Search

Summary: Structural performance assessment is a critical stage in the health monitoring for the maintenance and risk management of engineering structures, in which the interstory drift is a key indicator. In this paper, a robust adaptive Kalman filter is proposed for an interstory drift estimation problem to show the health condition of steel structures in the case that the statistics or internal dynamics describing the signals and measurements are not known precisely. More precisely, we build an adaptive current Jerk model (ACJM) where the model parameters are updated in each time step to presuppose the statistics characterization of the steel dynamic, while the unknown measurement noise covariance is adapted based on a fixed‐lag innovation with respect to measurements. Moreover, a robust adaptive Kalman filter is designed for the modelling uncertainties in each time increment by solving a minimax game: one player tries to select an "actual" model far from the proposed ACJM with an exponential decay tolerance, while its "hostile" player, namely, the optimum filter, is designed by minimizing the estimation error according to the selected "actual" model. Finally, some simulation and experimental results show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Summary: Optimizing agroecosystems and crops for micronutrient uptake while reducing issues with inorganic contaminants (metal(loid)s) is a challenging task. One promising approach is to use arbuscular mycorrhizal fungi (AMF) and investigate the physiological, molecular and epigenetic changes that occur in their presence and that lead to changes in plant metal(loid) concentration (biofortification of micronutrients or mitigation of contaminants). Moreover, it is important to understand these mechanisms in the context of the soil microbiome, particularly those interactions of AMF with other soil microbes that can further shape crop nutrition. To address these challenges, a two‐pronged approach is recommended: exploring molecular mechanisms and investigating microbiome management and engineering. Combining both approaches can lead to benefits in human health by balancing nutrition and contamination caused by metal(loid)s in the agro‐ecosystem. [ABSTRACT FROM AUTHOR]

This research paper presents a comprehensive study on optimizing the critical artificial intelligence (AI) factors influencing cost management in civil engineering projects using a multi-criteria decision-making (MCDM) approach. The problem addressed revolves around the need to effectively manage costs in civil engineering endeavors amidst the growing complexity of projects and the increasing integration of AI technologies. The methodology employed involves the utilization of three MCDM tools, specifically Delphi, interpretive structural modeling (ISM), and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). A total of 17 AI factors, categorized into eight broad groups, were identified and analyzed. Through the application of different MCDM techniques, the relative importance and interrelationships among these factors were determined. The key findings reveal the critical role of certain AI factors, such as risk mitigation and cost components, in optimizing the cost management processes. Moreover, the hierarchical structure generated through ISM and the influential factors identified via MICMAC provide insights for prioritizing strategic interventions. The implications of this study extend to informing decision-makers in the civil engineering domain about effective strategies for leveraging AI in their cost management practices. By adopting a systematic MCDM approach, stakeholders can enhance project outcomes while optimizing resource allocation and mitigating financial risks. [ABSTRACT FROM AUTHOR]

This study addresses the imperative of enhancing design flexibility in large-scale infrastructure projects to effectively navigate emerging uncertainties. Current appraisal methods often prioritize risk mitigation over uncertainty management, which hinders the implementation of flexible designs. Instead, this research explores the integration of design flexibility through real options reasoning (ROR) to create flexible infrastructures. As a result, a conceptual framework is proposed that draws from contemporary industrial practices such as modularization and project safeguards. Modularization, grounded in the alignment of functions and components, bolsters flexibility. Project safeguards, operationalized as passive or active measures, embed options in project outcomes. A proposed method emerges that synthesizes real options, modularization, and project safeguards into four guiding steps and nine helpful heuristics. These steps, enriched by heuristics, offer a structured approach to grasp, strategize, and implement design flexibility, transforming it from theory to impactful project management. Case-based and numerical validation was conducted to validate the proposed method. The proposed approach is illustrated through a case study of Heathrow Airport's £14 billion expansion. The research implications include the exploration of improved appraisal methods for large infrastructure projects, the advancement of ROR and application of heuristics in engineering management, and additional investigation into the integration of modularization and safeguards to enhance design flexibility in uncertain environments. This study presents a new methodological approach that offers a structured framework for effectively communicating the advantages of design flexibility in managing uncertainty during the appraisal process of large-scale infrastructure projects. The approach empowers project leaders to tackle uncertainties intrinsic to large-scale projects. It fosters design flexibility's benefits, guides project selection aligned with growth goals, and manages uncertainties by conceptualizing projects as sequenced steps. This method comprises four sequential steps, each pivotal for enhancing design flexibility. It begins by translating strategic needs into tangible options, enhancing adaptive decision-making. The subsequent step involves selecting optimal product types and modularization options to fortify flexibility. Operationalizing risk, modularization, and safeguards in the third step cements flexibility into project architecture. The final step involves cost–benefit analysis, aiding informed investment decisions. These steps, enriched by simple heuristics, offer a structured approach to grasp, strategize, and implement design flexibility, transforming it to impactful project management. Ultimately, this approach guides projects toward a more adaptive future. The proposed approach is ideal for intuitive executive decision-making through practical shortcuts, particularly when analytical modeling faces limitations. [ABSTRACT FROM AUTHOR]

