Planning Of Maintenance Activities Research Articles

Data-driven performance analysis of an active chilled beam air conditioning system: A machine learning approach for energy efficiency and predictive maintenance

This study aims to enhance energy performance estimation and maintenance planning for office buildings in Tehran. Machine learning and data mining techniques were employed, utilizing Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), Narrow Neural Networks, and optimizable SVMs. A comprehensive dataset of 2500 experimental samples was utilized with five input parameters (building size, occupancy patterns, environmental conditions, and ACB system parameters) and one output parameter (electricity consumption). GPR models performed better in estimating electricity consumption, achieving a Root Mean Square Error (RMSE) of 297.01 and an impressive R-squared value of 0.99. To optimize energy performance and design more energy-efficient buildings, this model accurately predicts energy consumption. Additionally, this study addressed maintenance planning for ACB systems, simulating maintenance issues with 2500 synthetic samples. The dataset included building age, size, number of floors, usage intensity, previous maintenance history, location, and environmental factors. Maintenance time was estimated using these features, and XGBoost was employed to predict ACB system maintenance time. Low MSE, RMSE, and R-squared values in the XGBoost model allow for accurate predictions. Using this model, facility managers can proactively plan maintenance activities, allocate resources, and minimize downtime. Through proactive measures, ACB performance and maintenance costs are optimized. The study accurately predicts ACB system energy consumption and maintenance time using the rational quadratic GPR model, applicable in Tehran and beyond. Additionally, MATLAB version R2022b was used for prediction, while DesignBuilder and EnergyPlus were utilized for technical analysis.

Modelling bridge deterioration using long short-term memory neural networks: a deep learning-based approach

PurposeBridge deterioration is a critical risk to public safety, which mandates regular inspection and maintenance to ensure sustainable transport services. Many models have been developed to aid in understanding deterioration patterns and in planning maintenance actions and fund allocation. This study aims at developing a deep-learning model to predict the deterioration of concrete bridge decks.Design/methodology/approachThree long short-term memory (LSTM) models are formulated to predict the condition rating of bridge decks, namely vanilla LSTM (vLSTM), stacked LSTM (sLSTM), and convolutional neural networks combined with LSTM (CNN-LSTM). The models are developed by utilising the National Bridge Inventory (NBI) datasets spanning from 2001 to 2019 to predict the deck condition ratings in 2021.FindingsResults reveal that all three models have accuracies of 90% and above, with mean squared errors (MSE) between 0.81 and 0.103. Moreover, CNN-LSTM has the best performance, achieving an accuracy of 93%, coefficient of correlation of 0.91, R2 value of 0.83, and MSE of 0.081.Research limitations/implicationsThe study used the NBI bridge inventory databases to develop the bridge deterioration models. Future studies can extend the model to other bridge databases and other applications in the construction industry.Originality/valueThis study provides a detailed and extensive data cleansing process to address the shortcomings in the NBI database. This research presents a framework for implementing artificial intelligence-based models to enhance maintenance planning and a guideline for utilising the NBI or other bridge inventory databases to develop accurate bridge deterioration models. Future studies can extend the model to other bridge databases and other applications in the construction industry.

Fuzzy FMEA-Resilience Approach for Maintenance Planning in a Plastics Industry ‎

The productivity and efficiency of industrial systems are highly affected by failures and machine breakdowns. Further, in asset-intensive industries, unexpected failures are considered the primary source of operational risk. In response, the maintenance department strives to calculate reliable estimates of the risk levels associated with such failures and develop resilient maintenance strategies that enable it to respond effectively to equipment failures. The research developed a framework for integrating fuzzy failure mode and effects analysis (FFMEA) with resilience engineering (RE) concepts for maintenance planning. The framework consists of four main stages: FFMEA, Risk iso-surface (RI), resilience assessment, and maintenance planning. In FFMEA, multiple sub-factors were considered for each main risk factor and evaluated using fuzzy logic. Then, in the RI stage, the risk priority number (RPN) was calculated through a fuzzy approach that considered the order of the importance of the main three risk factors. The fuzzy resilience assessment was applied through a survey of fifty-one questions related to the main four RE potentials to determine the need for resilient maintenance strategies. Finally, the RPN-Resilience diagram was employed to classify maintenance activities into six main maintenance strategies. A case study from a production line of plastic bags was used for illustration. The main advantage of the proposed FFMEA is that it divides the main risk criteria into sub-criteria to increase the accuracy of risk assessment and evaluate resilience potentials under fuzziness. In conclusion, the integration of the risk-resilience evaluation is a valuable tool for effectively planning maintenance activities.

