Novel integration of extreme learning machine and improved Harris hawks optimization with particle swarm optimization-based mutation for predicting soil consolidation parameter

The study proposes an improved Harris hawks optimization(IHHO) algorithm by integrating the standard Harris hawks optimization(HHO) algorithm and mutation-based search mechanism for developing a high-performance machine learning solution for predicting soil compression index. HHO is a newly introduced meta-heuristic optimization algorithm(MOA) used to solve continuous search problems.Compared to the original HHO, the proposed IHHO can evade trapping in local optima, which in turn raises the sear...     

An iterative curve fitting approach for solving the Weber problem in spatial economics

Early methods for solving the Weber problem by locating the point of minimum aggregate distance employed physical analogues. There is no closed-form mathematical method for replicating these mechanical procedures because analytical procedures result in high order polynomials requiring numerical methods. Iterative techniques using gradient related methods can be used; but in the small number of cases where a trial solution coincides with a data point or where the final solution itself is a data point, gradient methods are unable to reach a solution. Other common iterative methods, which are not gradient related, avoid these difficulties, but are less efficient. The method presented in algorithm form does not encounter difficulty when a trial solution encounters a data point. A paraboloid is fitted through five points on the surface formed by the total distances and derivatives are used to locate a trial minimum. The trial minimum becomes the center of the next paraboloid and the process is continued. The algorithm presented here is simpler to program and run than the gradient related methods, when they are combined with a separate test for the conditions of a minimum. In addition, the algorithm is more efficient than the non-gradient related methods such as the grid search technique.     

MACHINE LEARNING AND ITS APPLICATION TO CIVIL ENGINEERING SYSTEMS

Today's state-of-the-art expert systems are plagued by four major problems: brittleness, lack of metaknowledge, knowledge acquisition, and validation. Knowledge acquisition by itself is a very time consuming and tedious process. The uncertainty of information and erroneous data have also caused knowledge engineers anxious moments. In order to address these problems, several machine learning techniques supported by well-formulated theories and algorithms, have been developed. In this article some of these techniques are reviewed along with examples of their application to civil engineering problems. The techniques presented either fall under the category “learning from examples” (commonly referred to as inductive learning) including the ID3 algorithm, the rough sets theory, and the PROTOS algorithm, or “learning from observations” (also known as conceptual clustering) including the COBWEB algorithm, or a combination of both.     

Machine Learning User Preferences for Structural Design

Abstract: The design process often proceeds through iterative stages of design configuration, analysis, evaluation, and redesign with the ultimate goal of optimization. Numerical methods for structural design optimization of only one attribute such as weight, strength, or cost are well known. However, these methods do not reflect the fact that designs are evaluated by the user in terms of their performance in several attributes. It has been extremely difficult to incorporate multiple attributes into design optimization algorithms because the acceptable tradeoffs between these attributes vary significantly between users.
This paper presents a new method for learning user-specific preferences and integrating them into the design evaluation, analysis, and optimization process in a meaningful way. The approach is a synthesis of formal decision theoretic methods with conventional design analysis techniques. The overall design objective is optimization of multiattribute utility from the viewpoint of the user.
A user-interactive computer-aided Multiattribute Structural Design Evaluation and Optimization System (MSDEOS) is presented. It enables machine learning of the user's willingness to make tradeoffs between performance attributes. With this system, it is feasible to integrate site-specific consideration of multiple attributes directly into computer aids for structural design optimization. Two examples are presented: seismic design, where tradeoffs are made between cost and damage index, and design of a three-story steel frame structure, where attributes are cost and drift index. The system learns the preferences of different users and reflects those preferences through the identification of a different optimal solution for each user.     

Rough set-based hybrid fuzzy-neural controller design for industrial wastewater treatment

Recent advances in control engineering suggest that hybrid control strategies, integrating some ideas and paradigms existing in different soft computing techniques, such as fuzzy logic, genetic algorithms, rough set theory, and neural networks, may provide improved control performance in wastewater treatment processes. This paper presents an innovative hybrid control algorithm leading to integrate the distinct aspects of indiscernibility capability of rough set theory and search capability of genetic algorithms with conventional neural-fuzzy controller design. The methodology proposed in this study employs a three-stage analysis that is designed in series for generating a representative state function, searching for a set of multi-objective control strategies, and performing a rough set-based autotuning for the neural-fuzzy logic controller to make it applicable for controlling an industrial wastewater treatment process. Research findings in the case study clearly indicate that the use of rough set theory to aid in the neural-fuzzy logic controller design can produce relatively better plant performance in terms of operating cost, control stability, and response time simultaneously, which is effective at least in the selected industrial wastewater treatment plant. Such a methodology is anticipated to be capable of dealing with many other types of process control problems in waste treatment processes by making only minor modifications.     