Developing real-time automatic intuitive devices based on electroencephalography (EEG) to improve hazard recognition performance (HRP) is essential in construction engineering management. However, existing studies generally developed devices based on unimodal data, failing to distinguish the role played by brain activities in different visual areas and ignoring the possible existence of other psychological mechanisms. Therefore, this study aims to use multimodal data based on brain-eye synergy to predict HRP and reveal the cognitive mechanism of hazard recognition to develop intuitive hazard recognition devices. Accordingly, we designed experiments to investigate this problem: (1) 53 construction workers completed a 120-trial hazard recognition task in a laboratory environment (N=6,360), (2) an EEG recorder collected the participants' EEG activities and an eye-tracking device recorded the data of eye movement, and (3) the power spectral density (PSD) of EEG was calculated and then imported into Matlab2018a in combination with EEGLAB for analysis. The results showed that the prediction results of hazard recognition have a time-variant effect. For fall-related hazards, EEG signals within 0–320 ms after the onset were significant in predicting HRP; for electric-related and fire-related hazards, EEG signals within 560–640 ms after the starting point were the most valuable. Also, compared with fall-related hazards, it took a significantly longer time to recognize electric-related and fire-related hazards, which consumed more attentional resources. Finally, the frontal lobe requires more attentional resources to finish cognitive activities than the occipital lobe to finish visual information processing activities, and the occipital lobe needs to process visual information first, and then transmit it to the frontal lobe for analysis and judgment. For the body of knowledge, this study, finding the time-variant effect of HRP, improves the theory of construction hazard recognition from an eye-brain synergy perspective and provides guidance for developing intuitive hazard recognition devices and improving management performance. This study combined eye-movement data and EEG data to predict the performance of hazard recognition using multimodal data. Not only did this study reveal the mechanism of hazard recognition, but it also proposed suggestions for project management and device development. Managers are suggested to implement refined management and regularly monitor the physiological signals of employees. Some construction companies started to use smart devices to measure workers' arousal, emotional valence, emotional regulation ability, fatigue, workload, or safety attention level before work every day. And it is recommended to provide safety reminders to employees based on whether there are any abnormalities in their physiological indicators. In addition to project management, the results of this study can serve the development or optimization of hazard recognition equipment. Developing devices cannot only consider EEG information, but also combine EEG information with eye-movement information. Meanwhile, developers can also consider different types of hazards and different brain regions to improve device performance. [ABSTRACT FROM AUTHOR]

Changes and modifications to existing products, known as engineering changes (EC), are common in complex product development. They require appropriate implementation planning and supervision to mitigate the economic downsides due to complexity. These tasks, however, take a high administrative toll on the organization. In response, automation by computer tools has been suggested. Due to the underlying process complexity, the application of artificial intelligence (AI) is advised. To support research and development of new AI-artifacts for EC management (ECM), a knowledge base is required. Thus, this paper aims to gather insights from existing approaches and discover literature gaps by conducting a systematic literature review. 39 publications applying AI methods and algorithms in ECM were identified and subsequently discussed. The analysis shows that the methods vary and are mostly utilized for predicting change propagation and knowledge retrieval. The review's results suggest that AI in EC requires developing distributed AI systems to manage the ensuing complexity. Additionally, five concrete suggestions are presented as future research needs: Research on metaheuristics for optimizing EC schedules, testing of stacked machine learning methods for process outcome prediction, establishment of process supervision, development of the mentioned distributed AI systems for automation, and validation with industry partners. [ABSTRACT FROM AUTHOR]

Over the last 30 years, the last planner system (LPS), as a Lean-driven production planning and control tool, has consistently demonstrated its capability to improve planning reliability and overall performance in construction projects. Recent research provides evidence suggesting that the LPS ability to do good to projects has not reached its full potential yet. In fact, it has been observed that by identifying the missing social mechanisms in its implementation process and managing more effectively the complex socio-technical nature of the LPS, its implementation effectiveness is improved. However, commonly used research approaches, such as the case study method, have been criticized for lacking scientific control, replicability, and external validity. Alternatively, immersive virtual reality (IVR)-based simulation games have the methodological capability to enable the study of the social mechanisms that the LPS engenders when implemented in projects, by providing a highly controlled, reproducible, and ecologically valid experimental environment. However, how to leverage effectively IVR-based gaming technology to study the social mechanisms that the LPS engenders in construction organizations is not well understood. In order to bridge this gap, this paper established the conceptual and technical foundations to set IVR-based experimental environments and developed an IVR prototype encompassing nonlean and LPS-based rounds. The evaluation of the IVR prototype involved both quantitative and qualitative research approaches, focusing on its functionality, user-perceived presence and usability, task performance, and lean expert feedback. The results demonstrated the prototype's capability to create a controlled experimental environment while offering participants a user-friendly, immersive, and realistic LPS simulation experience. The contributions of this study are twofold: (1) the addition of a new method to the research methodology toolbox in construction engineering and management; and (2) the generation of design insights to develop IVR prototypes for research in construction engineering and management, and preliminary evidence for its ecological validity. [ABSTRACT FROM AUTHOR]

