Article
Neurosciences
Yonglin Lin, Ruolei Gu, Jiali Zhou, Yiman Li, Pengfei Xu, Yue-jia Luo
Summary: The study found that social information can impact decision-making, with Social Misalignment Sensitivity and egocentric tendency playing important roles in this process. Different brain regions were activated in socially aligned and misaligned situations, indicating variations in the monitoring system's scope. The dorsolateral prefrontal cortex may selectively interact with SMS in individuals with a low switching threshold, suggesting sensitivity to inter-individual variation.
Article
Business
Giustina Secundo, Gianluca Elia, Alessandro Margherita, Karl-Heinz Leitner
Summary: The article proposes a new approach to sustain strategic decision making in project management by adopting a knowledge visualization view. Moreover, it provides an operational tool for managers and analysts at different levels engaged into the management of a project.
MANAGEMENT DECISION
(2022)
Article
Forestry
Courtney A. Schultz, Lauren F. Miller, Sarah Michelle Greiner, Chad Kooistra
Summary: This study assessed the perceived value of Risk Management Assistance (RMA) and the factors affecting its use in improving wildfire decision-making. While RMA helped line officers communicate decisions more clearly, factors such as personality, pre-season exposure to RMA, local political dynamics, and decision biases influenced its utilization. The findings highlight the complexities of embracing risk management in fire management and similar emergency contexts.
Article
Engineering, Geological
Kok-Kwang Phoon, Zi-Jun Cao, Jian Ji, Yat Fai Leung, Shadi Najjar, Takayuki Shuku, Chong Tang, Zhen-Yu Yin, Yoshida Ikumasa, Jianye Ching
Summary: Modeling is just one aspect of decision making, and the lack of consideration for uncertainties is a major limitation. This review paper focuses on uncertainty quantification and calculation, and discusses how it enhances the role of modeling in decision making. The key output from a reliability analysis is the probability of failure, which is sensitive to data and meaningful for both system and component failures. Geotechnical software can provide better decision support by computing the probability of failure/reliability index as one basic output.
SOILS AND FOUNDATIONS
(2022)
Article
Business
Xu Zhang, Mark Goh, Sijun Bai, Zonghan Wang, Qun Wang
Summary: This study provides a decision model for making robust risk response decisions for single projects within project portfolios under uncertain project interdependencies. The model uses a four-stage interval optimization model based on a two-tier risk-project network. The results show that considering project interdependencies is more beneficial for decision makers compared to ignoring interdependencies or considering them with certainty.
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
(2023)
Review
Environmental Sciences
Marissa K. Webber, Constantine Samaras
Summary: Decision making under deep uncertainty is crucial for managing flooding exacerbated by climate change. Green infrastructure, as an important adaptation strategy, has multiple co-benefits and is characterized as low-regret under uncertainty. However, there is limited research on the integration of green infrastructure in decision making under deep uncertainty. This paper reviews publications that use decision making under deep uncertainty frameworks and focus on green infrastructure adaptation strategies in flood management, identifying trends and proposing solutions for future research.
Article
Management
Amir Sakka, Mounira Kourjieh, Ines Ben Kraiem
Summary: The selection of the most suitable project management method is critical for the success of IT projects and the company's profitability and operability. However, due to the variety of PMMs and companies' strategies, the decision-making process can be complex and potentially misleading. This study proposes a methodological approach for decision analysis that provides a structured and reproducible decision-making process with the assistance of a group decision support system. The methodology was evaluated with three real case studies and identified opportunities for further improvements and research.
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2023)
Article
Environmental Sciences
G. Harik, Ibrahim Alameddine, R. Zurayk, M. El-Fadel
Summary: A spatio-temporal Agent Based Modeling (ABM) framework is developed to probabilistically predict farmers' decisions concerning their future farming practices when faced with potential water scarcity induced by future climate change. The proposed framework forecasts farmers' behavior assuming varying utility functions and successfully captures the actions and interactions between farmers and their environment. Including social factors in the model significantly improves the accuracy of predicting farmers' decisions.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2023)
Article
Green & Sustainable Science & Technology
Ivan Litvaj, Olga Ponisciakova, Dana Stancekova, Jaroslava Svobodova, Jozef Mrazik
Summary: This paper discusses the complexity of decision-making faced by managers in a dynamic and turbulent environment, particularly in relation to quality management. It emphasizes the importance of the connection between theory and practice, and highlights the use of procedures, methods, and knowledge in the decision-making process within the context of quality management.