Ground penetrating radar for moisture assessment in railway tracks: An experimental investigation

The health monitoring of railway networks offers a means to ensuring high-quality service, avoiding safety risks, and optimally planning maintenance actions to minimize life-cycle costs. Monitoring of the substructure is particularly linked to the tracking of degradation, which often stems from water infiltration, causing moisture accumulation in the underlying ballast layers. Such deterioration incurs substantial expenses in the order of several million per annum, while reducing the useful life of the track infrastructure. In optimizing management, it is important to improve detection schemes via affordable and non-invasive procedures. Such a solution is found in use of Ground Penetrating Radar (GPR) technology, which employs non-invasive radar pulses to map the subsurface, and possibly detect water infiltration. Train-based GPR systems thus offer tremendous potential for the development of preventive and automated monitoring of railway network infrastructure. Nevertheless, despite previous efforts in this direction, the technology remains relatively under-explored and lacks comprehensive studies to establish its suitability for this task. Moreover, there is no consensus on a standardized procedure for automatic inference of reliable indicators of railway health from GPR observations. In this work, we report on an extensive experimental analysis conducted on a controlled railway track, built by the Swiss Federal Railways, for this campaign. The humidity condition of the railway track was artificially altered to reach different levels of water content and GPR measurements were gathered under the varying conditions, with ground truth assessed through lab tests on collected samples. GPR data with complete ground truth labels deliver a rare benchmark, which can enhance understanding of this technology. Our findings show that GPR systems can effectively detect moisture infiltration in railway tracks, although several challenges are to be addressed for the development of accurate, automated procedures.

Thermal transfer analysis of machines used for the treatment of textile materials

The present paper aims to introduce a comprehensive thermal analysis of the sizing-finishing machine (Squeezing Pader Machine) to identify the overheating areas to organise maintenance activities. As there is no universal application for checking the condition of a machine, specific methods must be used depending on the characteristics of the machines being monitored, as machine condition monitoring is very important in terms of productivity, economic benefits and maintenance. The thermal analysis of the equipment in this work paper, has an important role (for planning maintenance activities), because this machine ensures optimal loading with different material treatment solutions, intended for mattresses, according to customer requirements. In the present paper, the authors conducted research on the whole technological flow to plan predictive maintenance activities using online monitoring methods. The mathematical modelling of the data obtained from the thermal analysis of the engines carried out based on the polynomial regression is used to predict the evolution of the temperatures along the analysed lines outside the measurement interval for each engine analysed. In this sense, the authors propose online monitoring of the motors that have the role of stretching and transporting the material, which is not included in the real-time temperature monitoring in the machine software, as the rest of the motors are monitored. Regular monitoring of motors with the help of thermography can help identify potential problems before they become serious, allowing preventive maintenance to improve the equipment’s reliability and efficiency.

Scheduling maintenance activities subject to stochastic job-dependent machine deterioration

This paper considers a maintenance scheduling problem on a single machine with a fixed job sequence involving job-dependent, stochastic deterioration. If the machine breaks down, then a time-consuming emergency maintenance activity needs to be conducted. Instead, planned maintenance activities can be conducted between jobs in order to improve the machine’s state and, thus, prevent the machine from breaking down. We distinguish two types of machines differing in the flexibility regarding the point of time when the maintenance activities need to be scheduled. The goal is to minimize the total expected completion time of jobs. We formalize two optimization problems, analyze their computational complexities, develop exact solution approaches, and conduct a computational study. Here, we show that our approaches can solve large instances to optimality within short run times and we analyze the advantage gained by planning maintenance activities depending on the actual state of the machine.