The rise of extreme precipitation events has attracted wide attention throughout the world. However, the analysis of precipitation extremes is not sufficient in the short period of instrumental observation and it is complicated by their non‐stationary behaviours. As a period dominated by natural forcing, the last millennium is helpful for understanding long‐term changes in precipitation extremes. Therefore, this study aimed to (1) investigate changes in daily and subdaily precipitation extremes in China from the last millennium to the end of the twenty‐first century, (2) detect the non‐stationarity of precipitation extremes and (3) compare design storms by using stationary and non‐stationary distributions. The annual maximum 1‐day precipitation (RX1day) and annual maximum 3‐hour precipitation (RX3hour) are used to quantify variations of precipitation extremes. The results show that the trend of RX1day was insignificant for the Medieval Climate Anomaly (MCA, 950–1250) and the Little Ice Age (LIA, 1500–1800). For the historical period (1850–2014), the trend of RX1day and RX3hour was insignificant in most subregions. In addition, the differences for RX1day between the historical period and the MCA/LIA were mostly in the range of −5% to 5%. For the future period (2015–2100), RX1day and RX3hour are projected to increase in almost all land grids compared with the historical period, and they show greater increases in western China than eastern China. The non‐stationarity of RX1day and RX3hour is mainly found under the high‐emission scenario. Moreover, stationary generalized extreme value (GEV) distribution underestimates 50‐year return levels for RX1day and RX3hour in most areas compared with non‐stationary GEV distribution under the high‐emission scenario. Overall, this study suggests that daily and subdaily extreme precipitation will intensify over China in the future. For design storms, non‐stationary methods need to be used for risk management and engineering design under the high‐emission scenario. [ABSTRACT FROM AUTHOR]

Some studies have shown that any part machined by an outsourcer usually has several basic quality characteristics. When the outsourcer's process capabilities are insufficient, the defective rate of various quality characteristics of the product will increase, thereby raising the rework rate and scrap rate. As a result, maintenance costs will go up, economic value will decrease, and even carbon emissions can increase during the production process. In addition, the process capability index and the radar chart are widely used in engineering management and other fields. Since process indicators often contain unknown parameters, sample data are needed for evaluation. With the rapid development of the Internet of Things and big data analysis, many companies regard rapid response as a basic requirement for timeliness and cost consideration. Therefore, companies often have to evaluate the process quality of ten small samples and decide whether to make some improvements. In order to solve the above problems, this study proposed a fuzzy radar chart evaluation model for the process quality of multi-quality characteristic parts based on the process capability index. Using this model can help all parts manufacturers continue to improve the quality of their machined parts as well as reduce their rework and scrap rates. Meanwhile, carbon emissions can be lessened during the production process, and companies can fulfill their social responsibilities. This fuzzy radar chart evaluation model is based on confidence intervals. As the company's past experience is incorporated, the evaluation accuracy can be maintained even with a smaller sample size. Furthermore, the fuzzy radar evaluation chart can simultaneously evaluate the process capabilities of all quality characteristics of the part. In addition to making it easier for manufacturers to master all quality characteristics, quality process capability can also help them seize improvement opportunities. [ABSTRACT FROM AUTHOR]

Military program management and system engineering re quire the expression of costs and delay trade‐off with respect to system architecture. If architecture frameworks (AF) such as NATO (NAF) were designed to fill this common need, their current state is essentially descriptive. As it turns out, building defense systems architectures using those frameworks in a properly anticipated cost/delay budget envelope would require to have all system engineering already solved, because the architecture frameworks are designed to provide an explicit representation of the operational domain that can be used in analysis, for articulation of issues and requirements, as support to planning, and as a means of solution design and validation, among other things. Thus Quality‐Resource‐Time optimality in a regularly evolving environment cannot be represented in acceptable delay without automated optimization assistance. Our contribution in this article explores coupling architecture framework with operation research (OR) models to enable computer assisted design and evaluation of heterogeneous views in NATO Architecture Framework (NAF). Our illustrative example is a Linear Programming based bridge between program management and system engineering to anticipate optimal trade‐offs. This article presents promising results, with which we hope to show how OR and AF will be indivisible in architecture evaluation process. [ABSTRACT FROM AUTHOR]

The integration of computer vision technology into construction sites poses various challenges due to the complex environment. Prior studies on computer vision related to heavy construction equipment has primarily focused on a limited range of equipment types provided in standard databases, such as the Microsoft Common Objects in Context (MS COCO) data set. The conventional approach has limitations in capturing the diverse working conditions and dynamic environments encountered in real construction sites. To overcome the challenge, this study proposes a new benchmark model for the automated detection and classification of a wide range of heavy construction equipment (i.e., nine representative types) commonly used in construction sites by using a deep convolution neural network. This study was conducted in four steps: (1) data collection and preparation, (2) data transformation, (3) model training, and (4) model validation. The proposed you only look once (YOLO)v5l (large, YOLOv5 with a larger network) model demonstrated high reliability, achieving a mean average precision (mAP)_0.5∶0.95 of 90.26%. This study makes a significant contribution to the domain of construction engineering and management by providing a more efficient and systematic management system to proactively prevent heavy equipment–related safety accidents with diverse working conditions and dynamic environments encountered at construction sites. Moreover, the proposed approach can be extended to integrate advanced techniques such as case-based reasoning, digital twin, and blockchain, allowing for the automated activity recognition in various occlusions, the carbon emissions monitoring and diagnostics of heavy equipment, and a robust real-time construction management system with enhanced security. [ABSTRACT FROM AUTHOR]

