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Intelligent Computing in Architecture, Engineering and Construction

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (15 May 2022) | Viewed by 17486

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Prof. Dr. Barbara Strug

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Institute of Applied Computers Science, Jagiellonian University, 30-348 Kraków, Poland
Interests: knowledge representation; CAD; machine learning; BIM; graph-based computing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Grażyna Ślusarczyk

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Guest Editor

Institute of Applied Computers Science, Jagiellonian University, 30-348 Kraków, Poland
Interests: graph grammars; computer-aided graphic design; pattern recognition; diagrammatic reasoning; algorithm analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The building and construction industry is a contributor to the global economy and currently employs about 7 percent of the world’s working-age population. It is estimated that about $10 trillion is spent on construction-related goods and services every year. In recent decades, the use of computer methods to aid and assist architects, engineers, and others involved in the building and construction industry has grown significantly, which has brought both opportunities and challenges. One of these elements is the introduction of building information modeling. The use of BIM has increased significantly within the building community and has largely contributed to the process of eliminating faults in designs. It has also provided a supportive layer for computing approaches helping to bring intelligent methods to the AEC (architecture, engineering and construction) domain.

This Applied Sciences Special Issue welcomes all new and advanced work on intelligent approaches to the AEC domain. All papers using BIM-based machine learning, artificial intelligence, data mining, knowledge representation, and processing approaches used and applied to AEC would be welcome, but other topics involving intelligent methods will also be considered. The focus of this Special Issue is on bringing intelligent methods to the building process at all its stages from conception, through the design and actual construction stages to the life-long maintenance phase.

Prof. Dr. Barbara Strug
Prof. Dr. Grażyna Ślusarczyk
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

AEC
BIM
machine learning
artificial intelligence
knowledge representation and processing
data mining

Published Papers (6 papers)

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Research

19 pages, 968 KiB

Open AccessFeature PaperArticle

A Selective Survey Review of Computational Intelligence Applications in the Primary Subdomains of Civil Engineering Specializations

by Konstantinos Demertzis, Stavros Demertzis and Lazaros Iliadis

Appl. Sci. 2023, 13(6), 3380; https://doi.org/10.3390/app13063380 - 7 Mar 2023

Cited by 3 | Viewed by 2710

Abstract

Artificial intelligence is the branch of computer science that attempts to model cognitive processes such as learning, adaptability and perception to generate intelligent behavior capable of solving complex problems with environmental adaptation and deductive reasoning. Applied research of cutting-edge technologies, primarily computational intelligence, including machine/deep learning and fuzzy computing, can add value to modern science and, more generally, to entrepreneurship and the economy. Regarding the science of civil engineering and, more generally, the construction industry, which is one of the most important in economic entrepreneurship both in terms of the size of the workforce employed and the amount of capital invested, the use of artificial intelligence can change industry business models, eliminate costly mistakes, reduce jobsite injuries and make large engineering projects more efficient. The purpose of this paper is to discuss recent research on artificial intelligence methods (machine and deep learning, computer vision, natural language processing, fuzzy systems, etc.) and their related technologies (extensive data analysis, blockchain, cloud computing, internet of things and augmented reality) in the fields of application of civil engineering science, such as structural engineering, geotechnical engineering, hydraulics and water resources. This review examines the benefits and limitations of using computational intelligence in civil engineering and the challenges researchers and practitioners face in implementing these techniques. The manuscript is targeted at a technical audience, such as researchers or practitioners in civil engineering or computational intelligence, and also intended for a broader audience such as policymakers or the general public who are interested in the civil engineering domain. Full article

(This article belongs to the Special Issue Intelligent Computing in Architecture, Engineering and Construction)

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14 pages, 2892 KiB

Open AccessArticle

Evolutionary Methods in House Floor Plan Design

by Katarzyna Grzesiak-Kopeć, Barbara Strug and Grażyna Ślusarczyk

Appl. Sci. 2021, 11(17), 8229; https://doi.org/10.3390/app11178229 - 5 Sep 2021

Cited by 6 | Viewed by 3737

Abstract

In this paper, an evolutionary technique is proposed as a method for generating new design solutions for the floor layout problem. The genotypes are represented by the vectors of numerical values of points representing endpoints of room walls. Equivalents of genetic operators for such a representation are proposed. A case study of the design problem of one-story houses is presented from the initial requirements to the best solutions. An evaluation method using requirement-weighted fitness function for evolved plans is also proposed. The obtained results as well as the advantages and issues related to such an approach are also discussed. Full article

(This article belongs to the Special Issue Intelligent Computing in Architecture, Engineering and Construction)

