Computational Tool for Post-Earthquake Evaluation of Damage in Buildings

A method and a computational tool oriented to assist the damage and safety evaluation of buildings after strong earthquakes is described in this article. The input of the model is the subjective and incomplete information on the building state, obtained by inspectors which are possibly not expert professionals of the field of building safety. The damage levels of the structural components are usually described by linguistic qualifications which can be adequately processed by computational intelligence techniques based on neuro-fuzzy systems what facilitate the complex and urgent tasks of engineering decision-making on the building occupancy after a seismic disaster. The hybrid neuro-fuzzy system used is based on a special three-layer feedforward artificial neural network and fuzzy rule bases and is an effective tool during the emergency response phase providing decisions about safety, habitability, and reparability of the buildings. Examples of application of the computer program are given for two different building classes

The Decision Tree Aided Neuro-Fuzzy Inference Characterization of the Stochastic Hydrology of the Tana Alluvial Aquifer

The Tana Alluvial Aquifer is the name given to the little-understood aquifer which is active in the areas bordering the River Tana Flow course as the river weaves its way through the sedimentary plains of Balambala, Garissa, Fafi and Ijara and, finally, into the Tana Delta areas, with the common denominator being the proximity to the Lower Tana catchment, especially the riparian corridor of the River itself, and beyond. The aquifer may extend to between five to fifteen kilometers away from the river channels course way, and at times, it may be felt even 20 kilometers away. The geology of the locality is heterogeneous and comprise sediments whose soil mechanics may not be easily deciphered, since some areas close to the river have very fresh water while others are saline (Bura East in Fafi Sub County easily comes to mind here).  There are areas far from the river but bearing fresh water (Mulanjo comes to mind). In some areas, sites close to the river discharge low yield figures, whereas those located farther afield discharge favorably. The water quality and discharge are therefore stochastic variables, subject to chance occurrence. In view of this inconsistency, and on the account of data scarcity, the neuro-fuzzy inference algorithm was developed to map the Universe of Discourse of the Tana Alluvial Aquifer, aka the T.A.A., as it relates to the longitudes, latitudes, depths, and discharges of the aquifers in the study area. The mapping was with respect to aquifer discharge, the variable used to characterize an aquifer, in terms of Transmissivity and Hydraulic Conductivity, thereby defining aquifer recharge propensity. Membership functions were developed using the trapezoidal membership family, and fuzzy rules were appropriately evolved from the fuzzified aquifer data, before finally employing the Sugeno inference engines (in Python) to make predictions of discharge, at each of the T.A.A. aquifer subsets mapped for fresh, saline, hard and brackish water species. The accuracy in the outputs achieved in the areas mapped vindicated the power of the neuro-fuzzy inference systems, as the accuracy oscillated between 92 and 99 percent, when the discharge values predicted were compared with the actual known discharge values of the wells mapped. The water quality class characterization was then undertaken using the decision tree (DT) algorithm in python which gave rise to a 100 percent prediction accuracy. The same DT algorithm could not successfully predict the discrete values of aquifer discharge or EC values, with as much accuracy (but performed excellently with salinity class data), and that was why fuzzy logic was employed. The study vindicated the use of the DT and Fuzzy Logic Algorithms as simple, yet powerful analytical tools, in characterizing the Stochastic Hydrology of the Tana Alluvial Aquifer.

Evaluation of the habitability of buildings affected by an earthquake using the fuzzy sets theory and the artificial neural networks