In response to the evolving landscape of the modern era, the requirements for engineering audit have undergone significant changes. To achieve efficient audit tasks and obtain accurate and reliable results, the integration of machine learning and wireless network technology has become essential, leading to the emergence of digital and information-based audit modes. This paper focuses on the development of a digital audit system that combines engineering audit management fusion with machine learning and wireless network technology. Such an approach reflects the dynamic shift in internal audit functions and objectives, providing clear guidelines for the future of digital audit management. By harnessing the power of machine learning and wireless networks, the digital audit system effectively addresses challenges associated with data management, sharing, exchange, and security during the audit process. Through seamless integration, it enables comprehensive electronic and digital management of internal and audit business processes. This research explores the platform's functionalities and its potential application, using actual audit data for analysis. The proposed digital audit system showcases superior real-time data querying performance, heightened accuracy in checks, and enhanced retrieval capabilities. The simulation results validate the system's efficacy, highlighting its ability to deliver true and dependable audit outcomes. By embracing digital transformation, the engineering audit field can harness the potential of cutting-edge technologies, thus paving the way for a more efficient, reliable, and future-ready approach to audit management. [ABSTRACT FROM AUTHOR]

Recently, Annadurai published an important paper in International journal of Management Science and Engineering Management for the optimal replenishment policy for inventory models of decaying items with shortages and salvage value. However, his solution procedure contained some questionable results. The purpose of this note is fourfold. First, we recap the inventory model proposed by Annadurai. Second, we provide our improvement. Third, we point out questionable results of Annadurai and then offer our revisions. Furth, we consider two boundary minimums that will be an improvement for the no shortage case and no stock case, respectively. [ABSTRACT FROM AUTHOR]

In engineering blasting, while efficiently breaking rocks with explosives, a large amount of toxic and harmful gases are generated, which not only pollutes the production environment but also easily leads to explosion smoke poisoning accidents. It must be highly valued by engineering technicians and management personnel. To effectively control the production of harmful gases during explosive blasting, an environmentally friendly and efficient harmful gas inhibitor has been developed, and its mechanism of action has been analyzed and revealed. Through model and on-site experiments, the appropriate addition ratio and charging structure scheme were determined, and good control effects were achieved. The research results indicate that the environment in which explosives are used has a significant impact on the composition of harmful gases produced during blasting. CO, NO, and NO2 are mainly produced in natural air environments, while NH3, CO, and NO are mainly produced in underground blasting environments. As the proportion of inhibitors added increases (2%, 4%, 6%), the decrease in the concentration of harmful gases during blasting first increases and then decreases. Compared with the control experiment, the total reduction rate of harmful gas concentration is 39.23%, 68.20%, and 59.69%, respectively, and the best control effect is achieved when 4% is added. When using the developed inhibitor adding device for the full hole addition scheme, the control effect of harmful gas concentration in blasting is the best, and the decrease in harmful gas concentration reaches 62.79%~84.73% at a distance of 30m~120m. The use of harmful gas inhibitors for blasting combined with other control measures can significantly improve the blasting operation environment, enhance the safety level of production operations, and have good promotion and application value. [ABSTRACT FROM AUTHOR]

The present study uses nine machine learning (ML) methods to predict wave runup in an innovative and comprehensive methodology. Unlike previous investigations, which often limited the factors considered when applying ML methodologies to predict wave runup, this approach takes a holistic perspective. The analysis takes into account a comprehensive range of crucial coastal parameters, including the 2% exceedance value for runup, setup, total swash excursion, incident swash, infragravity swash, significant wave height, peak wave period, foreshore beach slope, and median sediment size. Model performance, interpretability, and practicality were assessed. The findings from this study showes that linear models, while valuable in many applications, proved insufficient in grasping the complexity of this dataset. On the other hand, we found that non-linear models are essential for achieving accurate wave runup predictions, underscoring their significance in the context of the research. Within the framework of this examination, it was found that wave runup is affected by median sediment size, significant wave height, and foreshore beach slope. Coastal engineers and managers can utilize these findings to design more resilient coastal structures and evaluate the risks posed by coastal hazards. To improve forecast accuracy, the research stressed feature selection and model complexity management. This research proves machine learning algorithms can predict wave runup, aiding coastal engineering and management. These models help build coastal infrastructure and predict coastal hazards. [ABSTRACT FROM AUTHOR]

Controlling heat flow is a key challenge for applications ranging from thermal management in electronics to energy systems, industrial processing, and thermal therapy. However, progress has generally been limited by slow response times and low tunability in thermal conductance. In this work, we demonstrate an electronically gated solid-state thermal switch using self-assembled molecular junctions to achieve excellent performance at room temperature. In this three-terminal device, heat flow is continuously and reversibly modulated by an electric field through carefully controlled chemical bonding and charge distributions within the molecular interface. The devices have ultrahigh switching speeds above 1 megahertz, have on/off ratios in thermal conductance greater than 1300%, and can be switched more than 1 million times. We anticipate that these advances will generate opportunities in molecular engineering for thermal management systems and thermal circuit design. [ABSTRACT FROM AUTHOR]