Reasserting the importance of qualitative methods in planning

The goals of this paper are to suggest what is lost by ignoring field research and to reassert the significance of qualitative research methods to the field of urban and regional planning. I assume that methodological choices between quantitative and qualitative methods are a matter of determining which research method is best suited to capture a particular ‘data slice’. Research methods are techniques to retrieve data and carry no theoretical baggage to bias their data set. To illustrate the significance of field research I discuss three points. First, I illustrate some of the technical strengths and weaknesses associated with qualitative research approaches, and highlight three important issues that are lost without field research methods: a link between planning researchers and the people they plan for, quality of life issues, and informal/illegal activity. Second, I propose that by pursuing a mixed-method research methodology, some of the issues lost by ignoring field research methods can be recaptured. Finally, I give an example of how I researched informal street vendors as part of the urban landscape through a mixed-method approach. This paper argues that until planning research re-evaluate what is being lost by their pursuit of quantitative methods, critical problems in the social realm will continue to lack the attention they need from urban and regional planners.Why all that obsession with Method? Because it was through Method that you arrived at the solution... (Umberto Eco, Foucault's Pendulum, 1989, p. 384)     

非介入性评价方法研究:广州某居住小区建成环境主观评价

以广州某小区的使用后评价案例为依托,运用非介入性评价方法进行主观评价的技术问题。研究的目的一是从主观评价方法学的大背景去讨论非介入性评价方法的应用技术,促进主观评价方法的多元化研究;二是利用评价获得的反馈信息来优化该小区以后各期的建筑设计：最后从方法学的角度摸索出一套以建筑管理文档为基础,以探索性研究、研究设计、文档编录与分析三大技术模块为核心的非介入性评价技术模式。     

Machine learning based novelty detection using modal analysis

Among many structural assessment methods, the change of modal characteristics is considered a well‐accepted damage detection method. However, the presence of environmental or operational variations may pollute the baseline and prevent a dependable assessment of the change. In recent years, the use of machine learning algorithms gained interest within structural health community, especially due to their ability and success in the elimination of ambient uncertainty. This paper proposes an end‐to‐end architecture to detect damage reliably by employing machine learning algorithms. The proposed approach streamlines (a) collection of structural response data, (b) modal analysis using system identification, (c) learning model, and (d) novelty detection. The proposed system aims to extract latent features of accessible modal parameters such as natural frequencies and mode shapes measured at undamaged target structure under temperature uncertainty and to reconstruct a new representation of these features that is similar to the original using well‐established machine learning methods for damage detection. The deviation between measured and reconstructed parameters, also known as novelty index, is the essential information for detecting critical changes in the system. The approach is evaluated by analyzing the structural response data obtained from finite element models and experimental structures. For the machine learning component of the approach, both principal component analysis (PCA) and autoencoder (AE) are examined. While mode shapes are known to be a well‐researched damage indicator in the literature, to our best knowledge, this research is the first time that unsupervised machine learning is applied using PCA and AE to utilize mode shapes in addition to natural frequencies for effective damage detection. The detection performance of this pipeline is compared to a similar approach where its learning model does not utilize mode shapes. The results demonstrate that the effectiveness of the damage detection under temperature variability improves significantly when mode shapes are used in the training of learning algorithm. Especially for small damages, the proposed algorithm performs better in discriminating system changes.     

On the potential of genetic algorithms in urban drainage modeling

A genetic algorithm (GA) is a stochastic search algorithm that applies the biological concept of survival of the fittest in order to search for the optimal solution to a problem. In this paper we explore the potential and the benefit of using GAs for solving problems in urban drainage modeling. The main problem areas where such methods are assumed to have some benefit as compared to traditional procedures are identified from the literature as model calibration and model predictive control. The use of GAs for multi-criteria decision analysis is not reported in the context of urban drainage modeling but believed to be an interesting field of application. The methodology is discussed by means of benchmark problem sets for each of the applications.     

Detail control strategies for topology optimization in architectural design and development

With the ability to generate forms with high efficiency and elegant geometry, topology optimization has been increasingly used in architectural and structural designs. However, the conventional topology optimization techniques aim at achieving the structurally most efficient solution without any potential for architects or designers to control the design details. This paper introduces three strategies based on Bi-directional Evolutionary Structural Optimization (BESO) method to artificially pre-design the topological optimized structures. These strategies have been successfully applied in the computational morphogenesis of various structures for solving practical design problems. The results demonstrate that the developed methodology can provide the designer with structurally efficient and topologically different solutions according to their proposed designs with multi-filter radii, multi-volume fractions, and multi-weighting coefficients. This work establishes a general approach to integrating objective topology optimization methods with subjective human design preferences, which has great potential for practical applications in architecture and engineering industry.     

