Analytical-numerical procedure incorporating cracking in RC beams

2014 ◽  
Vol 31 (5) ◽  
pp. 986-1010 ◽  
Author(s):  
K.A. Patel ◽  
Sandeep Chaudhary ◽  
A.K. Nagpal
Keyword(s):  
Input Data ◽  
Beam Element ◽  
Computational Effort ◽  
Span Length ◽  
Rc Beams ◽  
Element Level ◽  
Concrete Cracking ◽  
Content Type ◽  
Service Load ◽  
Everyday Design

Purpose – The purpose of this paper is to develop, for use in everyday design, a procedure that incorporates the effect of concrete cracking in reinforced concrete (RC) beams at service load and requires computational efforts which is a fraction of that required for the available methods. Further for ease of use in everyday design the reinforcement input data is minimized. The procedure has been demonstrated for continuous beams and is under development for tall building frames. Design/methodology/approach – The procedure is analytical at the element level and numerical at the structural level. A cracked span length beam element consisting of three cracked zones and two uncracked zones has been used. Closed form expressions for flexibility coefficients, end displacements, crack lengths, and mid-span deflection of the cracked span length beam element have been presented. In order to keep the procedure analytical at the element level, average tension stiffening characteristics are arrived at for cracked zones. Findings – The proposed procedure, at minimal computation effort and minimal reinforcement input data, yields results that are close to experimental and finite element method results. Practical implications – The procedure can be used in everyday design since it requires minimal computational effort and minimal reinforcement input data. Originality/value – A procedure that requires minimal computational effort and minimal reinforcement input data for incorporating concrete cracking effects in RC structures at service load has been developed for use in everyday design.

An element incorporating cracking for reinforced concrete skeletal structures at service load

2016 ◽  
Vol 20 (9) ◽  
pp. 1257-1276 ◽  
Author(s):  
KA Patel ◽  
Sandeep Chaudhary ◽  
AK Nagpal
Keyword(s):  
Reinforced Concrete ◽  
Numerical Procedure ◽  
Computational Time ◽  
Single Element ◽  
Structural Level ◽  
Element Level ◽  
Skeletal Structures ◽  
Service Load ◽  
Average Tension ◽  
Everyday Design

An element has been proposed to take into account cracking in the reinforced concrete skeletal structures subjected to a service load. A typical skeletal member is modeled as a single element and is visualized to consist of at most five zones (cracked or uncracked). Closed-form expressions for the flexibility and stiffness coefficients and end displacements have been obtained. Furthermore, for use in everyday design, a hybrid analytical–numerical procedure has been developed using the proposed element. The procedure is analytical at the element level and numerical at the structural level. To keep the procedure analytical at the element level, the average tension stiffening characteristics are arrived at for the cracked zones. The developed procedure has been validated in limiting cases by comparison with the experimental results reported elsewhere and by comparison with the finite element method results. The proposed element would lead to a drastic reduction in computational time for large reinforced concrete structures, for example, tall reinforced concrete building frames.

Cracked span length beam element for service load analysis of steel concrete composite bridges

2015 ◽  
Vol 157 ◽  
pp. 201-208 ◽  
Author(s):  
M.P. Ramnavas ◽  
K.A. Patel ◽  
Sandeep Chaudhary ◽  
A.K. Nagpal
Keyword(s):  
Beam Element ◽  
Span Length ◽  
Service Load ◽  
Composite Bridges ◽  
Load Analysis

scholarly journals A Practical Finite Element Modeling Strategy to Capture Cracking and Crushing Behavior of Reinforced Concrete Structures

Materials ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 506 ◽  
Author(s):  
Alexandre Mathern ◽  
Jincheng Yang
Keyword(s):  
Finite Element ◽  
Reinforced Concrete ◽  
Constitutive Relations ◽  
Fe Analysis ◽  
Rc Beams ◽  
Cracking Behavior ◽  
Concrete Cracking ◽  
Rc Structures ◽  
Nonlinear Fe Analysis ◽  
Modeling Strategy