Optimizing maintenance logistics on offshore platforms with AI: Current strategies and future innovations

Offshore platforms are vital assets for the oil and gas industry, serving as the primary facilities for exploration, extraction, and processing. Maintenance logistics plays a crucial role in ensuring these platforms operate efficiently and safely. However, the remote and harsh environments of offshore platforms present significant challenges for maintenance activities. Traditional maintenance strategies often struggle to meet the demands of these environments, leading to inefficiencies, increased costs, and potential safety risks. This review discusses the application of Artificial Intelligence (AI) in optimizing maintenance logistics on offshore platforms. Current strategies involve a combination of preventive, predictive, and corrective maintenance approaches. Preventive maintenance schedules regular inspections and replacements based on predetermined intervals, while predictive maintenance utilizes data analytics to predict equipment failures and plan maintenance activities accordingly. Corrective maintenance addresses issues as they arise, often in response to unexpected failures. AI offers opportunities to enhance these strategies by leveraging advanced data analytics, machine learning, and optimization algorithms. AI-enabled predictive maintenance can analyze vast amounts of data from sensors, historical maintenance records, and environmental factors to forecast equipment failures with greater accuracy. This allows for proactive maintenance planning, minimizing downtime and reducing maintenance costs. Furthermore, AI can optimize maintenance logistics by improving resource allocation and scheduling. Through real-time monitoring and analysis, AI systems can prioritize maintenance tasks based on urgency, equipment criticality, and resource availability. This ensures that maintenance crews are deployed efficiently, reducing idle time and improving overall productivity. Future innovations in AI for maintenance logistics on offshore platforms include the integration of Internet of Things (IoT) devices and autonomous systems. IoT sensors can provide real-time data on equipment condition and environmental factors, enabling more precise predictive maintenance models. Autonomous maintenance robots equipped with AI algorithms can perform routine inspections and minor repairs, reducing the need for human intervention in hazardous environments. However, implementing AI in offshore maintenance logistics also poses challenges, including data quality, cybersecurity, and workforce readiness. Ensuring data accuracy and reliability is crucial for effective AI models, requiring robust data collection and management processes. Cybersecurity measures must be strengthened to protect AI systems from malicious attacks that could disrupt operations or compromise safety. Additionally, workforce training and education are essential to prepare personnel for working alongside AI systems and interpreting AI-generated insights. Optimizing maintenance logistics on offshore platforms with AI offers significant benefits in terms of efficiency, cost savings, and safety. By leveraging AI technologies, current maintenance strategies can be enhanced, and future innovations can revolutionize offshore maintenance practices, making operations more sustainable and resilient in the face of evolving challenges.

An Innovative Approach to Surface Deformation Estimation in Forest Road and Trail Networks Using Unmanned Aerial Vehicle Real-Time Kinematic-Derived Data for Monitoring and Maintenance

The significant increase in hiking, wood extraction, and transportation activities exerts a notable impact on the environmental balance along trails and forest roads in the form of soil degradation. The aim of this study was to develop a Deformation Classification Model for the surface of a multi-use trail, as well as to calculate sediment deposition and generate a flood hazard map in a partially forested region. The eBee X mapping Unmanned Aerial Vehicle (UAV) equipped with the senseFly S.O.D.A. 3D camera and Real-Time Kinematic (RTK) technology flew over the study area of 149 ha in Northern Greece at an altitude of 120 m and achieved a high spatial resolution of 2.6 cm. The specific constellation of fixed-wing equipment makes the use of ground control points obsolete, compared to previous, in most cases polycopter-based, terrain deformation research. Employing the same methodology, two distinct classifications were applied, utilizing the Digital Surface Model (DSM) and Digital Elevation Model (DEM) for analysis. The Geolocation Errors and Statistics for Bundle Block Adjustment exhibited a high level of accuracy in the model, with the mean values for each of the three directions (X, Y, Z) being 0.000023 m, −0.000044 m, and 0.000177 m, respectively. The standard deviation of the error in each direction was 0.022535 m, 0.019567 m, and 0.020261 m, respectively. In addition, the Root Mean Square (RMS) error was estimated to be 0.022535 m, 0.019567 m, and 0.020262 m, respectively. A total of 20 and 30 altitude categories were defined at a 4 cm spatial resolution, each assigned specific ranges of values, respectively. The area of each altitude category was quantified in square meters (m2), while the volume of each category was measured in cubic meters (m3). The development of a Deformation Classification Model for the deck of a trail or forest road, coupled with the computation of earthworks and the generation of a flood hazards map, represents an efficient approach that can provide valuable support to forest managers during the planning phase or maintenance activities of hiking trails and forest roads.