La teoría de conjuntos difusos y las redes neuronales son herramientas de inteligencia computacional que cada vez tienen un uso más extendido en la ingeniería sísmica. En este artículo se desarrolla un método y una herramienta computacional que hace uso de estas técnicas para apoyar la evaluación del daño y de la seguridad de los edificios después de sismos fuertes. Se utiliza una red neuronal artificial de tres capas y un algoritmo de aprendizaje tipo Kohonen, así como conjuntos difusos para manipular información subjetiva como las calificaciones de los niveles de daño presentes en los edificios evaluados. También se aplica una base de reglas difusas para contribuir al proceso de toma de decisiones. Se ha desarrollado un programa de ordenador que utiliza estas técnicas, cuyos datos de entrada del programa corresponden a la información subjetiva e incompleta del estado del edificio obtenida por profesionales posiblemente inexpertos en el campo de la evaluación del comportamiento sísmico de los edificios. El método propuesto ha sido implementado en una herramienta de especial utilidad durante la fase de respuesta a emergencias, que facilita las decisiones sobre habitabilidad y reparabilidad de los edificios. Para ilustrar su aplicación, se incluyen ejemplos de aplicación del programa para dos clases diferentes de edificios.The fuzzy sets theory and the artificial neural networks are computational intelligence tools which are nowadays widely used in earthquake engineering. This paper develops a method and a computer program which use these computational intelligence tools in order to support the damage and safety evaluation of buildings after strong earthquakes. The model uses an artificial neural network with three layers and a Kohonen learning algorithm; it also uses fuzzy sets in order to manage subjective information such as linguistic qualification of the damage levels in buildings and a fuzzy rule base to support the decision making process. All these techniques are incorporated in the developed computer program. The input data is the subjective and incomplete information about the building state obtained by possibly non experienced evaluators in the field of the seismic performance of buildings. The proposed method is implemented in a tool especially useful in the emergency response phase, when it supports the decision making regarding the building habitability and reparability. In order to show its effectiveness, two examples are included for two different types of buildings.Peer Reviewe

Herramienta computacional para la evaluación post-sísmica de daños en edificios

En este artículo se desarrolla un método y una herramienta computacional orientada a apoyar la evaluación de daños y de la seguridad de los edificios después de sismos fuertes. Los datos de entrada del programa de computador corresponden a información subjetiva e incompleta del estado del edificio, obtenida por profesionales posiblemente inexpertos en el campo de la construcción. Los niveles de daño de los componentes estructurales normalmente son descritos por calificaciones lingüísticas que pueden ser adecuadamente procesadas con técnicas de inteligencia computacional basadas en sistemas neurodifusos. Estas técnicas facilitan la realización de las complejas y urgentes tareas de toma de decisiones de los ingenieros en relación con la ocupación de los edificios después de un desastre sísmico. El sistema híbrido neuro-difuso, descrito en este artículo, está basado en una red neuronal de tres capas artificiales unidireccionales especiales y una base de reglas de lógica difusa. Este sistema es una herramienta de especial utilidad durante la fase de respuesta a emergencias, que facilita las decisiones de habitabilidad y reparabilidad de los edificios. Para ilustrar su aplicación se incluyen ejemplos de aplicación del programa para tres clases diferentes de edificios.Postprint (published version

Fuzzy Sets in Business Management, Finance, and Economics

This book collects fifteen papers published in s Special Issue of Mathematics titled “Fuzzy Sets in Business Management, Finance, and Economics”, which was published in 2021. These paper cover a wide range of different tools from Fuzzy Set Theory and applications in many areas of Business Management and other connected fields. Specifically, this book contains applications of such instruments as, among others, Fuzzy Set Qualitative Comparative Analysis, Neuro-Fuzzy Methods, the Forgotten Effects Algorithm, Expertons Theory, Fuzzy Markov Chains, Fuzzy Arithmetic, Decision Making with OWA Operators and Pythagorean Aggregation Operators, Fuzzy Pattern Recognition, and Intuitionistic Fuzzy Sets. The papers in this book tackle a wide variety of problems in areas such as strategic management, sustainable decisions by firms and public organisms, tourism management, accounting and auditing, macroeconomic modelling, the evaluation of public organizations and universities, and actuarial modelling. We hope that this book will be useful not only for business managers, public decision-makers, and researchers in the specific fields of business management, finance, and economics but also in the broader areas of soft mathematics in social sciences. Practitioners will find methods and ideas that could be fruitful in current management issues. Scholars will find novel developments that may inspire further applications in the social sciences