Nonlinear finite element (FE) analysis of reinforced concrete (RC) structures is characterized by numerous modeling options and input parameters. To accurately model the nonlinear RC behavior involving concrete cracking in tension and crushing in compression, practitioners make different choices regarding the critical modeling issues, e.g., defining the concrete constitutive relations, assigning the bond between the concrete and the steel reinforcement, and solving problems related to convergence difficulties and mesh sensitivities. Thus, it is imperative to review the common modeling choices critically and develop a robust modeling strategy with consistency, reliability, and comparability. This paper proposes a modeling strategy and practical recommendations for the nonlinear FE analysis of RC structures based on parametric studies of critical modeling choices. The proposed modeling strategy aims at providing reliable predictions of flexural responses of RC members with a focus on concrete cracking behavior and crushing failure, which serve as the foundation for more complex modeling cases, e.g., RC beams bonded with fiber reinforced polymer (FRP) laminates. Additionally, herein, the implementation procedure for the proposed modeling strategy is comprehensively described with a focus on the critical modeling issues for RC structures. The proposed strategy is demonstrated through FE analyses of RC beams tested in four-point bending—one RC beam as reference and one beam externally bonded with a carbon-FRP (CFRP) laminate in its soffit. The simulated results agree well with experimental measurements regarding load-deformation relationship, cracking, flexural failure due to concrete crushing, and CFRP debonding initiated by intermediate cracks. The modeling strategy and recommendations presented herein are applicable to the nonlinear FE analysis of RC structures in general.

A cost evaluation model for IoT-enabled prefabricated construction supply chain management

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wennan Zhang ◽  
Kai Kang ◽  
Ray Y. Zhong
Keyword(s):  
Hong Kong ◽  
Supply Chain ◽  
Input Data ◽  
Evaluation Model ◽  
Construction Cost ◽  
Influential Factors ◽  
System Boundary ◽  
Supply Chain System ◽  
Content Type ◽  
The Relationship

PurposeThis paper proposes an evaluation model for prefabricated construction to guide a supply chain with controllable costs. Prefabricated construction is prevalent due to area limitations. Nevertheless, the development is limited by budget control and identifying the factors affecting cost. The degree of close collaboration in the supply chain is closely interconnected with cost performance that includes direct and indirect factors. This paper not only quantizes these factors but also distinguishes the degree of influence of various factors.Design/methodology/approachSystem dynamics is applied to simulate and analyze the construction cost factors through Vensim software. It can also clarify the relationship between cost and other influencing factors. The input data are collected from an Internet of Things (IoT)-enabled system under a Building Information Modeling (BIM) system and Hong Kong government reports.FindingsSimulation results indicate that prefabricated construction cost is mainly influenced by government promotion degree (GPD), working pressure from on-site construction (WPOSC), prefab quality (PQ), load-bearing capacity per vehicle (LBPV) and mold quality (MQ). However, it is more sensitive toward GPD, which indicates that the government should take measures to promote this construction technology. On-site worker management is also essential for the assembly process and indirectly influences the construction cost.Research limitations/implicationsThis paper quantifies indirect influential factors to clarify the specific features for prefabricated construction. The investigated factors are limited.Practical implicationsThe contractor can identify all factors and classify the levels of influence to make decisions under the supply chain system boundary.Social implicationsThe input data are collected from an IoT-enabled system under a BIM system and Hong Kong government reports. Thus, the relationship between construction cost influential factors can be investigated.Originality/valueThis paper quantifies indirect influencing factors and clarifies the specific features in prefabricated construction. The contractor could identify these factors to make decisions and classify the levels of influence under the supply chain system boundary.

Comparison of flexural capacity of different hollow slab beams with/without pavement layer reinforced by steel plates

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Long Liu ◽  
Lifeng Wang ◽  
Ziwang Xiao
Keyword(s):  
Finite Element ◽  
Bearing Capacity ◽  
Steel Plate ◽  
Plate Thickness ◽  
Experimental Results ◽  
Nonlinear Material ◽  
Finite Element Models ◽  
Rc Beams ◽  
Flexural Capacity ◽  
Content Type

PurposeReinforcement of reinforced concrete (RC) beams in-service have always been an important research field, anchoring steel plate in the bottom of the beams is a kind of common reinforcement methods. In actual engineering, the contribution of pavement layer to the bearing capacity of RC beams is often ignored, which underestimates the bearing capacity and stiffness of RC beams to a certain extent. The purpose of this paper is to study the effect of pavement layer on the RC beams before and after reinforcement.Design/methodology/approachFirst, static load experiments are carried out on three in-service RC hollow slab beams, meanwhile, nonlinear finite element models are built to study the bearing capacity of them. The nonlinear material and shear slip effect of studs are considered in the models. Second, the finite element models are verified, and the numerical simulation results are in good agreement with the experimental results. Last, the finite element models are adopted to carry out the research on the influence of different steel plate thicknesses on the flexural bearing capacity and ductility.FindingsThe experimental results showed that pavement layers increase the flexural capacity of hollow slab beams by 16.7%, and contribute to increasing stiffness. Ductility ratio of SPRCB3 and PRCB2 was 30% and 24% lower than that of RCB1, respectively. The results showed that when the steel plate thickness was 1 mm–6 mm, the bearing capacity of the hollow slab beam increased gradually from 2158.0 kN.m to 2656.6 kN.m. As the steel plate thickness continuously increased to 8 mm, the ultimate bearing capacity increased to 2681.0 kN.m. The increased thickness did not cause difference to the bearing capacity, because of concrete crushing at the upper edge.Originality/valueIn this paper, based on the experimental study, the bearing capacity of hollow beam strengthened by steel plate with different thickness is extrapolated by finite element simulation, and its influence on ductility is discussed. This method not only guarantees the accuracy of the bearing capacity evaluation, but also does not require a large number of samples, and has certain economy. The research results provide a basis for the reinforcement design of similar bridges.