Optimizing Remaining Useful Life Predictions for Aircraft Engines: A Dilated Recurrent Neural Network Approach

Predicting the remaining useful life (RUL) plays a crucial rule in the field of prognostics and health management (PHM) for mechanical systems. Specifically within the domain of turbofan engines, predicting RUL plays a vital role in strategically planning maintenance activities. Consequently this aids in optimizing the overall performance of the energy system by reducing downtime and improving sustainability and efficiency. This research endeavors to forecast the RUL of turbofan engines. It employs a Dilated Recurrent Neural Network (D-RNN) Approach, a neural network structure that integrates dilated convolutions into the recurrent layers. The model underwent fine-tuning through a random grid search optimization and was tested using the Commercial Modular Aero Propulsion System Simulation (C-MAPSS) dataset. The results showcase the superior performance of the proposed D-RNN, outperforming the accuracy of other research studies.

A lightweight residual network based on improved knowledge transfer and quantized distillation for cross-domain fault diagnosis of rolling bearings

Predictive maintenance advocates the use of artificial intelligence to analyze big data and provides support for monitoring health conditions and planning maintenance activities in smart manufacturing systems. However, deep networks with massive parameters incur high computational costs, hindering its practical applications. Knowledge distillation (KD) transfers knowledge from a deep network to a lightweight network at the cost of an evident decline in diagnostic accuracy. Instead of an accuracy-size trade-off, a framework for creating a lightweight residual network with higher diagnostic accuracy is proposed in this paper. First, the generalization ability of the teacher network is enhanced based on a domain-adversarial neural network (DANN) with normalization attention mechanism (NAM) and ResNet18. Then, a small student network, ResNet6, efficiently learns the knowledge from its well-learned teacher network through the improved probability KD (IPKD) and uniform quantization. The IPKD is designed to obtain the utmost knowledge, represented as probability distributions of labels and feature space, and narrow the gap between teacher and student networks. Meanwhile, the uniform quantization is incorporated into the distillation process for quantization and distillation co-optimization, further compressing the student network. Experiments on two open-source and one wheelset bearing sets are conducted for performance verification and comparison. The results demonstrate that the obtained lightweight student network has a similar accuracy to the deep network for cross-domain fault diagnosis of bearings with various damages and speeds, even bearing sources, and its quantized network has a much smaller size while retaining a comparatively similar accuracy, which makes it more practical for actual industrial equipment.

A Bayesian approach for reliability estimation for non-homogeneous and interval-censored failure data

Reliability estimation is paramount to predicting costs, planning maintenance actions, and estimating system availability in high-risk industries such as aerospace, aeronautics, and Oil and Gas (O&G). However, a shortage of historical data is very common because of restrictions related to manufacturer rights, costs, or collecting the data. In this context, generic datasets serve as prior information to model failure distributions. A popular approach to dealing with non-homogeneous generic data assumes that the failure rate is constant, which is not true in many cases. In this paper, we propose a Bayesian approach to consider non-constant failure rates using the Weibull distribution. Here, we account for the uncertainty on the Weibull parameters by modeling the population variability via Empirical Bayes. In addition, the challenge is to consider the generic dataset to provide interval-censored failure data. To obtain the posterior distribution after incorporating specific data on the equipment of interest, the Bayesian model also considers the likelihood of complete failure and censored data. As there is no family of conjugate distributions, the posterior estimate is sampled through the Markov Chain Monte Carlo method. The model was tested using simulated data as an application example.

