Spiking Neural [Formula: see text] Systems are Neural System models characterized by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural [Formula: see text] systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these [Formula: see text] systems, a specified number of spikes are consumed and a specified number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron. [Formula: see text]In the present work, a novel communication strategy among neurons of Spiking Neural [Formula: see text] Systems is proposed. In the resulting models, called Spiking Neural [Formula: see text] Systems with Communication on Request, the spikes are requested from neighboring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural [Formula: see text] systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron). [Formula: see text]The Spiking Neural [Formula: see text] Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems.

Spiking Neural P Systems with Communication on Request.

The article presents a deep neural network model for the prediction of the compressive strength of foamed concrete. A new, high-order neuron was developed for the deep neural network model to improve the performance of the model. Moreover, the cross-entropy cost function and rectified linear unit activation function were employed to enhance the performance of the model. The present model was then applied to predict the compressive strength of foamed concrete through a given data set, and the obtained results were compared with other machine learning methods including conventional artificial neural network (C-ANN) and second-order artificial neural network (SO-ANN). To further validate the proposed model, a new data set from the laboratory and a given data set of high-performance concrete were used to obtain a higher degree of confidence in the prediction. It is shown that the proposed model obtained a better prediction, compared to other methods. In contrast to C-ANN and SO-ANN, the proposed model can genuinely improve its performance when training a deep neural network model with multiple hidden layers. A sensitivity analysis was conducted to investigate the effects of the input variables on the compressive strength. The results indicated that the compressive strength of foamed concrete is greatly affected by density, followed by the water-to-cement and sand-to-cement ratios. By providing a reliable prediction tool, the proposed model can aid researchers and engineers in mixture design optimization of foamed concrete.

Deep neural network with high-order neuron for the prediction of foamed concrete strength

Sustainable decision-making in civil engineering, construction and building technology can be supported by fundamental scientific achievements and multiple-criteria decision-making (MCDM) theories. The current paper aims at overviewing the state of the art in terms of published papers related to theoretical methods that are applied to support sustainable evaluation and selection processes in civil engineering. The review is limited solely to papers referred to in the Clarivate Analytic Web of Science core collection database. As the focus is on multiple-criteria decision-making, it aims at reviewing how the papers on MCDM developments and applications have been distributed by period of publishing, by author countries and institutions, and by journals. Detailed analysis of 2015–2017 journal articles from two Web of Science categories (engineering civil and construction building technology) is presented. The articles are grouped by research domains, problems analyzed and the decision-making approaches used. The findings of the current review paper show that MCDM applications have been constantly growing and particularly increased in the last three years, confirming the great potential and prospects of applying MCDM methods for sustainable decision-making in civil engineering, construction and building technology.

Sustainable Decision-Making in Civil Engineering, Construction and Building Technology

Building information modeling based building design optimization for sustainability

http://scientiairanica.sharif.edu/article_2093_39002dbcaa0fc5bb8f4f33b6b189a977.pdf

Hybrid multiple criteria decision making methods: a review of applications in engineering

Self-constructing wavelet neural network algorithm for nonlinear control of large structures

Sustainable building design has become a wide and multidisciplinary research endeavor including mechanical, electrical, electronic, communication, acoustic, architectural, and structural engineering. It involves the participation of owners, contractors, suppliers and building users. There has been a lot of talk about sustainable buildings in the past few years. Most of the published research is concerned with saving energy and water and making the buildings more environmentally friendly by, say, reducing the carbon emissions. In this article, sustainable building design is reviewed from the viewpoint of structural engineering. Different strategies presented in the literature are summarized. Finally, the authors argue that the next big leap in sustainable building design should come from the integration of the smart structure technology including the use of hybrid and semi-active vibration controllers that can result in substantially lighter and more efficient structures.

/pdf/sustainable-building-design-24fgep3qgu.pdf

Sustainable building design

A robust filtered sliding mode control (SMC) approach is presented for vibration control of wind-excited highrise building structures. Rather than using a Lyapunov-function based control design, an alternative way is provided to find the control force based on the equivalent control force principle to obtain the control force. A low pass filter is properly selected to remove the high-frequency components of the control force while retaining the structural stability. The performance of the proposed filtered SMC is evaluated by application to a wind-excited 76-story building benchmark problem equipped with an active tuned mass damper (ATMD) on the roof. Due to the elimination of high-frequency part of the control force, the structure, sensors, actuators, and dampers are all less excited, and consequently their response is reduced compared with the unfiltered SMC approach. In addition, the required control forces are reduced which means a reduction in the size of actuators, thus making their implemen...

/pdf/robust-vibration-control-of-wind-excited-highrise-building-vxlyprwjap.pdf

Robust vibration control of wind-excited highrise building structures

Sliding mode control (SMC) is among the popular approaches for control of systems, especially for unknown nonlinear systems. However, the chattering in SMC is generally a problem that needs to be resolved for better control. A time-varying method is proposed for determining the sliding gain function in the SMC. Two alternative tuning algorithms are proposed for reducing the sliding gain function for systems. The first algorithm is for systems with no noise and disturbance but with or without unmodeled dynamics. The second algorithm is for systems with noise, disturbance, unmodeled dynamics, or any combination of them. Compared with the state-dependent, equivalent-control-dependent, and hysteresis loop methods, the proposed algorithms are more straightforward and easy to implement. The performance of the algorithms is evaluated for five different cases. A 90% to 95% reduction of chattering is achieved for the first algorithm used for systems with sensor dynamics only. By using the second algorithm, the chattering is reduced by 70% to 90% for systems with noise and/or disturbance, and by 25% to 50% for systems with a combination of disturbance, noise, and unmodeled dynamics.

Algorithms for chattering reduction in system control

The senior author developed the approach of interactive computer-aided design of steel structures on the first-generation personal computers in mid-1980s The goal of this research is to develop web-based courseware to complement undergraduate teaching of structural steel design Java Applets are being developed for interactive design of steel beams, compression members, beam-columns, connections, and plate girders In this article, an example applet, called Beam Tutor, is presented for design of single-span and multi-span steel beams It can design a variety of beam types such as simply-supported, cantilever, propped, fixed, and multi-span continuous beams subjected to a multitude of loadings including linearly varying load according to two different codes of the American Institute of Steel Construction: Allowable Stress Design (ASD) and Load and Resistance Factor Design (LRFD) Specifications With the help of the web-based courseware, students can find answers to many what-if scenarios quickly © 2009 Wiley Periodicals, Inc Comput Appl Eng Educ 20: 383–389, 2012

Nengmou Wang

Papers

Self-constructing wavelet neural network algorithm for nonlinear control of large structures

Sustainable building design

Robust vibration control of wind-excited highrise building structures

Algorithms for chattering reduction in system control

Web‐based tutor for interactive design of single‐span and continuous steel beams