VGTU Journals (Vilnius Gediminas Technical University - Vilnius Tech)
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Urban flood disaster risk management using multi-criteria decision-making methods: A scoping review
No full textStatistical data indicates a rising trend in the frequency and unpredictability of floods globally. Regions traditionally less affected by flooding are reportedly experiencing an increased impact, underscoring the widespread nature of this phenomenon. Over the past decade, the overall incidence of floods has significantly increased, affecting billions of people worldwide. If appropriate measures are not taken, floods driven by climate change, urbanization, and the consequences of human activities will continue to increase in frequency and intensity, leading to even greater economic, social, and environmental losses. Proper selection of management strategies and risk reduction measures is becoming particularly important for reducing flood risks. Multi-Criteria Decision Making (MCDM) methods are well-suited for addressing such complex, multi-criteria problems. Therefore, this study aims to explore the research fields of MCDM application in flood management and identify the most widely used methods. This is done to clarify their benefits and enhance their applicability. The Systematic Literature Review (SLR) revealed that the research field is broad and dynamic, evolving over the decades. However, the application of MCDM remains popular and, according to current trends, continues to gain popularity. New research fields are also emerging, such as “Identification and/or Mapping of Flood-Prone Areas” and “Sustainable Infrastructure Assessment”, highlighting scientist growing concern about the importance of evaluating vulnerable areas and applying sustainable solutions to address flood management challenges. The findings are particularly relevant to real estate and property management, as they support the development of evidence-based frameworks for assessing property-level flood resilience and for guiding investment decisions to protect built assets
Research on the valuation of internet enterprise data assets based on value chain theory
No full textAs data assets grow strategically important yet remain difficult to value in internet enterprises, this study analyzes the factors influencing their valuation. Using value chain theory and a system dynamics model, it uncovers the mechanisms of value formation. Results show that data asset value is realized dynamically across stages – collection, analysis, mining, and application – shaped by internal attributes and external factors. The process follows a diminishing marginal return pattern and exhibits significant value lag. Therefore, data asset assessment should account for the full life-cycle, intrinsic properties, and technological conditions
Optimising scheduled maintenance on operational buildings: a microservice-based BIM framework
No full textOperation and Maintenance (O&M) aims to preserve the quality of the building throughout its life, keeping maintenance costs within acceptable limits. Maintenance involves different tasks, from replacing air conditioning filters to restoring structural elements. Each task has an optimal frequency, which can be flexible within a specific time range, a cost, and a duration. These maintenance activities may disrupt building operations by repeatedly interrupting ongoing activities. This research seeks to reduce these disruptions by grouping tasks within reasonably close time frames to schedule preventive maintenance plans while respecting their frequency. We propose an optimisation model, solvable using a general-purpose solver, which identifies the best time range for grouping O&M tasks. By penalising deviations from the optimal period, the model ensures that tasks are performed at the most cost-effective time. Integrated within a microservice-based architecture, the optimisation engine seamlessly links an input database and a BIM model, orchestrated using Dynamo for Revit. A case study illustrates the effectiveness of this system, consolidating multiple tasks into optimised work clusters and significantly reducing operational disruptions. The originality of this work lies in its innovative combination of optimisation techniques and BIM tools, providing a practical and scalable solution for efficient O&M management
Social enterprises as a tool for supporting housing supply in compliance with the Agenda 2030
No full textThere is a space at the interface between the public and state sectors and the private profitable, market sector for the operation of the social economy. The social economy is a socially beneficial area in solving social problems in many countries of the European Union and is still consolidating its position in terms of the Agenda 2030. The paper presents a framework of new and unexplored issues, where the novelty a current overview of the role of social enterprises in providing housing support for low-income groups in regions of the Slovak Republic with elevated unemployment rates, in alignment with the objectives set forth in Agenda 2030. We also focus on the potential of social enterprises in solving the issue of housing for people with insufficient income in marginalized communities, as this area has received little attention in Slovakia. As part of the empirical part, we conducted an analysis of the development of social enterprises and their establishment in the regions. Since a quarter of the social enterprises are located in the least developed regions, where unemployment is significantly worse than the Slovak average, we examined by correlation whether there is a dependence between the number of available job seekers of working age in % of the working age population in individual regions of the Slovak Republic and the number of social enterprises established in the regions of the Slovak Republic. From empirical research, we can identify that social housing enterprises are an important tool that helps solve the issue of housing for people who do not have sufficient income to secure it however, the absence of effective financial support results in their uniform composition
Dynamic multi-scale simulation for evaluating combat effectiveness against aerial threats
No full textThe development and assessment of modern weapon systems require efficient and flexible simulation tools. This paper introduces a multi-scale discrete-event simulation framework designed to evaluate the dynamic combat effectiveness of weapon systems. The framework combines high-resolution and low-resolution models to address the complexities of real-world engagements while maintaining computational efficiency. Physical processes are encapsulated as modular state transition functions, allowing seamless integration of a multi complexity level modeling approach. The framework’s versatility is demonstrated through a case study analyzing the effectiveness of a tank weapon system against a fleet of drones. Non-deterministic methods such as Monte Carlo simulations for uncertainty quantification are used to evaluate probabilistic key metrics, such as projectile accuracy and lethality, providing insights into engagement dynamics and optimization of firing strategies. By leveraging a hybrid continuous/discrete approach and modular design, the framework enables comprehensive assessments of weapon effectiveness during an engagement, bridging gaps in traditional deterministic methodologies for both static and dynamic targets. Future enhancements will focus on optimizing sampling techniques for broader applicability of high-resolution stochastic simulations in modern combat scenarios
A novel hybrid model for predicting the bearing capacity of piles
No full textDue to the uncertainty of soil condition and pile design characteristics, it is always a challenge for geotechnical engineers to accurately determine the bearing capacity of piles. The main objective of this study is to propose a hybrid model coupling least squares support vector machine (LSSVM) with an improved particle swarm optimization (IPSO) algorithm for the prediction of bearing capacity of piles. The improved PSO algorithm was used to optimize the LSSVM hyperparameters. The performance of the IPSO-LSSVM model was compared with seven artificial intelligence models, namely adaptive neuro-fuzzy inference system (ANFIS), M5 model tree (M5MT), multivariate adaptive regression splines (MARS), gene expression programming (GEP), random forest (RF), regression tree (RT) and a stacked ensemble model. Six statistical indices (e.g., coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), relative root mean squared error (RRMSE), BIAS and discrepancy ratio (DR)) were used to evaluate the performance of the models. The R2, MAE, RMSE, RRMSE and BIAS values of the IPSO-LSSVM model were 1, 4.27 kN, 6.164 kN, 0.005 and 0, respectively, for the training datasets and 0.9977, 22 kN, 36.03 kN, 0.0275 and –11, respectively, for the testing datasets. Compared with the ANFIS, MARS, GEP, M5MT, RF, RT and the stacked ensemble models, the proposed IPSO-LSSVM model shows high accuracy and robustness on the test datasets. In addition, the sensitivity, uncertainty, reliability and resilience of the IPSO-LSSVM model were also analyzed in this study.