Analysis and prediction of railway track longitudinal level using multiple machine learning methods

With the fast development of computer science, many prediction models based on machine learning methods have been used in the railway industry, which can better predict the random characteristics in track degradation, plan maintenance activities, and eventually meet the requirement of railway transportation. However, the applicability and generality of these models are unclear and the comparative analysis of these models on the random railway track is rare, which makes it hard for railway engineers to choose the most suitable prediction models in practice. In this paper, the track longitudinal level of a section of a railway measured monthly by the rail infrastructure alignment acquisition system for 1.5 years in the Netherlands has been analysed using multiple mathematical methods. After that, three machine learning-based prediction models were developed to predict the future development of the track longitudinal level, using support vector machine, grey model and deep neural network. The prediction performance of different prediction models is compared and discussed. Recommendations for choosing prediction models and further development are provided.

Perawatan Preventif Mesin Pompa Air

The Planned maintenance activities can make the production process run smoothly. The object of the research was pump machine with three phase. Based on obsevations, pumps often experience damage to critical components. This study aims to implement preventive maintenance on pump. The advantage of using preventive maintenance can determine the optimal type of maintence and maintenance time intervals for each critical component. The research stages include determining the probability density function (pdf) and reliability, determining the maintenance interval for critical component using the downtime minimization criteria that will be used to schedule maintenance and determining the cost of corrective and preventive for each critical component. The result of the application of preventive maintenance, there were three component that experienced a decrease in maintenance costs, namely the electromotor component experiencing a 23,7% cost reduction with a 52% reliability level, the radial bearing component experiencing a cost reduction by 24% with a reliability level of 66,64% and the impeller component experiencing a decrease 19,5% cost with a 69% reliability rate. While the maintenance time interval for the electromotor component is 142,4 hours, the radial bearing component is 153,5 hours and the impeller component is 168,8 hours.

Reliability analysis of 15MW horizontal axis wind turbine rotor blades using fluid-structure interaction simulation and adaptive kriging model

Over the course of the last four decades, the rotor diameter of Horizontal Axis Wind Turbines (HAWTs) has undergone a substantial increase, expanding from 15 m (30 kW) to an impressive 240 m (15MW), primarily aimed at enhancing their power generation capacity. This growth in blade swept area, however, gives rise to heightened loads, stresses and deflections, imposing more rigorous demands on the structural robustness of these components. To prevent sudden failure and to plan effective inspection, maintenance, and repair activities, it is vital to estimate the reliability of the rotor blades by considering all the forces (aerodynamic and structural dynamics) acting on them over the turbine's lifespan. This research proposes a comprehensive methodology that seamlessly combines fluid-structure interaction (FSI) simulation, Kriging model/algorithm and Adaptive Kriging Monte Carlo Simulation (AKMCS) to assess the reliability of the HAWT rotor blades. Firstly, high-fidelity FSI simulations are performed to investigate the dynamic response of the rotor blade under varying wind conditions. Recognizing the computationally intensive nature and time-consuming aspects of FSI simulations, a judicious approach involves harnessing an economical Kriging model as a surrogate. This surrogate model adeptly predicts blade deflection along its length, utilizing training and testing data derived from FSI simulations. Impressively, the Kriging model predicts blade deflection 400 times faster than the FSI simulations, showcasing its enhanced efficiency. The optimized surrogate model is then used to estimate the flap wise blade tip deflection for one million wind speed samples generated using Weibull distribution. Thereafter, to evaluate the reliability of the blades, statistical modeling using methods such as Monte Carlo Simulation (MCS), AKMCS is performed. The results demonstrate the faster convergence of AKMCS requiring only 21 samples, as opposed to 1 million samples for MCS with minimal reduction in the precision of the estimated probability of failure (Pf) and reliability index (β). Demonstrated on the backdrop of an IEA-15MW offshore reference WT rotor blade, the proposed methodology underscores its potential to be seamlessly incorporated into the creation of WT digital twins, due to its near real-time predictive capabilities for Pf and β assessments.

