Deterministically generated negative selection algorithm for damage detection in civil engineering systems

The Negative Selection (NS) algorithm is the first algorithm to come from the study of natural immune systems, and is the most widely known and applied algorithm in the field. It has been used to build intrusion detection systems along with many other security-related tasks. However, it has not been possible to use the Negative Selection algorithm on many real-world scenarios. The present research shows an optimization of the negative selection algorithm to make its execution faster. The optimized algorithm remains functionally the same, providing the same results as the unoptimized algorithm. Details are given about the optimization scheme used and the optimized negative selection algorithm is tested on the UCI Breast Cancer data set. The performance of the unoptimized negative selection algorithm is compared to the performance of the algorithm with the proposed optimization. Three claims about the function of the optimized negative selection algorithm are made and tested with four experiments. The results of the experiments are used to demonstrate that the algorithm is faster and does not change the negative selection algorithm or lower its accuracy. Although there has been research into the optimization of the Negative Selection algorithm, this work will only apply to hyper-sphere detectors, which has not been done before.

In this study, we demonstrate large-scale sensors, intended for damage detection in various engineering structures, within the framework of structural health monitoring. The construction of sensors is based on a hybrid structure of a conductive intermittent pattern (made of silver) with embedded sensing elements made of a carbon-based resistive ink. Because the electrical resistance of the silver pattern was much lower than that of the resistance of sensing elements, the sensing properties of such sensors depend mainly on the properties of the carbon ink. Thanks to such construction, it was possible to achieve the electrical resistance of 1-m-long sensors within the desired range. Conducted laboratory tests have shown relatively low sensitivity to temperature changes and the capability of the sensors for damage detection.

The monitoring of a structural condition of steel bridges is an important issue. Good condition of infrastructure facilities ensures the safety and economic well-being of society. At the same time, due to the continuous development, rising wealth of the society and socio-economic integration of countries, the number of infrastructural objects is growing. Therefore, there is a need to introduce an easy-to-use and relatively low-cost method of bridge diagnostics. We can achieve these benefits by the use of Unmanned Aerial Vehicle-Based Remote Sensing and Digital Image Processing. In our study, we present a state-of-the-art framework for Structural Health Monitoring of steel bridges that involves literature review on steel bridges health monitoring, drone route planning, image acquisition, identification of visual markers that may indicate a poor condition of the structure and determining the scope of applicability. The presented framework of image processing procedure is suitable for diagnostics of steel truss riveted bridges. In our considerations, we used photographic documentation of the Fitzpatrick Bridge located in Tallassee, Alabama, USA.

Variational Auto-Encoders (VAE) and Long Short-Term Memory (LSTM) are investigated in this article in the framework of Structural Health Monitoring (SHM). The presented approaches aims to combine sensor data with numerical modelling of the dynamical system in a Reduced Order Modelling setting. Two Finite Element case-of-study are proposed with the scope of identify non-linear forces and physical parameter degradation. Starting from a reduced set of sensors data and different levels of knowledge of the physical system, the reconstruction capabilities of time series data are presented and compared for both proposed architectures.

Damage analysis in composite materials using a hybrid artificial immune system

Early detection of threats to electrical energy distribution systems helps professionals make decisions and mitigate interruptions in supply and improper activation of the protection system. Biologically inspired methods, e.g., artificial neural networks, genetic algorithms, and ant colonies, solve optimization problems and facilitate pattern recognition and decision-making. The present work presents a tool for detecting and classifying voltage disturbances based on the negative selection algorithm, which identifies and eliminates self-reactive cells, associated with multiresolution analysis, which analyzes the signal at different scales of detail, allowing a more complete understanding and detailed description of the phenomenon in question. The negative wavelet selection algorithm demonstrates robustness to detect and classify disturbances.

