Optimization of life-cycle cost of retrofitting school buildings under seismic risk using evolutionary support vector machine

The assessment of the seismic performance of existing school buildings is especially important in seismic-disaster mitigation planning. Utilizing a support vector machine coupled with a fast messy genetic algorithm, this study developed two inference models, both using the same input variables: i.e., 18 building characteristics selected based on expert opinion. The first model was designed to judge whether a building needs to be retrofitted; and the second, to estimate the cost of retrofitting buildings to specific levels. The study proposes a life-cycle seismic risk framework that takes into account projections of the seismic risk a given building will confront over the course of its entire existence, and thus helps determine the economically optimal level of retrofitting. The results of a case study indicate that the higher upfront cost of retrofitting that is required to reach higher seismic performance levels could, depending on the level of predicted seismic risk, be offset by lower repair costs in the long run. It is hoped that this research will serve as a basis for further studies of the assessment of the life-cycle seismic risk of school buildings, with the wider aim of arriving at an economically optimal building-retrofit policy

Novel Genetic Algorithm-Based Evolutionary Support Vector Machine for Optimizing High-Performance Concrete Mixture

An effective method for optimizing high-performance concrete mixtures can significantly benefit the construction industry. However, traditional proportioning methods are not sufficient because of their expensive costs, limitations of use, and inability to address nonlinear relationships among components and concrete properties. Consequently, this research introduces a novel genetic algorithm (GA)–based evolutionary support vector machine (GA-ESIM), which combines the K-means and chaos genetic algorithm (KCGA) with the evolutionary support vector machine inference model (ESIM). This model benefits from both complex input-output mapping in ESIM and global solutions with faster convergence characteristics in KCGA. In total, 1,030 data points from concrete strength experiments are provided to demonstrate the application of GA-ESIM. According to the results, the newly developed model successfully produces the optimal mixture with minimal prediction errors. Furthermore, a graphical user interface is utilized to assist users in performing optimization tasks

Development of data-mining technique for seismic vulnerability assessment

Assessment of seismic vulnerability of urbaninfrastructure is an actual problem, since the damage caused byearthquakes is quite significant. Despite the complexity of suchtasks, today’s machine learning methods allow the use of “fast”methods for assessing seismic vulnerability. The article proposesa methodology for assessing the characteristics of typical urbanobjects that affect their seismic resistance; using classification andclustering methods. For the analysis, we use kmeans and hkmeansclustering methods, where the Euclidean distance is used as ameasure of proximity. The optimal number of clusters isdetermined using the Elbow method. A decision-making model onthe seismic resistance of an urban object is presented, also themost important variables that have the greatest impact on theseismic resistance of an urban object are identified. The studyshows that the results of clustering coincide with expert estimates,and the characteristic of typical urban objects can be determinedas a result of data modeling using clustering algorithms

Towards disaster risk mitigation on large-scale school intervention programs

Education infrastructure is one of the main barriers on school quality in Low- and Middle-Income Countries (L&MICs), since it is insufficient and unevenly distributed. Improving the school infrastructure is needed to provide a high-quality education environment. Although research on how to improve the infrastructure is available, there is still a lack of a consistent and systematic approach to develop large-scale interventions at the national or regional level. To fill this gap, we propose a data-driven methodology with the purpose of developing a prioritization of interventions to carry out a seismic disaster risk reduction program. The method starts by identifying groups of similar buildings using clustering analysis, starting with a seismic taxonomy as descriptor (i.e., model input). Then, domain experts analyze the suggested clusters to design scalable interventions for the representative building of each cluster. The proposed data-driven methodology requires experts’ criteria in each step to validate the results and make them applicable, but significantly reduces the bias by automating the decision-making process. We use as case study the Dominican Republic public school infrastructure and present the results of the application of the proposed method. The method presented herein is extensible to other infrastructure portfolios, as well as to other types of hazards

Preliminary planning efficiency evaluation for school buildings considering the tradeoffs of MOOP and planning preferences

Seismic resistance and cost effectiveness are often two important building planning objectives for architects. However, these objectives nearly always share a negative correlation with each other, which can cause planning delays and confusion. The conflict between these two is a Multi-Objective Optimization Problem (MOOP). Besides, building planning often encompasses both subjective and objective factors. However, most current efficiency evaluation methods focus on the latter and underemphasize the former. Current efficiency evaluation methods are thus not optimized for actual building planning needs. The aim of this study is to develop a new planning efficiency evaluation approach to resolve the above problems. Research methods include the indifference curve, efficient frontier and Data Envelopment Analysis (DEA). The indifference curve deduced the subjective planning preferences of architects; efficient frontier theory constructed the efficient frontier of school buildings; and DEA evaluated the efficiency of various building factors objectively. A total of 326 school buildings in Taichung City, Taiwan in an empirical study designed to illustrate proposed approach effectiveness. The results show that using only objective evaluation or subjective recognition is insufficient to explain the true nature of building planning. Findings can serve as benchmarks for inefficient school buildings at preliminary planning stage

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

An overview of a leader journal in the field of transport: a bibliometric analysis of “Computer-Aided Civil and Infrastructure Engineering” from 2000 to 2019

