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Civil infrastructure systems (CIS) require effective systems-level operation and maintenance (O&M) processes to ensure safety and efficiency. Such processes demand significant human efforts in human/team cognition, decision-making, and execution of activities. Poor human behaviors could affect CIS O&M safety and efficiency. This review synthesized...
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... Cyber system reliability refers to the uncertainties that could arise during the data collection and analysis for supporting civil infrastructure operations and decision processes (Sun et al., 2020). These uncertainties can impact subject experts' decisions and behavioral reliabilities when they assess and inspect the condition of civil infrastructures through three steps of data analysis: 1) pre-processing of raw data, 2) data processing, and 3) data interpretation. ...
... AI techniques in the Construction sector could boost the sector's automation, productivity, and reliability. Construction practitioners and researchers use AI to identify design conflicts, predict potential risks and human errors in the workflows, and learn infrastructure degradation patterns Sun et al., 2020). However, integrating the emerging AI techniques and algorithms is still challenging for ensuring civil infrastructure projects' safety and productivity. ...
... CISs constitute physical and cyber systems (e.g., sensing and actuating systems) such as NPPs, which require appropriate and effective operation and maintenance to provide vital services to contemporary society (Sun et al., 2020). Two primary goals in civil infrastructure operations are ensuring operational safety and productivity. ...
Two primary goals for civil infrastructure construction and operation processes are ensuring process safety and productivity. Large-scale construction and operation projects, such as the maintenance of bridges and power plants, can involve thousands of spatially and temporally distributed operation activities. However, the reliability of the physical facilities, operation systems, and human behaviors brings challenges for civil infrastructure operators to satisfy the operational safety and productivity goals. Artificial Intelligence (AI) can proactively detect and resolve reliability issues in civil infrastructure construction, maintenance, and operation processes, which traditional computational approaches cannot provide effective support. AI methods (e.g., machine learning and symbolic reasoning techniques) have become valuable tools for infrastructure engineers and operators. This chapter first defines the roles of workers and AI in civil infrastructure systems engineering and discusses the obstacles that hinder civil infrastructure systems from fully achieving their safety and productivity goals. Then, the following subsections present research and applications of AI methods for addressing the identified challenges. The last sub-section of this chapter discusses some issues in using AI methods to resolve reliability issues in civil infrastructure operations.
... Given these points, the widespread adoption of deep learning-based non-intrusive computer vision techniques for automatic pavement distress recognition has led to a significant surge due to the beneficial impact on road agencies, road users (Sun et al., 2020) and the environment (Xu et al., 2021a). Computer Vision systems based on Deep Learning can provide enriched information to achieve a high-level understanding of objects and events present in a scene, including automated post-construction quality assessment for defects in road inspection using visual data (Luo et al., 2022). ...
Research on road infrastructure structural health monitoring is critical due to the increasing problem of deteriorated conditions. The traditional approach to pavement distress detection relies on human-based visual recognition, a time-consuming and labor-intensive method. While Deep Learning-based computer vision systems are the most promising approach, they face the challenges of reduced performance due to the scarcity of labeled
data due, high annotation costs misaligned with engineering applications, and limited instances of minority defects. This paper introduces a novel generative diffusion model for data augmentation, creating synthetic images of rare defects. It also investigates methods to enhance image quality and reduce production time. Compared to Generative Adversarial Networks, the optimal configuration excels in reliability, quality and diversity. After incorporating synthetic images into the training of our pavement distress detector, YOLOv5, its mean average precision has been enhanced. This computer-aided system enhances recognition and labelling efficiency, promoting intelligent maintenance and repairs.
... Sewer system is a key city infrastructure protecting social assets and safety from excess and polluted water (Sun et al., 2020;Yazdanfar and Sharma, 2015;Zhou et al., 2019). Without timely operation and maintenance, the system may suffer from structural and/or functional damages, which directly affect the system performance and shorten its technical lifetime Xie et al., 2019). ...