First published online 22 October 202
Electromagnetic wave-driven deep learning for structural evaluation of reinforced concrete strength
No full textMonitoring the performance of reinforced concrete structures, particularly in terms of strength reduction, presents significant challenges due to the practical limitations of traditional detection methods. This study introduces an innovative framework that incorporates a non-destructive technique using electromagnetic waves (EM-waves) transmitted via Radio Frequency Identification (RFID) technology, combined with two-dimensional (2-D) Fourier transform, fractal dimension analysis, and deep learning techniques to predict reductions in structural strength. Experiments were conducted on three reinforced concrete beam (RCB) specimens exhibiting various levels of reinforcement corrosion. From these, a dataset of 1,800 EMwave images was generated and classified into “normal” and “reduced strength” categories. These categories were used to train and validate a Convolutional Neural Network (CNN), which demonstrated robust performance, achieving a high accuracy of 0.91 and an F1-score of 0.93 in classifying instances of reduced structural strength. This approach offers a promising solution for detecting strength reduction in reinforced concrete infrastructures, enhancing both safety and maintenance efficiency.
First published online 5 November 202
Managing the multi-stakeholder complex in P3 project decision-making: a mix-method review
No full textManaging stakeholders in a public-private partnership (P3) project is complex; it involves uncertainties, variations, and intricacies. A typical P3 system has a dynamic multi-stakeholder system requiring active project management to avoid delays, conflicts, and partnership failures. Presently, limited research has explored stakeholder management in P3 projects. The current study uses bibliometric and scientometric analyses to identify stakeholder-related issues in P3 decision-making. A keyword co-occurrence and clustering reveal that project stakeholders strongly influence significant P3 decision-making in risk management, concession design, procurement, and sustainability. Therefore, a detailed content analysis is conducted to discuss this in detail. The study reveals that poor structuring of roles and responsibilities, public opposition, information asymmetry, principal-agent problems, knowledge management, and corruption are crucial stakeholder issues in decision-making. Further, a systems thinking framework is used to study the stakeholder dynamics for early engagement and relationship management for P3 projects. Lastly, the study findings are summarised as a conceptual framework of stakeholder-related issues with corresponding stakeholder management process steps. The review contributes to inclusive stakeholder management for P3 projects, helping early-stage researchers and practitioners. They can develop a more profound domain knowledge of P3 stakeholder-related issues, decision-making aspects, and stakeholder management elements
Residential satisfaction indicator: Latin American evidence
No full textLatin American construction companies, particularly in Ecuador, operate in a highly competitive environment. In this context, achieving customer satisfaction is a primary objective for the success of real estate projects. This research aims to develop a comprehensive residential satisfaction index that includes sustainability dimensions, based on global satisfaction indices and structural equation modeling. This study will enable residential builders to assess their performance from the customer’s perspective and make strategic decisions aimed at improving quality, which, in turn, will lead to greater customer satisfaction in future projects. The findings of this research will provide valuable input for decision-making in the real estate sector, with a focus on the Latin American context
Spatiotemporal patterns and prediction of multi-region house prices via functional mixed effects model
No full textHouse prices have always been a popular indicator for real estate market monitoring. This study explores the spatiotemporal patterns of house prices at the community level in San Francisco from January 2009 to April 2024. A functional spatiotemporal semiparametric mixed effects (FST-SM) model was proposed to analyze the Zillow Home Value Index (ZHVI), considering spatiotemporal variations. This response is associated with known influences and unknown latent random effects. The random-effects component was expanded using functional principal components. The conditional autoregressive (CAR) structure of the principal component scores was adopted to analyze nonparametric time trends and spatiotemporal correlations. The proposed model was compared with other time-series models in terms of spatiotemporal prediction. The results show that the prediction accuracy of the proposed model is higher than that of other regular models. In summary, a functional mixed effects model was proposed to describe spatiotemporal patterns and forecast house prices. This study can provide valuable references for decision-making by local governments, real estate suppliers, and house buyers