A spam detection model based on negative selection algorithm is proposed in this paper. The artificial immune system creates techniques to solve complex computations, aiming to developing immune based models. This is done by distinguishing self from non-self. Preliminary mathematical analysis will expose the computation and experimental description of the method and how it is applied to spam detection. A new detector model and matching rule model are also generated for effective matching of both self and non-self in other to burst the detector performance of the model. Our unique matching technique use in the negative selection algorithm help the model to overcome the limitation of a normal negative selection algorithm in defining harmfulness of self and non-self. This improves the requirement of the model and satisfactory requirement in terms of true positive and false positive rates. The experimental result confirms that the proposed model is able to establish a better true positive on an unknown spam Models and application in artificial immune system is coming up as an active and attractive field of great diversity. Great source of inspiration to computational model are drawn from the already knowledge of the immune system. Over the past years, rapid expansion of computer network system as change the world. It is essential for an effective computer security system because attacks and criminal intend are increasingly popular in computer network(1) . There are several measure put in place by many companies in the area of creating anti spam software based on signatures and have a very efficient performance in detecting spam fast. Though, new variation of spam and unknown spam are very difficult to detect by this software. The traditional way of detecting spam based on signature is no more efficient for today systems. Recent years, researchers are interested in the field of immune system in achieving computer security. Immune theory was first applied to computer abnormality detection by Forrest et al. After then, several proposition in the area of immune system as made tremendous success. Negative selection algorithm, while not reacting to the self cells uses the immune system capability to detect unknown antigens. Its mechanism protects body against self reactive lymphocytes. Receptors are made through a pseudo-random genetic re-arrangement process during the generation of T-cells (2); they then undergo a censoring process in the thymus called the negative selection. In this process T-cells that do not bind to self-proteins are destroyed. Therefore, immunological function and protection of the body against foreign antigens is possible through circulation of matured T-cells. Artificial negative selection algorithm was proposed to follow that pattern; generating random detectors and then discard those that match self samples. It is a spam detection in which training data from only one of the classes are available. With these illustrations, the traditional negative selection algorithm believes that all self are not dangerous and all non- self

Most damage-assessment strategies for dynamic systems only distinguish between undamaged and damaged conditions without recognizing the level or type of damage or considering unseen conditions. This paper proposes a novel framework for structural health monitoring (SHM) that combines supervised and unsupervised learning techniques to assess damage using a system’s structural response (e.g., the acceleration response of big infrastructures). The objective is to enhance the benefits of a supervised learning framework while addressing the challenges of working in an SHM context. The proposed framework uses a Linear Discriminant Analysis (LDA)/Probabilistic Linear Discriminant Analysis (PLDA) strategy that enables learning the distributions of known classes and the performance of probabilistic estimations on new incoming data. The methodology is developed and proposed in two versions. The first version is used in the context of controlled, conditioned monitoring or for post-damage assessment, while the second analyzes the single observational data. Both strategies are built in an automatic framework able to classify known conditions and recognize unseen damage classes, which are then used to update the classification algorithm. The proposed framework’s effectiveness is first tested considering the acceleration response of a numerically simulated 12-degree-of-freedom system. Then, the methodology’s practicality is validated further by adopting the experimental monitoring data of the benchmark study case of the Z24 bridge.

In this paper, we present an Internet of Things framework for structural health monitoring. The proposed system detects the delimitation and debonding in composite concrete structures: more specifically, develop an IoT-Based Non-Destructive Test (NDT) to detect debonding between Carbon Fiber sheets and Concrete slabs. An Inter Digital Capacitance Sensor is designed and fabricated to function as the primary detection element. The sensor is embedded within an IoT node that manages the measurement process, captures the measurement location automatically, and performs essential data filtering operations with an attached graphical interface that allows basic control and early-access to the measured data. The IoT node integrates within the larger framework via wireless WiFi connection throughout which the data is transferred and control functions are administrated. The paper presents the system design details including mathematical modeling for the capacitance sensing element, finite element simulation results, and practical setup measured data.

(1989). Knowledge based systems in civil engineering. Civil Engineering Systems: Vol. 6, No. 1-2, pp. 3-4.