A total productive maintenance & reliability framework for an active pharmaceutical ingredient plant utilising design for Lean Six Sigma

This research investigates the requirement for and relationship and implementation of a total productive maintenance (TPM) and Reliability Centred Maintenance (RCM) strategy within an Active Pharmaceutical Ingredient (API) Plant. This research aimed to study the tools and techniques of TPM and Reliability Engineering and then deploy a designed model to an API plant. A case study involving Design for Lean Six Sigma phases of Define, Measure, Analyse, Design, and Verify was utilised to build an API site TPM pilot program. Data was collected using interviews across Company 'X's local and Global Engineering teams. Process runtime, downtime and plant availability metrics were compiled and a new design for Total Productive Maintenance and Reliability was proposed and verified. A maintenance framework was designed to optimally incorporate Total Productive Maintenance, Reliability and Operational Excellence with an emphasis on Overall Equipment Efficiency (OEE) realizing a 33 % reduction in planned maintenance activities, a 70 % reduction in Corrective Maintenance, Cleaning for Maintenance was reduced by 50 %, the pilot maintenance area of the centrifuge has its OEE increased by 20 % and plant availability increased by two hundred and 6 h. This research highlights the importance of Total Productive Maintenance as a key component of an effective maintenance strategy and its potential to transform maintenance practices. Based on this research and results, TPM is recommended to be applied to any API manufacturing organization. A limitation of the study is that it is a single-site case study. The novelty of this research is based upon the emphasis on Reliability Engineering to remove non-value add Maintenance time from the manufacturing schedule. The Total Productive Maintenance & Reliability model designed and implemented in this research is unique in the literature and can be leveraged by engineering professionals and academics to understand the benefits of TPM.

THE DEVELOPMENT OF A 2D/3D BIM-GIS WEB PLATFORM FOR PLANNED MAINTENANCE OF BUILT AND CULTURAL HERITAGE: THE MAIN10ANCE PROJECT

Abstract. The integration of Building Information Modeling (BIM), or Historic BIM (HBIM), and Geographic Information Systems (GIS) has assumed a crucial role not only in the management of smart cities or urban districts, as well as in the construction sector or infrastructures but also in the management, conservation and safeguarding of cultural heritage. This integration opens up new opportunities for decision-makers to assess the impact of interventions on heritage sites, evaluate preservation strategies, and facilitate informed decision-making processes. This contribution presents and describes the open-source web platform developed within the Interreg MAIN10ANCE project. The project aims to promote a common methodology for the cooperation and collaboration of the various stakeholders involved in the restoration, management and maintenance activities of built and cultural heritage sites. This objective has been pursued by creating a BIM-GIS database, focused on the definition of planned maintenance activities, whose data has been made available to professionals and management bodies by means of a shared web platform, usable directly in situ on a tablet or portable devices. The paper highlights the key components of the web platform, including the BIM, GIS, and artifact Viewers, the Planner, and the overall Dashboard. The platform's multiscale and multi-source approach makes it extensible to various cultural assets, whether environmental, architectural, or movable. By integrating historical and geometric information and planning maintenance activities, the web platform thus supports public administrations and professionals, amending existing state-of-the-art solutions.

Optimizing condition monitoring of ball bearings: An integrated approach using decision tree and extreme learning machine for effective decision-making

Abstract This article presents a study on condition monitoring and predictive maintenance, highlighting the importance of tracking ball bearing condition to estimate their Remaining Useful Life (RUL). The study proposes a methodology that combines three algorithms, namely Variational Mode Decomposition (VMD), Decision Tree (DT), and Extreme Learning Machine (ELM), to extract pertinent features and estimate RUL using vibration signals. To improve the accuracy of the method, the VMD algorithm is used to reduce noise from the original vibration signals. The DT algorithm is then employed to extract relevant features, which are fed into the ELM algorithm to estimate the RUL of the ball bearings. The effectiveness of the proposed approach is evaluated using ball bearing data sets from the PRONOSTIA platform. Overall, the results demonstrate that the suggested methodology successfully tracks the ball bearing condition and estimates RUL using vibration signals. This study provides valuable insights into the development of predictive maintenance systems that can assist decision-makers in planning maintenance activities. Further research could explore the potential of this methodology in other industrial applications and under different operating conditions.