Article
Public, Environmental & Occupational Health
Kara Morgan, Zachary A. Collier, Elisabeth Gilmore, Ketra Schmitt
Summary: Emerging risks are characterized by a lack of data, rapidly changing information, and the absence of existing predictive models. Effective decision-making for these risks requires scoping the decision context and shared responsibility between analysts and decision-makers in rapidly evolving situations. Simplified analytical approaches may be more suitable for emerging risks, providing increased transparency, ease of explanation, and the ability to conduct new analyses quickly. Continued dialogue and discussion among decision and risk analysis communities can enhance the credibility and usefulness of models for emerging risks.
Article
Economics
Xileidys Parra, Xavier Tort-Martorell, Fernando Alvarez-Gomez, Carmen Ruiz-Vinals
Summary: The decision-making process (DMP) in organizations has undergone changes influenced by information technologies and computational science. This study provides a chronological review of the information-driven DMP evolution and discusses how technology has impacted information generation, storage, management, and its utilization for improved decision-making and knowledge acquisition.
JOURNAL OF THE KNOWLEDGE ECONOMY
(2022)
Article
Computer Science, Information Systems
Min-Seok Pang, Gwanhoo Lee
Summary: Many IT projects in the U.S. federal government have faced failures and overruns, causing wastage of tax revenues and eroding public confidence. Existing research from the private sector and information systems field has not sufficiently addressed the factors influencing IT project performance in the federal government. By analyzing official datasets, we find that federal bureaus with more IT managerial officials and supervisors experience lower time overruns in their IT projects, although this relationship weakens when there are more ongoing projects. These findings contribute new theoretical insights and policy implications for IT project management in the government.
Article
Engineering, Industrial
Mohammad Hadi Charkhakan, Gholamreza Heravi
Summary: This study proposes a framework for conflict management based on prediction models, and conducts manageability analysis of conflicts in construction projects. The results show that identifying weaknesses in decision-making patterns and organizational culture is the most effective measure to prevent conflicts.
ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT
(2023)
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
Green & Sustainable Science & Technology
Nomeda Dobrovolskiene, Anastasija Pozniak, Manuela Tvaronaviciene
Summary: Real estate projects are commonly chosen based on risk and return, with limited sustainability assessment methods. The newly developed Real Estate Sustainability Index (RESI) introduces a technological dimension for evaluating projects and promoting technological progress and investments.
Article
Operations Research & Management Science
Reza Faturechi, Shabtai Isaac, Elise Miller-Hooks, Lei Feng
ANNALS OF OPERATIONS RESEARCH
(2018)
Article
Green & Sustainable Science & Technology
Shabtai Isaac, Slava Shubin, Gad Rabinowitz
Article
Construction & Building Technology
Vito Getuli, Pietro Capone, Alessandro Bruttini, Shabtai Isaac
AUTOMATION IN CONSTRUCTION
(2020)
Article
Construction & Building Technology
A. Mavrigiannaki, G. Pignatta, M. Assimakopoulos, M. Isaac, R. Gupta, D. Kolokotsa, M. Laskari, M. Saliari, I. A. Meir, S. Isaac
Summary: This paper examines the benefits and barriers associated with the implementation of the Net Zero Energy (NZE) concept at a settlement scale, using the comprehensive approach developed in the EU Horizon 2020 ZERO-PLUS project. The ZERO PLUS approach leads to achieving NZE settlements with lower initial costs, low energy consumption, and high renewable energy production. The main issues encountered in the implementation of NZE settlements are external barriers raised by planning policies and the challenge of integrating project stakeholders' needs.
ENERGY AND BUILDINGS
(2021)
Article
Green & Sustainable Science & Technology
Konstantyn Povetkin, Shabtai Isaac
JOURNAL OF CLEANER PRODUCTION
(2020)
Article
Construction & Building Technology
Clara Mariana Katsuragawa, Gunnar Lucko, Shabtai Isaac, Yi Su
Summary: This research proposes a new fuzzy scheduling method that models activity durations as continuous and flexible fuzzy cones, and explicitly expresses continuous buffers in both time and work/space dimensions to generate efficient schedules. It also identifies full or partial fuzzy criticality and fuzzy float to help managers allocate attention effectively.
JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT
(2021)
Article
Construction & Building Technology
Shabtai Isaac, Maxim Shimanovich
Summary: This paper presents a novel method for the automated scheduling and control of Mechanical and Electrical Works, utilizing topological analysis of component position to define relationships between installation tasks and determine the optimal schedule. Specific control points are defined to support effective progress tracking, ensuring tasks adhere to the schedule in practice.
AUTOMATION IN CONSTRUCTION
(2021)
Article
Construction & Building Technology
E. Zilberberg, P. Trapper, I. A. Meir, S. Isaac
Summary: The methodology allows for optimizing the dimensions of a building's structural components at the initial design stage to minimize energy consumption. Results from a case study show that structural components with higher thermal mass can reduce operational energy consumption by up to 3%, and adding insulation significantly reduces operational energy consumption.
ENERGY AND BUILDINGS
(2021)
Article
Engineering, Industrial
Eliav Tuval, Shabtai Isaac
Summary: While cloud-based collaborative building design processes have become common, the Covid-19 pandemic has posed challenges to colocated meetings in the construction industry. This study aims to develop an alternative method for design coordination in a cloud-based BIM environment, reducing the need for meetings and improving the design process.
JOURNAL OF MANAGEMENT IN ENGINEERING
(2022)
Article
Geosciences, Multidisciplinary
M. Vatenmacher, T. Svoray, M. Tsesarsky, S. Isaac
Summary: This research introduces a new approach to reduce the complexity of vulnerability analysis in infrastructure systems. By framing vulnerability analysis as part of a decision-making process, the focus is shifted to clear and quantifiable requirements of specific end-users. The approach identifies critical components in supply chains by reversing the analysis process and reveals minimum threshold states in which critical supplies will be interrupted. The method also allows the consideration of highly relevant information currently ignored to avoid excessive computational burden.
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION
(2022)
Article
Engineering, Civil
Lior Nahum, Shabtai Isaac, Alva Peled, Oded Amir
Summary: This study focused on the construction of textile-reinforced concrete beams made of carbon fabrics without steel rebars, aiming to save materials and reduce Global Warming Potential (GWP). Two approaches were explored: replacing steel rebars with carbon textiles to eliminate the thick concrete protective layer, and optimizing truss-like beam designs based on topology optimization. The results demonstrated significant savings in concrete and reinforcement, as well as reduced weight when using textiles as reinforcement instead of steel.
JOURNAL OF COMPOSITES FOR CONSTRUCTION
(2023)
Article
Green & Sustainable Science & Technology
Niv Yonat, Shabtai Isaac, Igal M. Shohet
Summary: The purpose of this research is to provide a theoretical and practical approach to manage complex infrastructures. The research addresses the challenges of complexity, nonlinearity, and uncertainty by directly studying the systems' responses. Various tools, such as graph theory, statistics, and digital signal processing, are applied within the framework of the theory of faults. The findings demonstrate the effectiveness of the theory and application in analyzing a municipal drainage system and developing real-time management tools.
SMART AND SUSTAINABLE BUILT ENVIRONMENT
(2023)
Article
Green & Sustainable Science & Technology
Ron Shwartz, Shabtai Isaac
Summary: This research offers a methodology for designing microgrids for communities with different types of consumers and energy systems, focusing on long duration power outages. It identifies optimal solutions for converting existing energy systems into microgrids based on community needs. The study also examines the potential for creating an extended system by linking microgrids.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2023)
Editorial Material
Construction & Building Technology
Adel Francis, Borja Garcia de Soto, Shabtai Isaac
FRONTIERS IN BUILT ENVIRONMENT
(2020)
Proceedings Paper
Architecture
Isaac A. Meir, Shabtai Isaac, Denia Kolokotsa, Konstantinos Gobakis, Gloria Pignatta
SUSTAINABILITY IN THE BUILT ENVIRONMENT FOR CLIMATE CHANGE MITIGATION (SBE19)
(2020)
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)
