Artificial Neural Networks in Construction Engineering and Management

Waziri, Baba Shehu; Bala, Kabir; Bustani, Shehu Ahmadu

doi:10.7492/ijaec.2017.006

Cited by 30 publications

(16 citation statements)

References 52 publications

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“…In connection with the fast development of information and communication technologies (ICT) since 1990s, recent research and development in applied AI solutions for enhanced practice in construction management has shown insightful perspectives and effective progressions. For example: • The use of artificial neural network (ANN) (Waziri et al, 2017) across various predication related issues in construction management, • The use of knowledge sustained analytic network process (ANP) (Chen, 2007) to evaluate options for informed decision making in construction management, and…”

Section: Intelligent Construction Management Systemmentioning

confidence: 99%

Grand Challenges in Construction Management

Chen

2019

Front. Built Environ.

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This article is written for the launch of the specialty section on construction management at the Frontiers in Built Environment. This new specialty section opens an on-line platform for academics and practitioners in the subject field to share valuable experiences and findings from innovative research and development as well as practices, which focus on the management of people/workforce, product/production, and processes across built environment project stages, including development and construction, operation and maintenance, as well as demolition and redevelopment, under considerations on the dependability and productivity of construction management services. In this article, the writer presents his humble opinions limited from experiences from academic research and professional services around the world in the past three decades. The topics derived on grand challenges in construction management are gigantic in terms of their coverages, but may have huge impacts on professional development in both short and longer term. Discussions presented here on grand challenges in construction management consist of four parts to respectively focus on an initial cognitive framework of the construction management body of knowledge (CMBOK), intelligence pervasive construction management systems, interdisciplinary digital innovation, and solutions for performance enhancement in megaproject delivery. The research methodology that support the writer to describe the four grand challenges includes literature review, comparison study, site observation, and system architecture analysis and development. It was the writer's initial abductive reasoning to identify the four grand challenges in construction management through an extensive literature review, which was based on national and regional strategies for the construction industry from the world top economies. Some key references relating to these four major topics are collected to support discussions in this article. It is expected that this article could contribute to the debate as well as research and development in construction management in terms of continuous technical enhancements on project-oriented performance and value regardless the scope, the location, and the time associated and concerned. As the study was conducted through abductive reasoning, this article should have its limitation on the coverage to all grand challenges in construction management, and this should leave many unanswered questions for further exploratory research.

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Section: Intelligent Construction Management Systemmentioning

confidence: 99%

Grand Challenges in Construction Management

Chen

2019

Front. Built Environ.

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“…Despite the numerous advantages of the ANN compared to the rule based and statistics NLP, it offers few explanations on the relations found between the data and the connection among neurons with the output data. The phenomenon is called blackbox effect which makes difficult to explain what is learned from the net (Paliwal and Kumar 2011;Waziri et al 2017).…”

Section: Natural Language Processing (Nlp): Rule Based Statistical Amentioning

confidence: 99%

“…(Pradhan et al 2004;Sivanandam and Deepa 2006;Rumelhart 1986) Structure similar to the human brain, ANNs are very effective for solving a complex problem ANNs have the ability to overcome the lack of data, they "Fill in the blank". This makes it particularly suitable for use in the NLP (Waziri et al 2017) Fig. 1 Explainability and performance qualitative graph of AI learning method (Gunning 2017)…”

Section: Natural Language Processing (Nlp): Rule Based Statistical Amentioning

confidence: 99%

Natural Language Processing for Information and Project Management

Giuda

Locatelli

Schievano

et al. 2019

Digital Transformation of the Design, Construction and Management Processes of the Built Environment

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The purpose of the paper is to investigate the state of the art of textual translation theories, methods, and tools into formal and numerical requirements to support information modelling and project management process. Natural Language Processing helps translate text requirements into numerical terms that are necessary for the application and success of information modelling and management in a datadriven process.

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“…In this phase, the quantity of the input and the output data in be defined [14][15][16]. The input parameters define the special parameters of the network in terms of dimension and the quantity of output parameters that determines the quantity of the solution surfaces created in the network [17][18][19]. The quantity of the input was considered as defined in the database for the analysis of the individual data.…”

Section: Modelling Of the Ann Frameworkmentioning

confidence: 99%

Guess the time of implementation of residential construction projects using neural networks ANN

AL-Zubaidi

Yas

Abbas³

2019

PEN

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The construction duration of residential projects, especially in building processes, significantly impact the business of a construction company. The balance between the planned cost, direct cost, and overheads directly depend on the precision of the implementation phase of the project. The application of the artificial neural network (ANN) to predict the duration of implementation of a residential construction project from the pre-design stage to completion is comprehensively discussed in this research. The study applies the backpropagation (BP) network made of nodes for error evaluation of the training states. Further, the proposed system illustrated that the artificial neural network (ANN) satisfy the three crucial criteria (cost, quality, and time) used for the evaluation of projects. The ANN provided accurate data for the training and estimation of, the duration of a residential construction project with adequate resources of implementation. The performance of the results for the ANN at 105 iteration shows that the prediction was 99.841% accurate for the overall system. The best fit occurred at the 99th epoch with an MSE of 0.10286.

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Artificial Neural Networks in Construction Engineering and Management

Cited by 30 publications

References 52 publications

Grand Challenges in Construction Management

Grand Challenges in Construction Management

Natural Language Processing for Information and Project Management

Guess the time of implementation of residential construction projects using neural networks ANN

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