Decay Dose Shielding Analysis with Hybrid Unstructured Mesh/Constructive Solid Geometry Monte Carlo Calculation and ADVANTG Acceleration

The target segments of the Oak Ridge National Laboratory Second Target Station (STS) neutron production facility become highly activated due to spallation reactions or nuclei transmutation by primary protons and emitted neutrons. Once the target segments are removed from their location within the core vessel, decay dose rates must be accurately quantified to determine the shielding configurations of remote-handling tools and transport casks and to aid in planning maintenance activities. For this analysis, we utilized a hybrid unstructured mesh (UM)/constructive solid geometry approach for calculating spallation products and neutron fluxes, activation calculations using the AARE package that includes the CINDER2008 activation code to calculate the decay photon source at different cooling times, and the ADVANTG code to accelerate the final decay photon transport calculation. Both Type 316 stainless steel (SS-316) and lead were investigated as candidates for shielding materials. The decay photon transport calculation through the thick SS-316 or lead shields exhibited between 25 and 30 orders-of-magnitude attenuations in the radial direction, depending on the shield. Such a difficult shielding calculation required advanced variance reduction. ADVANTG has some missing features, which limits its usability in spallation neutron source applications. It does not support volumetric sources created for MCNP6.2 UM capability. An approximate source was created for this problem. Not only was this approximate source needed for running the ADVANTG calculation to generate the weight windows, but also it was essential to develop source biasing (SB) parameters that were crucial for dramatically accelerating the decay photon transport in this problem. With this approximate source, the analysis was completed in a very reasonable computational time, and the design of the STS remote-handling equipment was finalized. This paper compares the efficiency of Monte Carlo simulations with different weight window and SB parameters calculated using different approximate ADVANTG calculations.

HBIM tools for knowledge, maintenance and conservation of concrete built heritage

In the 20th century, reinforced concrete was one of the most popular building materials. It was formerly regarded as a material of outstanding durability and resilience, but over the past 50 years, it has revealed severe fundamental flaws. Main weaknesses of concrete include susceptibility to chemical agents and degradation brought on by poor design and construction. As a result, restoration interventions, recovery efforts, and re-functionalization of these artifacts have progressively grown in recent decades. Only when these improvements are combined with ongoing maintenance are they effective in extending the life of the structure. The most accurate knowledge of the building and all of its components can improve the efficiency of maintenance and recovery actions.
 Recently, the use of BIM tools for reinforced concrete buildings is spreading more and more, going beyond new construction to include built heritage. A BIM model of an existing asset may be used to assess the building's condition of conservation, pinpoint any issues, and assess the alternatives available for repair and conservation while projecting the impact of the adjustments on the structure. This data may be used to plan maintenance and restoration activities, track the building's condition of conservation through time, and assess the effects of any alterations on the structure.
 In this work, a workflow for "reverse engineering", enabling the creation of an HBIM model of an existing building, is proposed and then applied to a case study building.
 This technique may be regarded as a watershed moment in the management of reinforced concrete structures since it simplifies and organizes all of the information needed to preserve the existing architectural heritage while utilizing available resources.

HBIM tools for knowledge, maintenance and conservation of concrete built heritage

In the 20th century, reinforced concrete was one of the most popular building materials. It was formerly regarded as a material of outstanding durability and resilience, but over the past 50 years, it has revealed severe fundamental flaws. Main weaknesses of concrete include susceptibility to chemical agents and degradation brought on by poor design and construction. As a result, restoration interventions, recovery efforts, and re-functionalization of these artifacts have progressively grown in recent decades. Only when these improvements are combined with ongoing maintenance are they effective in extending the life of the structure. The most accurate knowledge of the building and all of its components can improve the efficiency of maintenance and recovery actions.
 Recently, the use of BIM tools for reinforced concrete buildings is spreading more and more, going beyond new construction to include built heritage. A BIM model of an existing asset may be used to assess the building's condition of conservation, pinpoint any issues, and assess the alternatives available for repair and conservation while projecting the impact of the adjustments on the structure. This data may be used to plan maintenance and restoration activities, track the building's condition of conservation through time, and assess the effects of any alterations on the structure.
 In this work, a workflow for "reverse engineering", enabling the creation of an HBIM model of an existing building, is proposed and then applied to a case study building.
 This technique may be regarded as a watershed moment in the management of reinforced concrete structures since it simplifies and organizes all of the information needed to preserve the existing architectural heritage while utilizing available resources.