Geospatial Inference and Management of Utility Infrastructure Networks

Ph. D. Thesis.Modern cities consist of spatially and temporally complex networks that connect urban infrastructure assets to the buildings they service. Critical infrastructure networks include transport, electricity, water supply, waste water and gas, all of which play a key role in the functioning of modern cities. Understanding network spatial connectivity, resource flow, dependencies and interdependencies is essential for infrastructure planning, management, and assessment of system robustness and resilience. However, there is a sparsity of fine spatial scale data from which such understanding can be derived or inferred. Often data is held within commercially sensitive organisations and may be incomplete topologically and/or spatially. Thus, there is an urgent need to develop new approaches to the integrated inference, management and analysis of the complex utility infrastructure networks. Such approaches should allow the highly granular representation of utility network connectivity to be represented in a spatially explicit manner, employing methods of data and information management to ensure they are scalable and generic. This thesis presents the development of such an approach, one that employs a geospatial ontology to formally define the key entities, attributes and relationships of fine spatial scale utility infrastructure networks. This ontology is used as the conceptual framework for the development of a suite of algorithms that allow the heuristic inference of the spatial layout of utility infrastructure networks for any urban conurbation within the UK. This is demonstrated via several case studies where the electricity feeder network between substations and buildings is generated for several different cities within the UK. Validation against the known network for the city of Newcastle upon Tyne indicates that the network can be inferred to high levels of accuracy (about 90%). Moreover, the algorithm is shown to be a transferable to the inference and integration of other utility infrastructure networks (gas, water supply, waste water, and new road layouts). ii The representation, management and analysis of such spatially complex and large utility networks is, however, a major challenge. The efficient storage, management and analysis of such spatial networks is explored via a comparison of a traditional RDMS approach (PgRouting within Postgres), spatial database (PostGIS) and a NoSQL graph-database (Neo4j), as well as a bespoke hybrid spatial-graph framework (combination of PostGIS and Neo4j). A suite of comparison tests of data writing, data reading and complex network analysis demonstrated that significant performance benefits in the use of the NoSQL graph database approach for data read (around 210% faster) and network analysis (between 420 and 1170 % faster). However, this was at the expenses of data writing which was found to be between 135 and 150% slower.MISTRAL project, School of Engineering at Newcastle University

Técnicas innovadoras para la evaluación del riesgo sísmico y su gestión en centros urbanos: Acciones ex ante y ex post

The objectives of this thesis are: the ex ante seismic risk evaluation for urban centers, the disaster risk management evaluation and the ex post risk evaluation of the damaged buildings after an earthquake. A complete review of the basic concepts and of the most important recent works performed in these fields. These aspects are basic for the development of the new ex ante and ex post seismic risk evaluation approaches which are proposed in this thesis and for the s evaluation of the effectiveness of the disaster risk management.Risk has been defined, for management purposes, as the potential economic, social and environmental consequences of hazardous events that may occur in a specified period of time. In Chapter 3 of this thesis, a multidisciplinary evaluation, that takes into account not only the expected physical damage, the number and type of casualties or economic losses, but also the conditions related to social fragility and lack of resilience, which favour the second order effects when a hazard event strikes an urban centre. The proposed general urban risk evaluation method is multi-hazard and holistic, that is, an integrated and comprehensive approach whose objective is to guide decision-making. This method has been applied to the cities of Bogota, Colombia, and Barcelona, Spain, and it is being applied to Metro Manila, Philippines.Chapter 4 develops a methodology for the disaster risk management evaluation. A disaster risk management index, DRMi, is conceptually supported and formulated, which measures the performance and the effectiveness of the risk management in a territory that can be a country, a subnational region or a city. The proposed DRMi is developed by quantifying four public policies: the risk identification, the risk reduction, the disaster management and the governance and financial protection. With this methodology eleven countries of Latin America and the Caribbean; Colombia at subnational level and Bogota at urban level were evaluated.In chapter 5 a methodology for the ex-post evaluation of the seismic damage is developed, using computational intelligence techniques. It has the objective of assisting non-expert professionals of building construction in evaluating the damage and safety of buildings after strong earthquakes, facilitating decision-making on their habitability and reparability during the emergency response phase. This neuro-fuzzy system has been adopted for its official use by the cities of Bogota and Manizales, in Colombia.The conclusions of this thesis are shown in Chapter 6, where also future lines of investigation are presented. The main conclusions are:- The proposed model for the holistic evaluation of the seismic risk facilitates the integral risk management and the decision making on risk reduction. The analysis of the results allows to establish priorities for the mitigation and actions of prevention and planning aiming to modify the conditions that influence on the risk of the zone.- The proposed disaster risk management index, DRMi, is consistent, methodical and has been developed to measure the risk management performance. This index allows to make the evaluation in a systematic and quantitative way and allows to define operation objectives and to improve the efficiency of the risk management. - A novel system of support to the habitability and damage evaluation of buildings, based on fuzzy logic and artificial neural networks was proposed. This kind of tool is useful due to the type of information that is handled, which is subjective and incomplete. Linguistic qualifications can appropriately be represented by fuzzy sets. An artificial neuronal network was used to calibrate the system starting from the experts judgment.Finally, several annexes are included which include details on methodological and calculation aspects related to the of proposed risk evaluation methods

