistic ballistic model.
Article
Operations Research & Management Science
Duc-Hoc Tran, Jui-Sheng Chou, Duc-Long Luong
Summary: This work presents a new hybrid evolutionary approach, called the fuzzy clustering artificial bee colony approach (FABC), to optimize resource assignment and scheduling for non-unit repetitive projects (NRP). The proposed method outperforms benchmark algorithms in terms of project duration and optimal solution deviation.
OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Jui-Sheng Chou, Jeffisa Delaosia Kosasih, Wai K. Chong
Summary: The study developed a cloud evolutionary machine learning system aimed at providing user-friendly web analytics for solving engineering problems. The system successfully addressed classification and regression problems with high accuracy and improved performance metrics.
ENGINEERING WITH COMPUTERS
(2022)
Article
Energy & Fuels
Jui-Sheng Chou, Sheng-Ming Hsu
Summary: This study established an automated platform using machine learning and optimization methods to predict residential electricity demand in each city in Taiwan, generating an accurate and effective model. By providing monthly electricity consumption information through a web-based system, it helps power providers and government discuss policies and set energy use goals.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Construction & Building Technology
Jui-Sheng Chou, Dinh-Nhat Truong
Summary: The MOFBI algorithm utilizes chaotic maps and elite populations to explore and exploit multi-objective search spaces, providing more accurate approximations of Pareto-optimal solutions compared to other algorithms.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Computer Science, Artificial Intelligence
Jui-Sheng Chou, Ngoc-Mai Nguyen, Chih-Pin Chang
Summary: The effective prediction of stock market prices and trends is crucial for investors and stakeholders. This study proposes an intelligent candlestick forecast system that combines three forecasting techniques. The system uses metaheuristic algorithms to optimize the prediction model and has been validated against actual market operations.
APPLIED SOFT COMPUTING
(2022)
Article
Construction & Building Technology
Dinh-Nhat Truong, Jui-Sheng Chou
Summary: This paper presents a novel fuzzy adaptive jellyfish search-optimized stacking system that demonstrates high accuracy and effectiveness in global optimization and engineering informatics.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Energy & Fuels
Jui-Sheng Chou, Tsung-Chi Cheng, Chi-Yun Liu, Chung-Yu Guan, Chang-Ping Yu
Summary: Plant microbial fuel cells (PMFCs) are an emerging green-energy technology. An artificial intelligence model using deep learning techniques and an optimization algorithm was developed to accurately forecast the power generation capacity of PMFCs. The model can also estimate the future power generation capacity of PMFC devices.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Construction & Building Technology
Chi-Yun Liu, Jui-Sheng Chou
Summary: Bridge collapses and fractures have occurred due to lack of inspection and maintenance, and traditional visual inspection methods have limitations. This study presents an unmanned aerial vehicle (UAV) equipped with a Bayesian-optimized deep learning model for computer vision-based identification of deterioration patterns and segmented areas in composite bridge decks. The proposed module improves inspection accuracy, reduces labor safety hazards, and can be embedded in an artificial intelligence chip for consumer-grade UAVs dedicated to external bridge inspections.
AUTOMATION IN CONSTRUCTION
(2023)
Article
Multidisciplinary Sciences
Jui-Sheng Chou, Asmare Molla
Summary: The complexity of engineering optimization problems is increasing. Classical gradient-based optimization algorithms have limitations in solving complex problems. Metaheuristics, including the jellyfish search optimizer (JSO), have become popular for their simplicity and robustness in yielding results. JSO outperforms many well-known metaheuristics in various benchmark functions and real-world applications. This paper provides a comprehensive discussion of the latest findings related to JSO, including its inspiration, variants, applications, and developments.
SCIENTIFIC REPORTS
(2022)
Article
Construction & Building Technology
Jui-Sheng Chou, Chi-Yun Liu, Handy Prayogo, Riqi Radian Khasani, Danny Gho, Gretel Gaby Lalitan
Summary: Reinforced concrete shear walls are commonly used in seismic structures, and modern building codes have provisions for shear capacity. However, existing provisions may have limitations, and rational methods can be used as an alternative to predict shear wall capacity. In this study, data-driven machine learning models are trained and optimized using metaheuristic algorithms to achieve the best results. This approach improves building safety, simplifies calculation processes, and reduces material costs.