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23 pages, 1638 KiB

Open AccessFeature PaperArticle

A Reinforcement Learning-Based Approach to Automate the Electrochromic Glass and to Enhance the Visual Comfort

by Raghuram Kalyanam and Sabine Hoffmann

Appl. Sci. 2021, 11(15), 6949; https://doi.org/10.3390/app11156949 - 28 Jul 2021

Cited by 1 | Viewed by 1722

Abstract

Daylight is important for the well-being of humans. Therefore, many office buildings use large windows and glass facades to let more daylight into office spaces. However, this increases the chance of glare in office spaces, which results in visual discomfort. Shading systems in buildings can prevent glare but are not effectively adapted to changing sky conditions and sun position, thus losing valuable daylight. Moreover, many shading systems are also aesthetically unappealing. Electrochromic (EC) glass in this regard might be a better alternative, due to its light transmission properties that can be altered when a voltage is applied. EC glass facilitates zoning and also supports control of each zone separately. This allows the right amount of daylight at any time of the day. However, an effective control strategy is still required to efficiently control EC glass. Reinforcement learning (RL) is a promising control strategy that can learn from rewards and penalties and use this feedback to adapt to user inputs. We trained a Deep Q learning (DQN) agent on a set of weather data and visual comfort data, where the agent tries to adapt to the occupant’s feedback while observing the sun position and radiation at given intervals. The trained DQN agent can avoid bright daylight and glare scenarios in 97% of the cases and increases the amount of useful daylight up to 90%, thus significantly reducing the need for artificial lighting. Full article

(This article belongs to the Special Issue Intelligent Computing in Architecture, Engineering and Construction)

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20 pages, 8812 KiB

Open AccessArticle

Estimating Contact Force Chains Using Artificial Neural Network

by Mengmeng Wu and Jianfeng Wang

Appl. Sci. 2021, 11(14), 6278; https://doi.org/10.3390/app11146278 - 7 Jul 2021

Cited by 5 | Viewed by 2618

Abstract

The inhomogeneous distribution of contact force chains (CFC) in quasi-statically sheared granular materials dominates their bulk mechanical properties. Although previous micromechanical investigations have gained significant insights into the statistical and spatial distribution of CFC, they still lack the capacity to quantitatively estimate CFC evolution in a sheared granular system. In this paper, an artificial neural network (ANN) based on discrete element method (DEM) simulation data is developed and applied to predict the anisotropy of CFC in an assembly of spherical grains undergoing a biaxial test. Five particle-scale features including particle size, coordination number, x- and y-velocity (i.e., x and y-components of the particle velocity), and spin, which all contain predictive information about the CFC, are used to establish the ANN. The results of the model prediction show that the combined features of particle size and coordination number have a dominating influence on the CFC’s estimation. An excellent model performance manifested in a close match between the rose diagrams of the CFC from the ANN predictions and DEM simulations is obtained with a mean accuracy of about 0.85. This study has shown that machine learning is a promising tool for studying the complex mechanical behaviors of granular materials. Full article

(This article belongs to the Special Issue Intelligent Computing in Architecture, Engineering and Construction)

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15 pages, 3914 KiB

Open AccessArticle

Regional Manufacturing Industry Demand Forecasting: A Deep Learning Approach

by Zixin Dou, Yanming Sun, Yuan Zhang, Tao Wang, Chuliang Wu and Shiqi Fan

Appl. Sci. 2021, 11(13), 6199; https://doi.org/10.3390/app11136199 - 4 Jul 2021

Cited by 20 | Viewed by 3717

Abstract

With the rapid development of the manufacturing industry, demand forecasting has been important. In view of this, considering the influence of environmental complexity and diversity, this study aims to find a more accurate method to forecast manufacturing industry demand. On this basis, this paper utilizes a deep learning model for training and makes a comparative study through other models. The results show that: (1) the performance of deep learning is better than other methods; by comparing the results, the reliability of this study is verified. (2) Although the prediction based on the historical data of manufacturing demand alone is successful, the accuracy of the prediction results is significantly lower than when taking into account multiple factors. According to these results, we put forward the development strategy of the manufacturing industry in Guangdong. This will help promote the sustainable development of the manufacturing industry. Full article

(This article belongs to the Special Issue Intelligent Computing in Architecture, Engineering and Construction)

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21 pages, 1310 KiB

Open AccessFeature PaperArticle

Optimum Shape Design of Geometrically Nonlinear Submerged Arches Using the Coral Reefs Optimization with Substrate Layers Algorithm

by Jorge Pérez-Aracil, Carlos Camacho-Gómez, Alejandro Mateo Hernández-Díaz, Emiliano Pereira and Sancho Salcedo-Sanz

Appl. Sci. 2021, 11(13), 5862; https://doi.org/10.3390/app11135862 - 24 Jun 2021

Cited by 2 | Viewed by 1720

Abstract

In this paper, a novel procedure for optimal design of geometrically nonlinear submerged arches is proposed. It is based on the Coral Reefs Optimization with Substrate Layers algorithm, a multi-method ensemble evolutionary approach for solving optimization problems. A novel arch shape parameterization is combined with the Coral Reefs Optimization with Substrate Layers algorithm. This new parameterization allows considering geometrical parameters in the design process, in addition to the reduction of the bending moment carried out by the classical design approach. The importance of considering the second-order behaviour of the arch structure is shown by different numerical experiments. Moreover, it is shown that the use of Coral Reefs Optimization with Substrate Layers algorithm leads to nearly-optimal solutions, ensuring the stability of the structure, reducing the maximum absolute bending moment value, and complying with the serviceability structural restrictions. Full article

(This article belongs to the Special Issue Intelligent Computing in Architecture, Engineering and Construction)

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