Técnicas innovadoras para la evaluación del riesgo sísmico y su gestión en centros urbanos: Acciones ex ante y ex post

The objectives of this thesis are: the ex ante seismic risk evaluation for urban centers, the disaster risk management evaluation and the ex post risk evaluation of the damaged buildings after an earthquake. A complete review of the basic concepts and of the most important recent works performed in these fields. These aspects are basic for the development of the new ex ante and ex post seismic risk evaluation approaches which are proposed in this thesis and for the s evaluation of the effectiveness of the disaster risk management.Risk has been defined, for management purposes, as the potential economic, social and environmental consequences of hazardous events that may occur in a specified period of time. In Chapter 3 of this thesis, a multidisciplinary evaluation, that takes into account not only the expected physical damage, the number and type of casualties or economic losses, but also the conditions related to social fragility and lack of resilience, which favour the second order effects when a hazard event strikes an urban centre. The proposed general urban risk evaluation method is multi-hazard and holistic, that is, an integrated and comprehensive approach whose objective is to guide decision-making. This method has been applied to the cities of Bogota, Colombia, and Barcelona, Spain, and it is being applied to Metro Manila, Philippines.Chapter 4 develops a methodology for the disaster risk management evaluation. A disaster risk management index, DRMi, is conceptually supported and formulated, which measures the performance and the effectiveness of the risk management in a territory that can be a country, a subnational region or a city. The proposed DRMi is developed by quantifying four public policies: the risk identification, the risk reduction, the disaster management and the governance and financial protection. With this methodology eleven countries of Latin America and the Caribbean; Colombia at subnational level and Bogota at urban level were evaluated.In chapter 5 a methodology for the ex-post evaluation of the seismic damage is developed, using computational intelligence techniques. It has the objective of assisting non-expert professionals of building construction in evaluating the damage and safety of buildings after strong earthquakes, facilitating decision-making on their habitability and reparability during the emergency response phase. This neuro-fuzzy system has been adopted for its official use by the cities of Bogota and Manizales, in Colombia.The conclusions of this thesis are shown in Chapter 6, where also future lines of investigation are presented. The main conclusions are:- The proposed model for the holistic evaluation of the seismic risk facilitates the integral risk management and the decision making on risk reduction. The analysis of the results allows to establish priorities for the mitigation and actions of prevention and planning aiming to modify the conditions that influence on the risk of the zone.- The proposed disaster risk management index, DRMi, is consistent, methodical and has been developed to measure the risk management performance. This index allows to make the evaluation in a systematic and quantitative way and allows to define operation objectives and to improve the efficiency of the risk management. - A novel system of support to the habitability and damage evaluation of buildings, based on fuzzy logic and artificial neural networks was proposed. This kind of tool is useful due to the type of information that is handled, which is subjective and incomplete. Linguistic qualifications can appropriately be represented by fuzzy sets. An artificial neuronal network was used to calibrate the system starting from the experts judgment.Finally, several annexes are included which include details on methodological and calculation aspects related to the of proposed risk evaluation methods.Postprint (published version

Neuro-fuzzy assessment of building damage and safety after an earthquake

This chapter describes the algorithmic basis of a computational intelligence technique, based on a neuro-jilzzy system, developed with the objective ofassisting nonexpert professionals ofbuilding construction to evaluate the damage andsafety ofbuildings after strong earthquakes, facilitating decision-making during the emergency response phase on their habitability and reparability. A hybrid neuro-jilzzy system is proposed, based on a special three-layer feed-forward artificial neural network and fuzzy rule bases. The inputs to the system are jilzzy sets, taking into account that the damage levels ofthe structural components are linguistic variables, defined by means ofqualifications such as slight, moderate or severe, which are very appropriate to handle subjective and incomplete information. The chapter is a contribution to the understanding ofhow soft computing applications, such as artificial neural networks and fuzzy sets, can be used to complex and urgent processes of engineering decision-making, like the building occupancy after a seismic disaster.Peer Reviewe