JOURNAL OF BUILDING ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Jui-Sheng Chou, Li-Ying Chen, Chi-Yun Liu
Summary: A baseline model for predicting the compressive strength of concrete was constructed using machine-learning methods. The model was optimized using a newly developed algorithm and historical data from concrete plants. An expert system was developed to facilitate quality management and improve the safety of concrete structures.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Engineering, Civil
Jui-Sheng Chou, Yu-Hsuan Chen, Chi-Yun Liu, Wai Oswald Chong
Summary: Due to the COVID-19 pandemic, construction sites in Taiwan have implemented measures such as limiting the number of workers and conducting independent work to prevent the spread of the virus. Unclear job handover and the presence of infected workers pose challenges to construction quality. Neglecting inspection processes can result in errors and poor quality. To address these issues, a literature analysis and in-depth interviews with subcontractors were conducted to identify quality management problems in private housing projects. Improvement measures and a simplified checklist were formulated based on practical feedback. An inspection application was also developed to facilitate construction practice, especially during the pandemic.
JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT
(2023)
Article
Energy & Fuels
Jui-Sheng Chou, Ngoc-Quang Nguyen
Summary: The energy sector needs to find a delicate balance between energy supply and demand. Accurate energy consumption forecasts can assist plant operators in achieving this goal. This study explores the application of various techniques from three categories of artificial intelligence, namely convolutional neural networks (CNNs), machine learning (ML), and time-series deep learning (DL), to predict short-term regional energy consumption.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Dinh-Nhat Truong, Jui-Sheng Chou
Summary: This study develops a novel fuzzy adaptive forensic-based investigation algorithm (FAFBI) to optimize frequency-constrained structural dome design. The results show that FAFBI outperforms other compared algorithms in finding optimal values. The proposed FAFBI algorithm is an effective tool for solving mathematical optimization problems and for use in the design phase of structural construction.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2023)
Article
Construction & Building Technology
Jui-Sheng Chou, Yu-Hsiu Chang, Asmare Molla, Wai Oswald Chong
Summary: Urban renewal involves different stakeholders with different expectations, such as residents and developers. This study analyzed completed and ongoing urban renewal projects and found that land integration is a crucial factor for success. Developers need to consider factors such as resident approval rates, complexity of ownership, environmental friendliness, high rewards, case review duration, and providing a vision of the community after renewal. Urban renewal also relies on government assistance and cooperation among developers, government, and landowners.
SUSTAINABLE CITIES AND SOCIETY
(2023)
Article
Construction & Building Technology
Jia Liang, Qipeng Zhang, Xingyu Gu
Summary: A lightweight PCSNet-based segmentation model is developed to address the issues of insufficient performance in feature extraction and boundary loss information. The introduction of generalized Dice loss improves prediction performance, and the visualization of class activation mapping enhances model interpretability.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Gilsu Jeong, Minhyuk Jung, Seongeun Park, Moonseo Park, Changbum Ryan Ahn
Summary: This study introduces a contextual audio-visual approach to recognize multi-equipment activities in tunnel construction sites, improving monitoring effectiveness. Tested against real-world operation data, the model achieved remarkable results, emphasizing the potential of contextual multimodal models in enhancing operational efficiency in complex construction sites.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Jin Wang, Zhigao Zeng, Pradip Kumar Sharma, Osama Alfarraj, Amr Tolba, Jianming Zhang, Lei Wang
Summary: This study presents a dual-path network for pavement crack segmentation, combining Convolutional Neural Network (CNN) and transformer. A lightweight CNN encoder is used for local feature extraction, while a novel transformer encoder integrates high-low frequency attention mechanism and efficient feedforward network for global feature extraction. Additionally, a complementary fusion module is introduced to aggregate intermediate features extracted from both encoders. Evaluation on three datasets confirms the superior performance of the proposed network.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Pierre Gilibert, Romain Mesnil, Olivier Baverel
Summary: This paper introduces a flexible method for crafting 2D assemblies adaptable to various geometric assumptions in the realm of sustainable construction. By utilizing digital fabrication technologies and optimization approaches, precise control over demountable buildings can be achieved, improving mechanical performance and sustainability.