Deep learning has shown promising performance in automated sewer defect detection, however, is generally data-driven and computationally intensive. Transfer learning (TL) solves the problem of data limitations and avoids the need to build models from scratch. This study compared the performance of a TL-based YOLO network (with 11 pretrained backbone CNNs) with four mainstream object detection methods (ODMs) for detecting five types of sewer defects. Results showed that the transferred YOLO methods generally outperformed the other ODMs, with improved detection precision, computation speed and intersection over union (IoU). Among the CNNs, Resnet18 achieved the best performance, while Inceptionresnetv2 was the least effective. The ODMs worked best in detecting disjoint, whereas tree root and crack were most challenging to predict. The work not only illustrated the benefits of TL, but also provided technical guidance to practitioners who lack expertise in ODMs and rely on TL for better sewer defect detection.
... Human-cyber (HC) reliability refers to the uncertainties that could arise during data analysis processes while analyzing large amounts of data related to civil infrastructure operation and decision processes. Such reliability demands more accurate and effective human-cyber interactions to ensure the safety and efficiency of the CIS O&M [29]. However, the lack of a systematic review of a wide range of HC reliability issues impedes SHM of bridges and prevents researchers and professionals from effectively using existing theoretical and practical tools in resolving their difficulties related to HC reliability problems. ...
Trustworthy and explainable structural health monitoring (SHM) of bridges is crucial for ensuring the safe maintenance and operation of deficient structures. Unfortunately, existing SHM methods pose various challenges that interweave cognitive, technical, and decision-making processes. Recent development of emerging sensing devices and technologies enables intelligent acquisition and processing of massive spatiotemporal data. However, such processes always involve human-in-the-loop (HITL), which introduces redundancies and errors that lead to unreliable SHM and service safety diagnosis of bridges. Comprehending human-cyber (HC) reliability issues during SHM processes is necessary for ensuring the reliable SHM of bridges. This study aims at synthesizing studies related to HC reliability for supporting the trustworthy and explainable SHM of bridges. The authors use a bridge inspection case to lead a synthesis of studies that examined techniques relevant to the identified HC reliability issues. This synthesis revealed challenges that impede the industry from monitoring, predicting, and controlling HC reliability in bridges. In conclusion, a research road map was provided for addressing the identified challenges.
... Sun et al. [45] highlighted that an indicator of designers' commitment to reducing project energy consumption is to consider operation and maintenance factors for project energy systems and equipment to ensure energy reduction. According to Hassanain et al. [46], those factors reduce systems maintenance and improve energy efficiency and are considered essential elements in reaching the project's energy reduction goal. ...
... In support of this, Broughton [47] highlighted that including those factors within project design starts by identifying information regarding accessibility, modularization, standardization, and machine maintenance of the selected project's systems and equipment. Sun et al. [45] argued that, apart from project systems and equipment information, seeking input from maintenance personnel is essential to include their viewpoints in project design and helps the design team ensure energy reduction durability. Hassanain et al. [46] stated that the project design team must ensure that both operational considerations and energy equipment maintenance information are integrated into the project design to ensure its effectiveness. ...
... Hassanain et al. [46] stated that the project design team must ensure that both operational considerations and energy equipment maintenance information are integrated into the project design to ensure its effectiveness. Further, Sun et al. [45] specified that to maximize the integration of maintenance factors into the project design, designers need to use a simulation program to simulate annual loads and peak demands for project systems to identify maintenance reduction opportunities that lower energy consumption. ...
The construction industry is considered one of the largest contributors to climate change through its consumption of natural resources and generation of greenhouse gases. Much of this can be attributed to inadequate decision making and follow-up within construction companies. To mitigate this problem, considerable research on Sustainable Development (SD) reports on decision support systems have been developed in order to make sound decisions with respect to the environment. Nonetheless, and despite the availability of such tools, these systems fail to track the commitment to SD decisions and goals during the different phases of construction projects in general and the design phase in particular. As such, this study identified three standard SD indicators: waste reduction, energy consumption, and carbon emissions as the main contributors, and developed the framework to track the project stakeholders’ commitment to the relevant SD indicators during the project design phase. The developed framework was validated via an expert panel and used to create a Sustainable Development Commitment Tracking Tool (SDCTT-D). The SDCTT-D tool was also applied in an infrastructure project case study. The results of this study gauged the usability of the developed tool and corroborated the research premise.
... Nonetheless, the practical application is much more complicated than this basic description. Compared to the conventional manual way, achieving automation-enabled façade visual inspection requires more interactions among humans, physical environment, and cyberelements (e.g., data and model) (Sun et al. 2020a). Unfortunately, the frequent and complex interactions within the human-cyber-physical system, such as the performance of various participants, the efficiency of information transfer, and the planning of the work schedule, make it arduous to manage the inspection process (Agnisarman et al. 2019). ...
