Miloš Kovačević

University of Belgrade · Chair of Management and Technology of Building

This paper outlines the RELAR project, funded by the Science Fund of the Republic of Serbia, seeks to enhance earthquake loss assessment and recovery. It utilizes Machine Learning and Image Recognition to speed up response and improve accuracy in estimating damages and repair costs. Traditional methods are slow and error-prone due to data limitatio...

The rapid and accurate assessment of earthquake-induced losses, followed by efficient recovery planning and execution, is a paramount concern for disaster-prone regions. This paper outlines the key aspects of the Framework for Rapid Earthquake Loss Assessment and Recovery (RELAR) project, financed by the Ministry of Science, Technological Developme...

This article proposes a new framework for rapid earthquake loss assessment based on a machine learning damage classification model and a representative sampling algorithm. A random forest classification model predicts a damage probability distribution that, combined with an expert-defined repair cost matrix, enables the calculation of the expected...

Knowing the right moment for the sale of used heavy construction equipment is important information for every construction company. The proposed methodology uses ensemble machine learning techniques to estimate the price (residual value) of used heavy equipment in both the present and the near future. Each machine in the model is represented with f...

Earthquake resilience starts with a sudden drop of performance when an earthquake strikes followed by a relatively long recovery phase. The dynamics and volume of investment directly affect the rate and level of recovery. In this paper, the recovery process of the city of Kraljevo, Serbia following the M5.4 November 3, 2010 Kraljevo earthquake is a...

Rapid earthquake damage and loss assessment is crucial both for insuring the safety of inhabitants in the immediate aftermath of an earthquake and for the recovery of the stricken communities in the long run. This paper investigates the potential of different machine learning methods for building a rapid earthquake loss assessment system intended f...

One of the main tasks of data-driven modelling methods is to induce representative model of underlying spatial - temporal processes based on former-historical data and data mining approach. As relatively new methods, capable of solving complex nonlinear problems, like the land use changes/cover (LULC) and urban growth, their applications are attrac...

This chapter introduces theoretical and practical aspects for applying GIS and geocomputation methods in landslide assessment problems. Machine Learning techniques in combination with GIS are proven useful for computation and building of complex non-linear spatial models, which is why they have been chosen in our work. Modeling principles that incl...

The representation of land use change (LUC) is often achieved using data-driven methods that include machine learning (ML) techniques. The main objectives of this research study are to implement three ML techniques, Decision Trees (DT), Neural Networks (NN) and Support Vector Machines (SVM) for LUC modeling, to compare these three ML techniques and...

Megaprojects are often associated with poor delivery performance and poor benefits realization. This article provides a method of identifying, in a quantitative and rigorous manner, the characteristics related to project management success in megaprojects. It provides an investigation of how stakeholders can use this knowledge to ensure more effect...

Support Vector Machines (SVM) is a machine learning (ML) algorithm commonly applied to the classification of remotely sensing data and more recently for modeling land use changes. However, in most geospatial applications the current literature does not elaborate on specifications of the SVM method with respect to data sampling, attribute selection...

Land use changes play an important role in interactions between human and physical systems, and have significant impacts on the environment at local, regional and global scales. Land use change is a complex process and so developing dynamic models to represent the process is a challenging task. Decision Trees (DT) is a Machine Learning (ML) method...

European transport policy, defined in the White Paper, supports shift from road to rail and waterborne transport. The hypothesis of the paper is that changes in the economic environment influence rail traffic volume. Therefore, a model for prediction of rail traffic volume applied in different economic contexts could be a valuable tool for the tran...

This paper introduces the current machine learning approach to solving spatial modeling problems in the domain of landslide susceptibility assessment. The latter is introduced as a classification problem, having multiple (geological, morphological, environmental etc.) attributes and one referent landslide inventory map from which to devise the clas...

In this research, machine learning algorithms were compared in a landslide-susceptibility assessment. Given the input set of GIS layers for the Starča Basin, which included geological, hydrogeological, morphometric, and environmental data, a classification task was performed to classify the grid cells to: (i) landslide and non-landslide cases, (ii)...

Quantitative techniques for prediction and classification in soil survey are developing rapidly. The paper introduces application of Support Vector Machines in the estimate of values of soil properties and soil type classification based on known values of particular chemical and physical properties in sampled profiles. Comparison of proposed approa...

Quantitative techniques for spatial prediction and classification in geological survey are developing rapidly. The recent applications of machine learning techniques confirm possibilities of their application in this field of research. The paper introduces Support Vector Machines, a method derived from recent achievements in the statistical learnin...

Case study addresses NW slopes of Frus¿ka GoraMountain, Serbia. Landslide activity is quite notorious in this region, especially along the Danube's right river bank, and recently intensified seismicity coupled with atmospheric precipitation might be critical for triggering new landslide occurrences. Hence, it is not a moment too soon for serious la...

Professionals and craftsmen in the construction sector make an intensive use of information in their decision-making processes but only make limited use of the abundant information that is potentially available to them, particularly on the web. Consequently, designs are impoverished, construction is defective, and innovation is delayed. To facilita...

The construction sector is notorious for the dichotomy between a its intensive use of information in its decision-making processes and b its limited access to, and insufficient use of, the pertinent information that is potentially available. In the context of the potential availability of valid information on the Web, we have developed a question-a...

Extracting and processing information from web pages is an important task in many areas like constructing search engines, information retrieval, and data mining from the web. A common approach in the extraction process is to represent a page as a 'bag of words' and then to perform additional processing on such a flat representation. In this paper w...

Automatic processing of Web documents is an important issue in the design of search engines, of Web mining tools, and of applications for Web information extraction. Simple text-based approaches are typically used in which most of the information provided by the page visual layout is discarded. Only some visual features, as the font face and size,...

Extracting and processing information from web pages is an important task in many areas like constructing search engines, information retrieval, and data mining from the Web. Common approach in the extraction process is to represent a page as a “bag of words” and then to perform an additional processing on such a flat representation. In this paper...

Extracting and processing information from web pages is an important task in many areas like constructing search engines, information retrieval, and data mining from the Web. Common approach in the extraction process is to represent a page as a "bag of words" and then to perform additional processing on such a flat representation. In this paper we...

Extracting and processing information from web pages is an important task in many areas like constructing search engines, information retrieval, and data mining from the Web. Common approach in the extraction process is to represent a page as a "bag of words" and then to perform additional processing on such a flat representation. In this paper we...

Extracting and processing information from Web pages is an important task in many areas like constructing search engines, information retrieval, and data mining from the Web. A common approach in the extraction process is to represent a page as a "bag of words" and then to perform additional processing on such a flat representation. We propose a ne...

Standard techniques for a web page classification usually take a sim-ple text-based approach, in which most of the information provided by the vis-ual layout of a page is discarded. In our work we propose a new classification approach based on the visual layout analyses, conducted before implementing standard classification techniques. A page is re...