Asphalt concrete stability estimation from non-destructive test methods with artificial neural networks

Terzi, Serdal; Karaşahin, Mustafa; Gökova, Süleyman; Tahta, Mustafa; Morova, Nihat; Uzun, İsmail

doi:10.1007/s00521-012-1023-1

Cited by 6 publications

(6 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Concerning neural networks, they have been successfully used in the past for regression problems in multiple domains. These range from predicting river daily flow [14] to asphalt concrete stability [15] and include deriving electronic temperature in nuclear fusion experiments [16] and many more. ANNs can be in particular a very powerful tool for regression problems with highly nonlinear relation [15,17].…”

Section: Context Of Optimization Assumptions and Strategymentioning

confidence: 99%

Optimisation of used nuclear fuel canister loading using a neural network and genetic algorithm

Solans

Rochman

Brazell

et al. 2021

Neural Comput & Applic

View full text Add to dashboard Cite

This paper presents an approach for the optimisation of geological disposal canister loadings, combining high resolution simulations of used nuclear fuel characteristics with an articial neural network and a genetic algorithm. The used nuclear fuels (produced in an open fuel cycle without reprocessing) considered in this work come from a Swiss Pressurised Water Reactor, taking into account their realistic lifetime in the reactor core and cooling periods, up to their disposal in the final geological repository. The case of 212 representative used nuclear fuel assemblies is analysed, assuming a loading of 4 fuel assemblies per canister, and optimizing two safety parameters: the fuel decay heat (DH) and the canister effective neutron multiplication factor k$$_{\mathrm{eff}}$$ eff . In the present approach, a neural network is trained as a surrogate model to evaluate the k$$_{\mathrm{eff}}$$ eff value to substitute the time-consuming-code Monte Carlo transport & depletion SERPENT for specific canister loading calculations. A genetic algorithm is then developed to optimise simultaneously the canister k$$_{\mathrm{eff}}$$ eff and DH values. The k$$_{\mathrm{eff}}$$ eff computed during the optimisation algorithm is using the previously developed artificial neural network. The optimisation algorithm allows (1) to minimize the number of canisters, given assumed limits for both DH and k$$_{\mathrm{eff}}$$ eff quantities and (2) to minimize DH and k$$_{\mathrm{eff}}$$ eff differences among canisters. This study represents a proof-of-principle of the neural network and genetic algorithm capabilities, and will be applied in the future to a larger number of cases.

show abstract

Section: Context Of Optimization Assumptions and Strategymentioning

confidence: 99%

Optimisation of used nuclear fuel canister loading using a neural network and genetic algorithm

Solans

Rochman

Brazell

et al. 2021

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…Pulse wave modulus testing is considered as a nondestructive testing method for field application to evaluate moisture damage-induced stiffness losses. Terzi et al, (2013) used the field and laboratory studies data to estimate the value of Marshall stability. Up to this aim, the pulse, Marshall properties, deflection, modulus of elasticity, density, and air void data were implemented as an input variable.…”

Section: Introductionmentioning

confidence: 99%

Suitability of Nondestructive Testing of Asphalt Concrete for Detecting the Impact of Moisture Damage

Sarsam¹

2021

bioex

View full text Add to dashboard Cite

Asphalt pavement susceptibility to moisture damage is considered as a major issue in the durability and service life of the roadway. Quick and nondestructive testing of asphalt concrete pavement are the major concern for predicting its suitability for evaluation. In the present investigation, nondestructive test has been implemented to detect the moisture damage issue of asphalt concrete mixture. Asphalt concrete specimens were prepared using Marshall method. Aggregates gradation of wearing, binder and base course was implemented for the preparation of the specimens. Specimens were tested for ultrasonic pulse velocity before and after practicing the moisture damage procedure. The variations of seismic modulus among various gradation before and after the moisture damage were considered as a criterion for moisture damage and related to tensile strength ratio TSR. It was observed that the pulse velocity decline by a range of (11 to 16) for asphalt concrete after moisture damage. It was concluded that the Seismic modulus as calculated from the ultrasonic pulse velocity test was effective in distinguishing the impact of moisture damage. The seismic modulus at optimum asphalt content decline by (34.7, 46.7, and 52.6) % after moisture damage for wearing, binder, and base course mixtures respectively. The ultrasonic pulse velocity test is recommended for assessing the susceptibility of asphalt concrete mixture to moisture damage.

show abstract

“…Concerns in terms of pavements design and tests are described in [21] as an action that involves several mandatory steps to assure the best anticipated performance in service. As main steps, authors mention selecting the aggregates, asphalt and additives to be used, testing the asphalt mixtures with varying proportions of the ingredients as close to the field condition as possible, and selecting the optimum mix design.…”

Section: Introductionmentioning

confidence: 99%

“…The test results influence the material selection for the project. There are several ways to determine the current status of asphalt concretes intended to be used in construction projects, such as core drilling (as destructive method) or Marshall stability test [21] (as non-destructive method). Authors mentioned the Marshall stability test which is related to the tensile strength of the asphalt mixture [21] and [5] and recommended non-destructive testing methods for determining the structural condition of an in-service pavement.…”

Section: Introductionmentioning

confidence: 99%

“…There are several ways to determine the current status of asphalt concretes intended to be used in construction projects, such as core drilling (as destructive method) or Marshall stability test [21] (as non-destructive method). Authors mentioned the Marshall stability test which is related to the tensile strength of the asphalt mixture [21] and [5] and recommended non-destructive testing methods for determining the structural condition of an in-service pavement. Other testing proposed method for determining the material parameters of a fatigue cracking model based on Accelerated Pavement Testing were identified by [5] and [22].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Workflow Automation in a Risk Management Framework for Pavement Maintenance Projects

Benţa

Rusu

Manolescu

2017

INT J COMPUT COMMUN

View full text Add to dashboard Cite

This paper presents a Workflow Management System (WfMS) for procurement process automation in road pavement maintenance and management. It fits information infrastructure for monitoring and maintenance of pavements and roads. Through the two roles of administrator and major users (builder and subcontractors), the solution models the entire process. This way, risks of exceeding allocated budget, time consuming tasks, overcoming deadline, and time consuming quality control, as main issues in risk management, are reduced and controlled.

show abstract

Asphalt concrete stability estimation from non-destructive test methods with artificial neural networks

Cited by 6 publications

References 16 publications

Optimisation of used nuclear fuel canister loading using a neural network and genetic algorithm

Optimisation of used nuclear fuel canister loading using a neural network and genetic algorithm

Suitability of Nondestructive Testing of Asphalt Concrete for Detecting the Impact of Moisture Damage

Workflow Automation in a Risk Management Framework for Pavement Maintenance Projects

Contact Info

Product

Resources

About