... Along with the increasing interest in automation, human factors have been explored and investigated in various applications and studies of automation in civil engineering area. According to a recent study by Sun et al. (2020a), uncertainty caused by human factors was mainly generated from three aspects: ...
... This study also assumed that the participants are rational people; thus, the selected human factors are required to eliminate personal emotions. To systematically discuss the human factors, some previous reviews have analyzed the human factors related to automation-enabled applications in the civil engineering area from different perspectives, such as human interaction types (Sun et al. 2020a), level of autonomy of the system (Agnisarman et al. 2019), and human performance (Kim and Irizarry 2019). However, these studies did not elaborate on the types of human factors from a general perspective. ...
Given that traditional façade visual inspection entails laborious, dangerous, and inefficient manual work, automation-enabled façade visual inspection has become a prevailing trend in both academia and industry. However, automation-enabled applications often encounter uncertainty problems. For automation-enabled façade visual inspection, uncertainty in reliability and efficiency is an important factor that determines the value of introducing automation to façade visual inspection. During automation-enabled façade visual inspection, human efforts play important roles throughout the whole process and compose a human-cyber-physical system. Therefore, human-related activities and human factors are prominent causes of uncertainty in automation-enabled façade visual inspection. To understand human-related uncertainty, this work designed a Delphi study with an expert panel to quantitatively evaluate human-related activities and human factors. Also, an optimized fuzzy Delphi method was adopted to process the collected evaluation opinions. Based on the results, the most critical activities and human factors influencing uncertainty were extracted. Additionally, a structure of uncertainty generation was developed to analyze the evaluation results and provide recommendations for uncertainty control. This research contributes to facilitating understanding of the uncertainty problem in human-cyber-physical systems and to providing effective recommendations for uncertainty control in automation-enabled façade visual inspection.
... Usage and operation for any civil infrastructure are dynamic processes that vary continuously based on environmental and human activities [46]. Maintaining or restoring the same level of functionality will rely on used materials for rehabilitation and strengthening existing infrastructure. ...
Construction material technologies are the driving force for enhancing and improving building and infrastructure functionality. Solutions for many challenges faced by the construction industry, such as low energy efficiency and high carbon emission, had sparkled from innovative construction materials technologies. Hence, selecting adequate construction materials will play an essential role in shaping the life cycle for infrastructure (“from cradle to cradle”) and consequently the project cost. This paper illustrated that innovation in construction materials could occur on various levels, including compositions selection, production technology and end-of-life options. Hence, construction materials roles in achieving civil infrastructure sustainability extend beyond satisfying performance criteria to shaping their whole life cycle. This needs a shift in specifications and guidelines for materials selections, design and rehabilitation concepts for new and old infrastructure, and accurate prediction models to simulate the performance.
For determining the effectiveness of area-based infrastructure management, a comprehensive measurement to implement and develop an infrastructure project would need to be integrated in holistic and knowledge management. The main objective of this study was to identify the keys to measurement the sustainability of area-based infrastructure project (AIP) management. First, the key performance indicators were reviewed and selected. Next, the interviews with project developers and managers of the pilot site were conducted to investigate the real context of significant keys. The discussion with five experts who have relevant experience in area-based infrastructure projects management then arose to validate possible key performance indicators. Based on the expert comments, the questionnaire was revised for its validity and clarity. Then, the data collection using a questionnaire was sent out to representative samples across Thailand. Later, there was the analysis to interpret the survey results. The results revealed that there are two groups of keys to measure the sustainability of AIP management. One was the “Holistic Management (HM)”, which consisted of seventeen components. The other was the “Knowledge Management (KM)”, which involved six similarity items. It was found that the aforementioned factors can explain 70.024% of the total cumulative variance in the entire datasets compiled for the assessment of keys to measurement the sustainability of AIP management. The main outcomes from this study can be beneficial for the development of effective and good governance strategies for achieving sustainability patterns of area-based infrastructure project management. This theory offers the understanding of how the effectiveness of key measurement of AIP management can drive sustainable development project s, which can help in project manager and developer research on identity-related transitions.