Pitting corrosion prediction from cathodic data: application of machine learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohamed Nadir Boucherit ◽  
Fahd Arbaoui
Keyword(s):  
Machine Learning ◽  
Physicochemical Properties ◽  
Input Data ◽  
Surface Condition ◽  
Recurrent Network ◽  
Localized Corrosion ◽  
Potential Region ◽  
Pitting Potential ◽  
Content Type ◽  
Nuclear Installation

Purpose To constitute input data, the authors carried out electrochemical experiments. The authors performed voltammetric scans in a very cathodic potential region. The authors constituted an experimental table where for each experiment we note the current values recorded at a low polarization range and the pitting potential observed in the anodic region. This study aims to concern carbon steel used in a nuclear installation. The properties of the chemical solutions are close to that of the cooling fluid used in the circuit. Design/methodology/approach In a previous study, this paper demonstrated the effectiveness of machine learning in predicting the localized corrosion resistance of a material by considering as input data the physicochemical properties of its environment (Boucherit et al., 2019). With the present study, the authors improve the results by considering as input data, cathodic currents. The reason of such an approach is to have input data that integrate both the surface state of the material and the physicochemical properties of its environment. Findings The experimental table was submitted to two neural networks, namely, a recurrent network and a convolution network. The convolution network gives better pitting potential predictions. Results also prove that the prediction by observing cathodic currents is better than that obtained by considering the physicochemical properties of the solution. Originality/value The originality of the study lies in the use of cathodic currents as input data. These data contain implicit information on both the chemical environment of the material and its surface condition. This approach appears to be more efficient than considering the chemical composition of the solution as input data. The objective of this study remains, at the same time, to seek the optimal neuronal architectures and the best input data.

Motion in frequency domain for harmonic balance simulation of electrical machines

2018 ◽  
Vol 37 (4) ◽  
pp. 1449-1460
Author(s):  
Markus Wick ◽  
Sebastian Grabmaier ◽  
Matthias Juettner ◽  
Wolfgang Rucker
Keyword(s):  
Steady State ◽  
Frequency Domain ◽  
Efficient Method ◽  
Harmonic Balance ◽  
Eddy Currents ◽  
Computational Effort ◽  
Electrical Machines ◽  
Accurate Approximation ◽  
Content Type ◽  
Magnetic Devices

Purpose The high computational effort of steady-state simulations limits the optimization of electrical machines. Stationary solvers calculate a fast but less accurate approximation without eddy-currents and hysteresis losses. The harmonic balance approach is known for efficient and accurate simulations of magnetic devices in the frequency domain. But it lacks an efficient method for the motion of the geometry. Design/methodology/approach The high computational effort of steady-state simulations limits the optimization of electrical machines. Stationary solvers calculate a fast but less accurate approximation without eddy-currents and hysteresis losses. The harmonic balance approach is known for efficient and accurate simulations of magnetic devices in the frequency domain. But it lacks an efficient method for the motion of the geometry. Findings The three-phase symmetry reduces the simulated geometry to the sixth part of one pole. The motion transforms to a frequency offset in the angular Fourier series decomposition. The calculation overhead of the Fourier integrals is negligible. The air impedance approximation increases the accuracy and yields a convergence speed of three iterations per decade. Research limitations/implications Only linear materials and two-dimensional geometries are shown for clearness. Researchers are encouraged to adopt recent harmonic balance findings and to evaluate the performance and accuracy of both formulations for larger applications. Practical implications This method offers fast-frequency domain simulations in the optimization process of rotating machines and so an efficient way to treat time-dependent effects such as eddy-currents or voltage-driven coils. Originality/value This paper proposes a new, efficient and accurate method to simulate a rotating machine in the frequency domain.

scholarly journals An Artificial Intelligence–Assisted Design Method for Topology Optimization without Pre-Optimized Training Data