Robust predictive modelling of water pollution using biomarker data

This paper describes the methodology of building a predictive model for the purpose of marine pollution monitoring, based on low quality biomarker data. A step-by-step, systematic data analysis approach is presented, resulting in design of a purely data-driven model, able to accurately discriminate between various coastal water pollution levels.The environmental scientists often try to apply various machine learning techniques to their data without much success, mostly because of the lack of experience with different methods and required ‘under the hood’ knowledge. Thus this paper is a result of a collaboration between the machine learning and environmental science communities, presenting a predictive model development workflow, as well as discussing and addressing potential pitfalls and difficulties.The novelty of the modelling approach presented lays in successful application of machine learning techniques to high dimensional, incomplete biomarker data, which to our knowledge has not been done before and is the result of close collaboration between machine learning and environmental science communities.     

An approach for building design optimization using design of experiments

Building simulation based optimization involves direct coupling of the optimization algorithm to a simulation model, making it computationally intensive. To overcome this issue, an approach is proposed using a combination of experimental design techniques (fractional factorial design and response surface methodology). These techniques approximate the simulation model behavior using surrogate models, which are several orders of magnitude faster than the simulation model. Fractional factorial design is used to identify the significant design variables. Response surface methodology is used to create surrogate models for the annual cooling and lighting energy with the screened significant variables. The error for these models is less than 10%, validating their effectiveness. These surrogate models speed up optimization with genetic algorithms, for single- and multi-objective optimization problems and scenario analyses, resulting in a better solution. Thus, optimization becomes possible within reasonable computational time with the proposed methodology. This framework is illustrated using the case study of a three-storey office building for New Delhi.     

The ‘Urban Elements’ method for teaching parametric urban design to professionals

The article proposes a method for teaching advanced urban design to working professionals in Singapore. The article aims to expand the discourse on parametric urban design education by introducing ‘Urban Elements’ as conceptual urban design instruments with an inherent rule-based logic, which can help to bridge gaps in teaching parametric urban design thinking. As case study we present a course developed for and delivered to the Urban Redevelopment Authority (URA) in Singapore in 2017 by the Future Cities Laboratory at the Singapore-ETH Centre. The article reports on the pedagogical method, course results and course feedback. The main difficulties of teaching professionals in parametric urban design are described and possible reasons and improvements are discussed. The results show that participants using the ‘Urban Elements’ method successfully linked theoretical input to urban design problems, applied evidence-based urban design strategies to these problems, and developed parametric definitions to explore the solution spaces of these urban design challenges. The teaching methodology presented opens up a new research field for urban design pedagogy at the intersection of explicating urban design intent, integrating multidisciplinary knowledge and exploring new software driven tools.     

Best practices in managing,supervising, and assessing architectural graduation projects: A quantitative study

Graduation project courses refer to the culmination of the learning experiences of higher education. These courses consolidate the disciplinary knowledge gained during architectural education while they integrate most of the learning outcomes of a program, which are intended to prepare students for their transition to the profession of architecture. The educational methods of these courses require constant attention, regular review, and continuous development to remain consistent with the changing standards of the profession given the significance of these courses. The problem lies in the diversity and controversy of these methods, thereby implying inconsistency in the best practices. In this study, several questions are raised in terms of the nature of these courses, enrollment criteria, topic selection, learning experience, and assessment methods. This study aims to investigate the best practices of managing, supervising, and assessing architectural graduation projects to provide guidelines for establishing and/or developing these courses. An analytical deductive methodology is adopted. This methodology is supported by a survey of a sample of 105 worldwide academic architects and is structured into four sections, namely, the analysis of the components of graduation projects, the survey and its procedures, the quantitative findings of the survey, and a discussion of the issues and results. This study draws conclusions to its research questions, thereby extending its influence on the quality of architectural programs and the benefits for individuals who are concerned with their development.     

“Adaptive radiation” optimization for climate adaptive building facade design strategy

This paper introduces an evolutionary algorithm methodology to solve facade optimization problems in different climates. The algorithm is based on the improvement of simple Genetic Algorithm (GA). The concept of Adaptive Radiation (AR) is derived from the biological process of adaptation where specific species are evolutionarily adapted to their immediate ecological niches. This algorithm obtains near global optimal solutions in significantly less computation time than simple GA. AR is implemented in three different climates in the United States to demonstrate its robustness and efficiency. Climate adaptive facade design strategies for these climates are illustrated based on the optimization results.     