Today's fast-changing environment for construction companies requires rapid responses and adaptation of their projects. Despite the multitude of tools applied for project cost management in engineering and construction companies, there is a need to form comprehensive solutions. The purpose of the study is to form a methodological approach to project cost management in the field of engineering construction based on alternative models to diagnose the development, assessment and selection of functional areas and content of cost management in the construction project, which allows one to increase adaptability and flexibility in the process of its implementation. The basis of research methodology is modeling, which allows one to adjust the economic and financial flows based on three S-curves, one for each component of the total cost of the work: direct costs, indirect costs and reserves. These curves include the direct cost curve for the main purchasing packages as well. This brings financial flows closer to reality because it is possible to adjust the S-curves according to the behavior of each subsystem. The contribution of the study is the proposed approach of integrating concepts related to the coordination and development of project design and production management (lean construction), forming a "3D model of management", in a broad and comprehensive management system. It assumes a comprehensive and complete way to manage civil engineering projects. The proposed methodological approach can make a significant contribution to the preparation of forecasts and estimates by planners and controllers in the context of construction projects. [ABSTRACT FROM AUTHOR]

This research navigates the application of simulation meta-modeling in understanding and controlling assembly line dynamics. Aimed at unveiling the relationship between input factors and output parameters in a production system, a precise meta-model was devised based on real system data and simulation statistics. The meta-model predicts parameteR-values within a verified validity range, simplifying complex variable relationships for enhanced decision-making in intricate systems. The accuracy of the model was notable within the tested range of CONWIP cards from 1 to 25, showcasing reliable output approximation. This study highlights the classical approach to simulation meta-modeling as cumbersome, propelling the quest for further simplification in analyzing complex production systems. The utilization of simulation meta-modeling emerged as a pivotal tool for validating complex simulation models and swiftly testing system sensitivity to input factor alterations. Noteworthy findings include the identification of an optimal number of CONWIP cards for maximizing throughput without excessive increases in Work-In-Progress or throughput time. The research also underscores the potential of 5th-degree polynomial models in approximating production performance and throughput time accurately, offering robust tools for informed decision-making. This venture marks a significant stride towards a more streamlined, accurate, and efficient analysis of complex production systems, showcasing the promising applicability of simulation meta-modeling in industrial engineering and production management. [ABSTRACT FROM AUTHOR]

Geostatistical techniques are applied to examine the life cycle of a mineral deposit. There are two main classes of geostatistical techniques: (1) deterministic techniques that include kriging and cokriging for a single best estimate, and (2) probabilistic techniques that include simulation, which infer probability distributions and simulate realizations to transfer multivariable and multilocation uncertainty through to larger-scale resource and reserve uncertainty. Probabilistic techniques are newer and more powerful in that they provide access to quantitative measures of uncertainty and models with correct spatial variability; however, they have not seen widespread application in all aspects of the life cycle of mines. Workflows and methodologies for the appropriate use of deterministic and probabilistic techniques have been discussed. Software, engineering practices and management expectations limit some applications. Applications have been reviewed, and enhancements are required to realize the full potential of geostatistical techniques, which have been discussed with examples. [ABSTRACT FROM AUTHOR]

Semitransparent photovoltaic (STPV) solar cells offer an immense opportunity to expand the scope of photovoltaics to special applications such as windows, facades, skylights, and so on. These new opportunities have encouraged researchers to develop STPVs using traditional thin‐film solar cell technologies (amorphous‐Si, CdTe, and CIGS or emerging solar cells (organic, perovskites, and dye‐sensitized). There are considerable improvements in both power conversion efficiency (PCE) and semitransparency of these STPV devices. This review studies the device structure of state‐of‐the‐art STPV devices and thereby analyzes the different approaches toward maximizing the product of PCE and average visible transmittance. The origins of PCE losses during the opaque‐to‐semitransparent transition in the different STPV technologies are discussed. In addition, critical practical aspects relevant to all STPV devices, such as compatibility of the top transparent electrode with the device structure, buffer layer optimization, light management engineering, scale‐up, and stability, are also reported. This overview is expected to facilitate researchers across different technologies to identify and overcome the challenges toward achieving higher light utilization efficiencies in STPVs. [ABSTRACT FROM AUTHOR]

Harmful algal and cyanobacterial blooms (HABs), occurring in inland and maritime waters, pose threats to natural environments by producing toxins that affect human and animal health. In the past, HABs have been assessed mainly by the manual collection and subsequent analysis of water samples and occasionally by automatic instruments that acquire information from fixed locations. These procedures do not provide data with the desirable spatial and temporal resolution to anticipate the formation of HABs. Hence, new tools and technologies are needed to efficiently detect, characterize and respond to HABs that threaten water quality. It is essential nowadays when the world's water supply is under tremendous pressure because of climate change, overexploitation, and pollution. This paper introduces Discrete Event System Specification-BLOOM, a novel framework for real-time monitoring and management of HABs. Its purpose is to support high-performance hazard detection with model-based system engineering and cyber-physical systems infrastructure for dynamic environments. [ABSTRACT FROM AUTHOR]