Computer-Aided Civil And Infrastructure Engineering (CACAIE) is an international journal, and the first documents was published from 1980. This article is to make an overview based on bibliometric analysis to celebrate the 35th anniversary of CACAIE till 2019. At present, 1045 publications can be indexed in the Clarivate Analytics Web of Science (WoS) from 2000 to 2019, and we explore the characteristics of these publications by bibliometric methods and tools (VOSviewer and CiteSpace). First, the fundamental information of publications is given with the help of some bibliometric indicators, such as the number of citations and h-index. According to high-citing and high-cited publications, we analyse that who pays closer attention to the journal and what the journal most focuses on considering sources, countries/regions, institutions and authors. After that, the influential countries/regions and references are presented, and collaboration networks are given to show the relationship among countries/regions, institutions and authors. In order to understand the development trends and hot topics, co-occurrence analysis and timeline view of keywords are made to be visual. In addition, publications in four fields – Construction & Building Technology; Engineering, Civil; Transportation Science & Technology; Computer Science, Interdisciplinary Applications – that CACAIE refers are summarized, and further discussions are made for the journal and scholars. Finally, some main findings are concluded according to all analysis. This article provides a certain reference for scholars and journals to further research and promote the scientific-technological progress. First published online 6 January 202

Semi-Active Adaptive Control of Coupled Structures for Seismic Hazard Mitigation

The research presented in this dissertation examines innovative structures connected with smart control devices driven by adaptive control methods. The research focuses on understanding the dynamics of coupled structures and evaluating the merits of adaptive control in enhancing the seismic performance of these structures and dealing with uncertainties. Coupled structures is recognized as an effective strategy to protect civil structures from seismic excitations. Coupling of adjacent structures has proved to offer functional benefits such as the potential for shifting the buildings’ natural frequencies, likely leading to a reduction in the natural period of vibration. Structural performance is further enhanced by implementing energy-dissipative devices to connect adjacent buildings to minimize the seismic structural responses. One of the main challenges to control civil structures is the high uncertainty involved throughout their lifetimes. Adaptive control promises to deal with changes in structures’ characteristics, such as seismic-induced damage. The simple adaptive control method, which is a reference-model following scheme, is used in the current research to improve the seismic behavior of adjacent buildings connected by structural links where control devices are implemented. The philosophy of the simple adaptive control method is that an actual system (often called plant) can be forced to track the behavior of pre-determined trajectories through adjustable adaptive gains. The effectiveness of the simple adaptive controller in reducing the seismic responses is compared with other adaptive and non-adaptive control methods. The results reveal that the simple adaptive controller is effective in alleviating the structural responses and dealing with uncertainties of coupled structures with both linear and nonlinear behavior. The results also show that the coupling strategy is viable for reducing the structural responses under seismic excitations

Sustainable Structural Design for High-Performance Buildings and Infrastructures

Exceptional design loads on buildings and structures may have different causes, including high-strain natural hazards, man-made attacks and accidents, and extreme operational conditions. All of these aspects can be critical for specific structural typologies and/or materials that are particularly sensitive. Dedicated and refined methods are thus required for design, analysis, and maintenance under structures’ expected lifetimes. Major challenges are related to the structural typology and material properties. Further issues are related to the need for the mitigation or retrofitting of existing structures, or from the optimal and safe design of innovative materials/systems. Finally, in some cases, no design recommendations are available, and thus experimental investigations can have a key role in the overall process. For this SI, we have invited scientists to focus on the recent advancements and trends in the sustainable design of high-performance buildings and structures. Special attention has been given to materials and systems, but also to buildings and infrastructures that can be subjected to extreme design loads. This can be the case of exceptional natural events or unfavorable ambient conditions. The assessment of hazard and risk associated with structures and civil infrastructure systems is important for the preservation and protection of built environments. New procedures, methods, and more precise rules for safety design and the protection of sustainable structures are, however, needed

Neural Networks: Training and Application to Nonlinear System Identification and Control

This dissertation investigates training neural networks for system identification and classification. The research contains two main contributions as follow:1. Reducing number of hidden layer nodes using a feedforward componentThis research reduces the number of hidden layer nodes and training time of neural networks to make them more suited to online identification and control applications by adding a parallel feedforward component. Implementing the feedforward component with a wavelet neural network and an echo state network provides good models for nonlinear systems.The wavelet neural network with feedforward component along with model predictive controller can reliably identify and control a seismically isolated structure during earthquake. The network model provides the predictions for model predictive control. Simulations of a 5-story seismically isolated structure with conventional lead-rubber bearings showed significant reductions of all response amplitudes for both near-field (pulse) and far-field ground motions, including reduced deformations along with corresponding reduction in acceleration response. The controller effectively regulated the apparent stiffness at the isolation level. The approach is also applied to the online identification and control of an unmanned vehicle. Lyapunov theory is used to prove the stability of the wavelet neural network and the model predictive controller. 2. Training neural networks using trajectory based optimization approachesTraining neural networks is a nonlinear non-convex optimization problem to determine the weights of the neural network. Traditional training algorithms can be inefficient and can get trapped in local minima. Two global optimization approaches are adapted to train neural networks and avoid the local minima problem. Lyapunov theory is used to prove the stability of the proposed methodology and its convergence in the presence of measurement errors. The first approach transforms the constraint satisfaction problem into unconstrained optimization. The constraints define a quotient gradient system (QGS) whose stable equilibrium points are local minima of the unconstrained optimization. The QGS is integrated to determine local minima and the local minimum with the best generalization performance is chosen as the optimal solution. The second approach uses the QGS together with a projected gradient system (PGS). The PGS is a nonlinear dynamical system, defined based on the optimization problem that searches the components of the feasible region for solutions. Lyapunov theory is used to prove the stability of PGS and QGS and their stability under presence of measurement noise