基于WebGIS的农业经济与环境评价决策支持系统的设计

探讨了基于网络地理信息系统的农业经济与环境评价决策支持系统的设计思想和相关的核心技术 ,主要讨论了利用 Arc SDE和 Arc IMS以及 MSSQL Server等数据库和网络集成技术 ,并对空间数据仓库理论应用于农业经济环境评价的思路作了介绍 .以美国加里福尼亚州YOLU县农业经济环境评价的研究和实验为例 ,对系统进行了测试和验证 .结果表明 ,基于网络地理信息系统的农业经济环境评价决策支持系统的设计是合理的 ,思路是正确的 ,可以作为我国这方面研究和应用的借鉴与参考     

Optimum design of post‐tensioned axially symmetric cylindrical walls using novel hybrid metaheuristic methods

An efficient methodology for various structural design problems is needed to optimize the total cost for structures. Although some methods seem to be efficient for applications, due to using special algorithm parameters, computational cost, and some other reasons, there is still much to be done in order to develop an effective method for general design applications. This paper describes the influence of the selected procedure on the design of cost‐optimized, post‐tensioned axially symmetric cylindrical reinforced concrete walls. In this study, the optimum design of axially symmetric cylindrical walls using several metaheuristic algorithms is investigated. The new generation algorithms used in the study are flower pollination algorithm, teaching–learning‐based optimization, and Jaya algorithm (JA). These algorithms are also compared with one of the previously developed algorithm called harmony search. The numerical examples were done for walls with 4‐ to 10‐m height and for 1, 5, 10, 15, 20, and 25 post‐tensioned load cases, respectively. Several independent runs are conducted, and in some of these runs, JA may trap to a local solution. To overcome this situation, hybrid algorithms such as JA using Lévy flights, JA using Lévy flights with probabilistic student phase (JALS), JA using Lévy Flights with consequent student phase (JALS2), and JA with probabilistic student phase are developed. It is seen that in many respects, the JALS2 and JALS are the most effective within the proposed hybrid approaches.     

Structural optimization using interior penalty function

Mathematical programming methods are among the most powerful optimization techniques. They may be classified into direct or indirect methods. In the indirect methods, the constrained design problem is converted into a sequence of unconstrained problems using penalty functions. In this way, the optimal solution of a constrained problem may be obtained using one of the unconstrained search techniques. The interior penalty function appears to be the most reliable uncon strained method while the variable metric method seems to be an extremely powerful algorithm. This paper presents the use of the interior penalty function coupled with the variable metric method for the solution of structural design optimization problems.     

Comparison of response surface and neural network with other methods for structural reliability analysis

The evaluation of the failure probability and safety levels of structural systems is of extreme importance in structural design, mainly when the variables are eminently random. Some examples of random variables on real structures are material properties, loads and member dimensions. It is necessary to quantify and compare the importance of each one of these variables in the structural safety. Many researchers studied structural reliability problems and nowadays there are several approaches for these problems. Two recent approaches, the Response Surface (RS) and the Artificial Neural Network (ANN) techniques, have emerged attempting to solve complex and more elaborated problems. In this work, these two techniques are presented, and comparison are carried out using the well known First Order Reliability Method (FORM), Direct Monte Carlo Simulation and Monte Carlo Simulation with Adaptive Importance Sampling technique with approximated and exact limit state functions. Problems with simple limit state functions (LSF) and closed form solutions of the failure probability are solved in order to highlight the advantages and shortcomings using these techniques. Some remarks are outlined regarding the fact that RS and ANN techniques have presented equivalent precision levels. It is observed that in problems where the computational cost of structural evaluations (looking for the failure probability and safety levels) is high, these two techniques may turn feasible the evaluation of the structural reliability through simulation techniques.     

A fuzzy approach to construction project risk assessment

The increasing complexity and dynamism of construction projects have imposed substantial uncertainties and subjectivities in the risk analysis process. Most of the real-world risk analysis problems contain a mixture of quantitative and qualitative data; therefore quantitative risk assessment techniques are inadequate for prioritizing risks. This article presents a risk assessment methodology based on the Fuzzy Sets Theory, which is an effective tool to deal with subjective judgement, and on the Analytic Hierarchy Process (AHP), which is used to structure a large number of risks. The proposed methodology incorporates knowledge and experience acquired from many experts, since they carry out the risks identification and their structuring, and also the subjective judgements of the parameters which are considered to assess the overall risk factor: risk impact, risk probability and risk discrimination. All of these factors are expressed by qualitative scales which are defined by trapezoidal fuzzy numbers to capture the vagueness in the linguistic variables. The most notable differences with other fuzzy risk assessment methods are the use of an algorithm to handle the inconsistencies in the fuzzy preference relation when pair-wise comparison judgements are necessary, and the use of trapezoidal fuzzy numbers until the defuzzification step. An illustrative example on risk assessment of a rehabilitation project of a building is used to demonstrate the proposed methodology.