The Journal of Management in Engineering (JME) is celebrating its 40th anniversary as a premier academic journal in the project engineering and construction management field. The journal initially focused on the importance of managerial issues in the engineering and construction industry and has since evolved to cover a wide range of topics. The journal has achieved a commendable impact factor and continues to attract diverse papers from academia and industry. JME has added new associate editors and editorial board members to enhance its expertise and has introduced special collections on important and emerging topics. The journal also utilizes social media platforms to engage with a wider audience. The editor in chief expresses gratitude to all contributors and welcomes feedback and suggestions for future research. [Extracted from the article]

Analyzing monitoring data to recognize structural anomalies is a typical intelligent application of structural safety monitoring, which is of great significance to hydraulic engineering operational management. Many regression modeling methods have been developed to describe the complex statistical relationships between engineering safety monitoring points, which in turn can be used to recognize abnormal data. However, existing studies are devoted to introducing the correlation between adjacent response points to improve prediction accuracy, ignoring the detrimental effects on anomaly recognition, especially the pseudo-regression problem. In this paper, an anomaly recognition method is proposed from the perspective of causal inference to realize the best exploitation of various types of monitoring information in model construction, including four steps of constructing causal graph, regression modeling, model interpretation, and anomaly recognition. In regression modeling stage, two deconfounding machine learning models, two-stage boosted regression trees and copula debiased boosted regression trees, are proposed for recovering the causal effects of correlated response points. The validation was carried out with Shanmen River culvert monitoring data, and experiment results showed that the proposed method in this paper has better anomaly recognition compared to existing regression modeling methods, as shown by lower false alarm rates and lower averaged missing alarm rates under different structural anomaly scenarios. [ABSTRACT FROM AUTHOR]

During the last two decades, although China has made remarkable progresses and achievements in space industry, there is still a considerable gap in side of technology and management between other space powers. In order to improve the international competitiveness of Chinese space and speed up the construction of Chinese space security and technology safety, it is urgent to evaluate and optimize the efficiency of space engineering management as the important basis for decision-making and management. With the connotation of spacecraft engineering management, this paper selects balanced scorecard (BSC) for spacecraft engineering management evaluation. The method designs a kind of a four-dimensional evaluation index framework with concrete items, which integrates characteristics of aerospace into the traditional dimension of value, customer, operational management process, growth and development, and combines index weight and expert estimation. Finally, this paper lists out evaluation cases as summarization of BSC problems and concerns, and proposes space project management evaluation assumption, hoping it can help space engineering moving in the direction of high quality, high efficiency and high benefit. [ABSTRACT FROM AUTHOR]

This paper introduces a Python application for visualizing an imbricate thrust system. The application uses the traditional geologic information to create an HTML geological map with real topography and a set of geological cross-sections with the essential structural and stratigraphic elements. On the basis of the high geological knowledge gained during the last three decades, the Palomeque sheets affecting the Cenozoic Malaguide succession in the Internal Betic Zone (SE Spain) were selected to show the application. In this area, a Malaguide Cretaceous to Lower Miocene succession is deformed as an imbricate thrust system, with two thrusts forming a duplex, affected later by a set of faults with a main strike-slip kinematic. The modeled elements match well with the design of the stratigraphic intervals and the structures reported in recent scientific publications. This proves the good performance of this Python application for visualizing the structural and stratigraphic architecture. This kind of application could be a crucial stage for future groundwater, mining, and civil engineering management. [ABSTRACT FROM AUTHOR]

The evaluation of wave run-up on beaches is of significant importance since it is used in different coastal engineering and management applications, including the assessment of beach morphology, the determination of erosion and flooding risk, and the design and maintenance of coastal structures. The main aim of this paper is to present a new method of wave run-up prediction, which is intended to be applicable to the various types of natural beaches. A single formulation was established for beaches, which were categorised as dissipative, reflective and intermediate. This comprehensive parametric run-up model, which included both wave set-up and swash excursion, was specifically developed to represent the wide-ranging conditions experienced in the swash zone in terms of beach conditions. The run-up model included a means to account for obliquely incident waves, varying foreshore sediment characteristics and the infiltration/exfiltration processes during swash events; it also used fitting parameters to account for other site-specific conditions. The novel run-up model produced comparable results to other parametric models, and showed good correlation with laboratory data and limited field data. However, while the model was deemed suitable to be adopted at varying coastal sites, field data are required for implementation to yield meaningful output. [ABSTRACT FROM AUTHOR]