This study aims at developing a framework for assessing and tracking structural conditions by combining computer vision-based three-dimensional (3D) reconstruction and vibration measurement. Starting from a finite element (FE) model with unknown geometric and material properties, the framework seeks to obtain the accurate representation of the structure by applying a sequence of two types of operations: (1) image collection, 3D reconstruction, and the extraction of localized geometric properties, and (2) vibration measurement, operational modal analysis, and extraction of global stiffness properties. The first step, termed the computer vision step, is based on the alignment of the approximate initial finite element model to the 3D reconstruction, from which the area and second moment of area associated with each element of the model are retrieved by slicing the reconstructed mesh. The second step, termed the vibration measurement step, performs system identification using vibration measurement data, followed by the Bayesian model updating to calibrate the element stiffness. Every time one of those operations is performed, the new information is merged into all the information obtained previously. The framework is validated using a laboratory-scale bamboo cantilever beam. The experimental validation showed the potential of the proposed framework for effectively assessing and tracking localized structural conditions by heterogeneous sources of information.

The negative selection algorithm is one of the oldest immune-inspired classification algorithms and was originally intended for anomaly detection tasks in computer security. After initial enthusiasm, performance problems with the algorithm lead many researchers to conclude that negative selection is not a competitive anomaly detection technique. However, in recent years, theoretical work has lead to substantially more efficient negative selection algorithms. Here, we report the results of the first evaluation of negative selection with r-chunk and r-contiguous detectors that employs these novel algorithms. On a collection of 14 datasets from real-world sources, we compare negative selection with r-chunk and r-contiguous detectors against techniques based on kernels, finite state automata, and n-gram frequencies, and find that negative selection performs competitively, yielding a slightly better average performance than all other techniques investigated. Because this study represents, to our knowledge, the most comprehensive one of string-based negative selection to date, the widely held view that negative selection is not a competitive anomaly detection technique may be inaccurate.

The implementation of resilience-enhancing strategies on existing concrete gravity dams is a task of primary importance for the society. This aim can be achieved by estimating the risk of concrete dams against multi-hazards and by improving the structural control. Focusing the attention only on the seismic hazard, numerical models assume great importance due to the lack of case studies. However, for the same reason, numerical models are characterised by a high level of uncertainty which must be reduced by exploiting all available information. In this way reliable predictive models of the structural behaviour can be built, thus improving the seismic fragility estimation and the dam control. In this context, the observations recorded by the monitoring systems are a powerful source of information. In this thesis two Bayesian frameworks for Structural Health Monitoring (SHM) of existing concrete gravity dams are proposed. On the one hand, the first proposed framework is defined for static SHM, so the dam displacements are considered as Quantity of Interest (QI). On the other hand, a dynamic SHM framework is defined by assuming the modal characteristics of the system as QI. In this second case an innovative numerical algorithm is proposed to solve the well-known mode matching problem without using the concept of system mode shapes or objective functions. Finally, a procedure based on the Optimal Bayesian Experimental Design is proposed in order to design the devices layout by optimizing the probability of damage detection. In all the three procedures the general Polynomial Chaos Expansion (gPCE) is widely used in order to strongly reduce the computational burden, thus making possible the application of the proposed procedure even without High Performance Computing (HPC). Two real large concrete gravity dams are analysed in order to show the effectiveness of the proposed procedures in the real world. In the first part of the thesis an extended literature review on the fragility assessment of concrete gravity dams and the application of SHM is presented. Afterwards, the statistical tools used for the definition of the proposed procedures are introduced. Finally, before the presentation of SHM frameworks, the main sources of uncertainties in the numerical analysis of concrete gravity dams are discussed in order to quantify their effects on the model outputs.

In this article, we present a new negative selection algorithm which the self-data is organized as a R-Tree structure. And the negative selection process could be transformed into the data query process in the self-R-Tree, if a new detector is indexed in any leaf node it will be dropped. As the time complexity of data query process in the tree is in the log level, the negative selection process of our algorithm is superior to the linearly comparation procedure in the traditional negative selection algorithms.

An optimal sensor placement design framework for structural health monitoring using Bayes risk