AUTOMATION IN CONSTRUCTION
(2024)
Review
Construction & Building Technology
Jorge Loy-Benitez, Myung Kyu Song, Yo-Hyun Choi, Je-Kyum Lee, Sean Seungwon Lee
Summary: This paper discusses the advancement of tunnel boring machines (TBM) through the application of artificial intelligence. It highlights the significance of AI-based management subsystems for automatic TBM operations and presents recent contributions in this field. The paper evaluates modeling, monitoring, and control subsystems and suggests research paths for integrating existing management subsystems into TBM automation.
AUTOMATION IN CONSTRUCTION
(2024)
Review
Construction & Building Technology
Alireza Shamshiri, Kyeong Rok Ryu, June Young Park
Summary: This paper reviews the application of text mining and natural language processing in the construction field, highlighting the need for automation and minimizing manual tasks. The study identifies potential research opportunities in strengthening overlooked construction aspects, coupling diverse data formats, and leveraging pre-trained language models and reinforcement learning.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Zhengyi Chen, Hao Wang, Keyu Chen, Changhao Song, Xiao Zhang, Boyu Wang, Jack C. P. Cheng
Summary: This study proposes an improved coverage path planning system that leverages building information modeling and robotic configurations to optimize coverage performance in indoor environments. Experimental validation shows the effectiveness and applicability of the system. Future research will focus on further enhancing coverage ratio and optimizing computation time.
AUTOMATION IN CONSTRUCTION
(2024)
Review
Construction & Building Technology
Yonglin Fu, Junjie Chen, Weisheng Lu
Summary: This study presents a review of human-robot collaboration (HRC) in modular construction manufacturing (MCM), focusing on tasks, human roles, and interaction levels. The review found that HRC solutions are applicable to various MCM tasks, with a primary focus on timber component production. It also revealed the diverse collaborative roles humans can play and the varying levels of interaction with robots.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Qiong Liu, Shengbo Cheng, Chang Sun, Kailun Chen, Wengui Li, Vivian W. Y. Tam
Summary: This paper presents an approach to enhance the path-following capability of concrete printing by integrating steel cables into the printed mortar strips, and validates the feasibility and effectiveness of this approach through experiments.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Honghu Chu, Lu Deng, Huaqing Yuan, Lizhi Long, Jingjing Guo
Summary: The study proposes a method called Cascade CATransUNet for high-resolution crack image segmentation. This method combines the coordinate attention mechanism and self-cascaded design to accurately segment cracks. Through a customized feature extraction architecture and an optimized boundary loss function, the proposed method achieves impressive segmentation performance on HR images and demonstrates its practicality in UAV crack detection tasks.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Daniel Lamas, Andres Justo, Mario Soilan, Belen Riveiro
Summary: This paper introduces a new method for creating synthetic point clouds of truss bridges and demonstrates the effectiveness of a deep learning approach for semantic and instance segmentation of these point clouds. The proposed methodology has significant implications for the development of automated inspection and monitoring systems for truss bridges.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Kahyun Jeon, Ghang Lee, Seongmin Yang, Yonghan Kim, Seungah Suh
Summary: This study proposes two enhanced unsupervised text classification methods for domain-specific non-English text. The results of the tests show that these methods achieve excellent performance on Korean building defect complaints, outperforming state-of-the-art zero-shot and few-shot text classification methods, with minimal data preparation effort and computing resources.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Yoonhwa Jung, Julia Hockenmaier, Mani Golparvar-Fard
Summary: This study introduces a transformer-based natural language processing model, UNIfORMATBRIDGE, that automatically labels activities in a project schedule with Uniformat classification. Experimental results show that the model performs well in matching unstructured schedule data to Uniformat classifications. Additionally, the study highlights the importance of this method in developing new techniques.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
De-Graft Joe Opoku, Srinath Perera, Robert Osei-Kyei, Maria Rashidi, Keivan Bamdad, Tosin Famakinwa
Summary: This paper introduces a digital twin technology combining Building Information Modelling and the Internet of Things for the construction industry, aiming to optimize building conditions. The technology is implemented in a university library, successfully achieving real-time data capture and visual representation of internal conditions.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Zaolin Pan, Yantao Yu
Summary: The construction industry faces safety and workforce shortages globally, and worker-robot collaboration is seen as a solution. However, robots face challenges in recognizing worker intentions in construction. This study tackles these challenges by proposing a fusion method and investigating the best granularity for recognizing worker intentions. The results show that the proposed method can recognize multi-granular worker intentions effectively, contributing to seamless worker-robot collaboration in construction.
AUTOMATION IN CONSTRUCTION
(2024)