Characterization of karst systems and forecast of their state variables are essential for groundwater management and engineering in karst regions. These objectives can be met by the use of process-based discrete-continuum models (DCMs). However, results of DCMs may suffer from inversion nonuniqueness. It has been demonstrated that the joint inversion of observations regulated by different natural processes can tackle the nonuniqueness issue in groundwater modeling. However, this has not been tested for DCMs thus far. This research proposes a methodology for the joint inversion of hydro-thermo-chemo-graphs, applying to two small-scale sink-to-spring experiments at Freiheit Spring, Minnesota, USA. In order to address conceptual uncertainty, a multimodel approach was implemented, featuring seven mutually exclusive variants. Spring hydro-thermo-chemo-graphs, for all the variants simulated by MODFLOW-CFPv2, were jointly inverted using a weighted least squares algorithm. Subsequently, models were compared in terms of inversion and forecast performances, as well as parameter uncertainties. Results reveal the suitability of the DCM approach for simultaneous inversion and forecast of hydro-physico-chemical behavior of karst systems, even at a scale of meters and seconds. The estimated volume of the tracer conduit passage ranges from approximately 46–51 m3, which is comparable to the estimate from the flood-pulse method. Moreover, it was demonstrated that the thermograph and hydrograph contain more information about aquifer characteristics than the chemograph. However, this finding can be site-specific and should depend on the analysis scale, the considered conceptual models, and the hydrological state, which are potentially affected by minor unaccountable processes and features. [ABSTRACT FROM AUTHOR]

The ongoing research in the field of decision-making can be analyzed from different perspectives. Research trends indicate that multi-attribute decision-making (MADM) methods have a significant impact on engineering and management scientific areas. Since many of the problems existing in the mentioned areas are associated with a certain level of uncertainty, type 2 fuzzy sets represent a common solution for the enhancement of conventional MADM methods. In this way, the decision-makers are encouraged to use linguistic expressions for the assessment of attributes' relative importance and their values. The purpose of this paper is to review a determination of attributes' relative importance, and their values, as well as the extension of ranking methods with type 2 fuzzy sets. The papers are systematically adjoined to groups consisting of hybrid models with the following characteristics: (1) indicating the procedure for modeling attribute relative importance and their values, (2) determining the extension of MADM methods with type 2 fuzzy sets to determine attributes' vector weights, and (3) the extension of MADM for attributes ranking with type 2 fuzzy sets. This study reviewed a total of 42 papers in the domain of engineering and management published from 2013 to 2023 in different journals indexed by the Springer, Science Direct, Emerald, Wiley, ProQuest, Taylor, and Francis research platforms. [ABSTRACT FROM AUTHOR]

BP neural network is the most representative computational and research method of current machine learning, BP neural network has gradually developed into the most widely used and now the most widely used in the industry with its powerful nonlinear feature function mapping computing capabilities, good data induction and feature recognition computing capabilities. This paper closely links the actual activities of project engineering technology management with modern machine learning network technology, and proposes a new type of engineering management activity model based on machine-integrated GABP neural network. Based on artificial intelligence technology, this paper discusses the theory of analytical neural learning in depth, discusses the theory of analytical neural learning in combination with genetic algorithms, and systematically discusses the models commonly used in this study based on theoretical foundations such as genetic algorithms. And points out the defects and defects of the neural network structure itself. Good generalization ability, in order to minimize the impact of subjective factors on the valuation results. Although there are many management software specializing in construction project engineering currently on the market, the service scope and demand range of these management software specializing in construction project engineering are relatively narrow. For example, construction project management software can only be used manage a construction project. In the actual operation process, it is also found that unreasonable time arrangements often occur in various links, such as personnel scheduling and material distribution of engineering projects. It will not only cause a large waste of material resources, but also affect the effect of labor. To this end, starting from the government's desire to improve the efficiency of engineering management projects, this paper designs an engineering management system for general projects based on artificial intelligence technology and neural networks, and after a large number of practical analysis. [ABSTRACT FROM AUTHOR]

This paper presented a fuzzy Bayesian network (FBN) model for failure risk analysis of oil storage tank leakage. The Bow-tie model for storage tank failure in oil depots was established and used to determine key risk factors. Fuzzy set theory and expert judgments were used to calculate the prior probability of basic events, after which the model was mapped to a Bayesian network. The probability of storage tank failure was calculated as 7.80 E-02. As new evidence was obtained, probability updating was tested using forward inference. Posterior probability of a basic event, seal gasket aging, showed the greatest risk increase, from 1.05 E-02–1.10 E-01. Using these newly acquired key factors, probability adaptation was used to conduct a dynamic analysis of storage tank failures and the consequence states. The dynamic failure probability was found to increase by 28.67 %. The model underwent sensitivity analysis and literature data analysis. This fuzzy Bayesian network model is beneficial to dynamic risk analysis in oil depot management. The findings can be used to reduce the likelihood of oil tank failure and decrease the severity of accidents. Suggestions were made, for site engineers and management, to reduce the risk of hazardous events. [ABSTRACT FROM AUTHOR]

This paper describes goals, process, and results in the effort to introduce new curricula at The Faculty of Technical Sciences of University Mother Theresa - Skopje fully devoted to teach students in the direction of sustainable development. The idea was born in the process of reaccreditation of the old curricula named "Mechanical Engineering and management" at The Faculty of Technical Sciences. The authors of this paper, and the idea for reconstruction were fully aware of the direction Mechanical Engineering needs to go to contribute to the sustainable development of the country and wider. Therefore, that idea led the developing process into new curricula titled: "Sustainable Design in Mechanical Engineering". From total of 40 courses, 15-20 are fully related to sustainable development goals and the curricula includes also a number of projects and diploma work by which students will additionally have an opportunity to add to their understanding and knowledge of sustainable development tools. The new curricula successfully passed the process of accreditation and it is now a part of this University offer. Based on this curricula, a number of courses for lifelong learning will be developed. It is expected that this curricula will attract attention both of the new students, but also of the professionals at different companies, state and municipality institutions. [ABSTRACT FROM AUTHOR]

