Applications of Soft Computing in Civil Engineering: A Review

Chandwani, Vinay; Agrawal, Vinay; Nagar, Ravindra

doi:10.5120/14047-2210

Cited by 16 publications

(12 citation statements)

References 85 publications

(71 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Such methods initially appeared three decades ago as a new family of computational algorithms based on heuristic approaches, which do not strictly adhere to the principles of theoretical mechanics, unlike the traditional analysis procedures, [1][2][3]. Although initially treated with suspicion, they are presently employed to form surprisingly powerful computation tools, the applicability of which is constantly being extended to different fields of engineering [1][2][3]. Artificial neural networks (ANNs), genetic algorithms and fuzzy logic are SC methods widely employed [4].…”

Section: Introductionmentioning

confidence: 99%

Framework for the development of artificial neural networks for predicting the load carrying capacity of RC members

2020

View full text Add to dashboard Cite

This paper aims at establishing a framework for the development of artificial neural networks (ANNs) capable of realistically predicting the load-carrying capacity of reinforced concrete (RC) members. Multilayer back propagation neural networks are developed through the use of MATLAB and enriched databases which contain information describing the variation of load-carrying capacity in relation to key design parameters associated with the RC specimens (i.e. beams) considered. This work forms the basis for the development of a knowledge-based structural analysis tool capable of predicting RC structural response. A detailed discussion is provided on the different aspects of the proposed framework which include (1) the formation and analysis of the relevant (experimental) data, (2) the architecture of the ANNs, (3) the training/calibration process they undergo and finally, (4) ways of extending their applicability enabling them to predict the behaviour of RC structural forms with design parameters not represented in the available experimental database. Non-linear finite element analysis is used for validating the predictions of the ANN models developed. The comparative study reveals that the ANN models developed through the proposed framework are capable of effectively predicting the load-carrying capacity s of the RC structural forms considered quickly, accurately and without requiring significant computational resources. Keywords Artificial neural network • Database • Sampling method • Ultimate limit state • Reinforced concrete • Training process • Finite element analysis • Failure • Latin hypercube sampling List of symbols v Shear span b Width of the beam specimen cross-section d Effective depth of the beam specimen cross-section A s Area of longitudinal reinforcement acting in tension A sw Area of transverse reinforcement v ∕d Shear span to depth ratio f c Uniaxial compressive strength of concrete f yl Yield stress of longitudinal reinforcement bars f yw Yield stress of transverse reinforcement bars s Spacing between shear links l Ratio of tensile reinforcement (l = A s ∕b ⋅ d) w Ratio of transverse reinforcement (l = A sw ∕b ⋅ s) V u Shear strength Abbreviations CFP Compressive force path ANN Artificial neural network ULS Ultimate limit state LHS Latin hypercube sampling * Afaq Ahmad,

show abstract

Section: Introductionmentioning

confidence: 99%

Framework for the development of artificial neural networks for predicting the load carrying capacity of RC members

2020

View full text Add to dashboard Cite

show abstract

“…In the Senior Design I and II (capstone) courses, students are required to use applicable modern engineering software in their engineering projects as part of achieving ABET student learning outcome 7:an ability to acquire and apply knowledge as needed using appropriate learning strategies (Chandwani et al, 2013). Students can learn more than one modern engineering software program to complete their project, as the capstone courses must cover at least two major basic design courses.…”

Section: Discussionmentioning

confidence: 99%

Computing Competency for Civil Engineering Graduates: Recent Updates and Developments in Saudi Arabia and the US

Ahmed¹,

Nayeemuddin²,

Ayadat³

et al. 2021

IJHE

View full text Add to dashboard Cite

This paper discusses recent updates and developments of computing-based courses in the civil engineering discipline. Competency in computing is one of the most important capabilities for university graduates to obtain given the rapid development of computer technology in professional work. Civil engineering is no exception. In fact, many contemporary civil engineering projects require a high degree of computing skills, ranging from performing basic office work to programming for decision support system application in controlling flood water gates to executing construction automation via digital printing technology. However, the curriculum content for computing in civil engineering has been developmentally stagnant in the past several decades. This could be partly due to learning outcomes for civil engineering graduates, which do not explicitly mention a certain degree of achievement with respect to computing skills. Several computing-based courses offered in various civil engineering programs across Saudi Arabia and the US were examined, and their contents were compared to recent survey results administered by the American Society of Civil Engineering Technical Committee on Computing and Information Technology. The discussion is extended by examining technical courses offered in the Civil Engineering Program in Prince Mohammad Bin Fahd University with respect to computing skills. The outcomes of this study are expected to give input and suggestions for future upgrades of computing-based courses offered within the civil engineering curriculum.

show abstract

“…The scope and purpose of this article is not to provide a comprehensive overview and discussion of these different techniques. For that, we refer the readers to works by Flood and Kartam, 1994a,b;Kartam et al, 1997;Adeli, 2001;Flood, 2001;Flintsch and Chen, 2004;Chandwani et al, 2013;Ye et al, 2019); and (Falcone et al, 2020). Nonetheless, a brief discussion about the ways in which various AI techniques may (or may not) support infrastructure leadership in stable and chaotic environments appears warranted and is included below.…”

Section: Ai and Infrastructure Leadership In The Context Of Complexitymentioning

confidence: 99%

Opportunities and Challenges for Artificial Intelligence Applications in Infrastructure Management During the Anthropocene

2021

View full text Add to dashboard Cite

Pervasive and accelerating climatic, technological, social, economic, and institutional change dictate that the challenges of the future will likely be vastly different and more complex than they are today. As our infrastructure systems (and their surrounding environment) become increasingly complex and beyond the cognitive understanding of any group of individuals or institutions, artificial intelligence (AI) may offer critical cognitive insights to ensure that systems adapt, services continue to be provided, and needs continue to be met. This paper conceptually links AI to various tasks and leadership capabilities in order to critically examine potential roles that AI can play in the management and implementation of infrastructure systems under growing complexity and uncertainty. Ultimately, various AI techniques appear to be increasingly well-suited to make sense of and operate under both stable (predictable) and chaotic (unpredictable) conditions. The ability to dynamically and continuously shift between stable and chaotic conditions is critical for effectively navigating our complex world. Thus, moving forward, a key adaptation for engineers will be to place increasing emphasis on creating the structural, financial, and knowledge conditions for enabling this type of flexibility in our integrated human-AI-infrastructure systems. Ultimately, as AI systems continue to evolve and become further embedded in our infrastructure systems, we may be implicitly or explicitly releasing control to algorithms. The potential benefits of this arrangement may outweigh the drawbacks. However, it is important to have open and candid discussions about the potential implications of this shift and whether or not those implications are desirable.

show abstract

Applications of Soft Computing in Civil Engineering: A Review

Cited by 16 publications

References 85 publications

Framework for the development of artificial neural networks for predicting the load carrying capacity of RC members

Framework for the development of artificial neural networks for predicting the load carrying capacity of RC members

Computing Competency for Civil Engineering Graduates: Recent Updates and Developments in Saudi Arabia and the US

Opportunities and Challenges for Artificial Intelligence Applications in Infrastructure Management During the Anthropocene

Contact Info

Product

Resources

About