Structural damage detection techniques are gaining widespread attention in construction engineering and management. However, the scarcity of structural damage samples and the cross-task transferability of existing knowledge currently limit this technique in practical applications. Therefore, this paper proposes a novel framework for structural damage detection with large scope of cross-task learning capability that incorporates Bayesian estimation and variational inference into the deep learning backbones and Bayesian weight function into the outer loop process of metalearning. Experimental results demonstrate the superiority of this method for both structural damage image classification and structural damage semantic segmentation. Compared with existing frameworks, the proposed method can alleviate the negative influence of domain bias and reduce computation time and costs due to sample labeling. This paper also discusses how the proposed framework can be used to train a model of the structural damage detection framework in extreme cases. The framework and findings presented in this paper have important theoretical and practical contributions to the literature on vision-based structural damage detection. [ABSTRACT FROM AUTHOR]

Human behavior recognition has widespread applications in real life, especially in the infrastructure field. Modern engineering projects involve many construction units and engineering documents, many personnel, large scales, and large amounts of information. The traditional engineering management information system also has some defects. To strictly control the construction order and work efficiency of the infrastructure site, deal with emergencies in a timely manner, and reduce the burden on staff, we design a human abnormal behavior recognition system based on deep learning and dual-channel C3D. Our main aim is to conduct an in-depth study of human behavior recognition and apply it to the safety management of existing residential construction sites. In the methods section, we first introduce the convolutional neural network in deep learning, explain the principle of neural networks and two-channel convolution, and use the C3D model as the recognition model of the algorithm. The improved model combines the convolutional neural network to obtain our improved model. In the experimental section, the working process, experimental environment, and objects of our designed human abnormal behavior recognition system are classified. The analysis comprehensively analyzes the influence of system identification, the recall rate and accuracy rate of different algorithms, the misrecognition rate of different angles, dual-channel fusion, and the influence of duration on the prediction results. We chose three different angles for behavior recognition, and the results show that from a specific angle, our anomaly recognition rate is 98.01%; from a lateral point of view, our anomaly recognition rate is 97.27%; and from a top-down point of view, the recognition rate is 95.68%. Under three different video surveillance angles, the anomaly recognition rate of our proposed dual-channel 3D convolution algorithm reaches over 95%. [ABSTRACT FROM AUTHOR]

Nowadays, zenith telescope-based digital zenith cameras (DZC), such as the COmpact DIgital Astrometric Camera (CODIAC) and VErtical by STArs (VESTA), are used to determine highly precise astrogeodetic deflections of the vertical (DoVs). The CODIAC and VESTA were developed by Eidgenossische Technische Hochschule (ETH) Zurich and University of Latvia, respectively, and only two CODIACs and four VESTAs were produced. The CODIAC has an established accuracy higher than 0.05″, while the accuracy of VESTA is ~0.1″. These two DZCs, which are the most used DZCs of the last decade, were used effectively over many survey campaigns. In this study, we used both the CODIAC and VESTA to conduct simultaneous observations at the School of Management and Engineering Vaud (HEIG-VD) in Yverdon-les-Bains, Switzerland, over five nights in August 2021. Our DZC measurements with CODIAC and VESTA mark the second time that simultaneous parallel observations were made with two different DZCs. Additionally, the VESTA was never tested against another DZC through comparative simultaneous measurements. These comparative measurements between the VESTA and CODIAC allowed for VESTA precision validation and checking the agreement between the two DZCs. The results of repeated, comparative DoV observations over five nights at HEIG-VD revealed a DoV measurement precision of VESTA around 0.13–0.16″ for 15 min long observation session and 0.10–0.13″ for 50 min long observation session. Mean DoV differences between CODIAC and VESTA at HEIG-VD were 0.08″ and −0.06″ for the North–South and East–West components, respectively. [ABSTRACT FROM AUTHOR]

The article presents the principles of sozology formulated by Walery Goetel and examples of their development in the scientific and research works as well as implementation and teaching work performed at the Faculty of Civil Engineering and Resource Management at AGH University of Science and Technology. These works are aimed at the rational management of mineral deposits and its extraction, as well as at the creation of utility values of the post-mining areas. It has also been shown that the work carried out at the faculty has gone far beyond mining activities using the experience gained from them. This has included, for example, the preservation and accessibility of the subsoil of medieval towns and the revitalization of towns with a predominantly industrial character. Work has also been conducted to rehabilitate sites and facilities for additional public use. The most important directions of solutions in the field of the reclamation and revitalization of post-mining areas developed with the participation of the faculty staff as well as in cooperation with national and international researchers are in this article. The continuous development of the Faculty is also reflected in its name changes: from the Faculty of Mining, through the Faculty of Mining and Geoengineering, to the Faculty of Civil Engineering and Resource Management. The current name reflects the actual scope of the subject matter undertaken in research and teaching, specifically the management of natural mineral deposits and also of secondary resources that should be reused in a closed cycle. [ABSTRACT FROM AUTHOR]