2021 ◽  
Vol 11 (19) ◽  
pp. 9041
Author(s):  
Alex Halle ◽  
Lucio Flavio Campanile ◽  
Alexander Hasse
Keyword(s):  
Topology Optimization ◽  
Input Data ◽  
Design Method ◽  
Design Procedure ◽  
Computational Effort ◽  
Training Data ◽  
Geometry Design ◽  
Random Input Data ◽  
Degree Of Filling ◽  
Initial Process

Engineers widely use topology optimization during the initial process of product development to obtain a first possible geometry design. The state-of-the-art method is iterative calculation, which requires both time and computational power. This paper proposes an AI-assisted design method for topology optimization, which does not require any optimized data. An artificial neural network—the predictor—provides the designs on the basis of boundary conditions and degree of filling as input data. In the training phase, the so-called evaluators evaluate the generated geometries on the basis of random input data with respect to given criteria. The results of those evaluations flow into an objective function, which is minimized by adapting the predictor’s parameters. After training, the presented AI-assisted design procedure generates geometries that are similar to those of conventional topology optimizers, but require only a fraction of the computational effort. We believe that our work could be a clue for AI-based methods that require data that are difficult to compute or unavailable.

scholarly journals INFLUENCE OF SPATIAL AND TEMPORAL RESOLUTION ON TIME SERIES-BASED COASTAL SURFACE CHANGE ANALYSIS USING HOURLY TERRESTRIAL LASER SCANS

2021 ◽  
Vol V-2-2021 ◽  
pp. 137-144
Author(s):  
K. Anders ◽  
L. Winiwarter ◽  
H. Mara ◽  
R. C. Lindenbergh ◽  
S. E. Vos ◽  
...  
Keyword(s):  
Time Series ◽  
Temporal Resolution ◽  
Input Data ◽  
Laser Scanning ◽  
Poor Performance ◽  
Computational Effort ◽  
Spatial Extent ◽  
Change Analysis ◽  
Surface Change ◽  
Natural Surface

Abstract. Near-continuously acquired terrestrial laser scanning (TLS) data contains valuable information on natural surface dynamics. An important step in geographic analyses is to detect different types of changes that can be observed in a scene. For this, spatiotemporal segmentation is a time series-based method of surface change analysis that removes the need to select analysis periods, providing so-called 4D objects-by-change (4D-OBCs). This involves higher computational effort than pairwise change detection, and efforts scale with (i) the temporal density of input data and (ii) the (variable) spatial extent of delineated changes. These two factors determine the cost and number of Dynamic Time Warping distance calculations to be performed for deriving the metric of time series similarity. We investigate how a reduction of the spatial and temporal resolution of input data influences the delineation of twelve erosion and accumulation forms, using an hourly five-month TLS time series of a sandy beach. We compare the spatial extent of 4D-OBCs obtained at reduced spatial (1.0 m to 15.0 m with 0.5 m steps) and temporal (2 h to 96 h with 2 h steps) resolution to the result from highest-resolution data. Many change delineations achieve acceptable performance with ranges of ±10 % to ±100 % in delineated object area, depending on the spatial extent of the respective change form. We suggest a locally adaptive approach to identify poor performance at certain resolution levels for the integration in a hierarchical approach. Consequently, the spatial delineation could be performed at high accuracy for specific target changes in a second iteration. This will allow more efficient 3D change analysis towards near-realtime, online TLS-based observation of natural surface changes.

Exploring incomplete information in maintenance materials inventory optimization

2014 ◽  
Vol 114 (1) ◽  
pp. 144-158 ◽  
Author(s):  
Antti Puurunen ◽  
Jukka Majava ◽  
Pekka Kess
Keyword(s):  
Sensitivity Analysis ◽  
Simulation Model ◽  
Input Data ◽  
Real Life ◽  
Significant Risk ◽  
Service Level ◽  
Content Type ◽  
Imperfect Data ◽  
Inventory Optimization ◽  
Critical Materials

Purpose – Ensuring the sufficient service level is essential for critical materials in industrial maintenance. This study aims to evaluate the use of statistically imperfect data in a stochastic simulation-based inventory optimization where items' failure characteristics are derived from historical consumption data, which represents a real-life situation in the implementation of such an optimization model. Design/methodology/approach – The risks of undesired shortages were evaluated through a service-level sensitivity analysis. The service levels were simulated within the error of margin of the key input variables by using StockOptim optimization software and real data from a Finnish steel mill. A random sample of 100 inventory items was selected. Findings – Service-level sensitivity is item specific, but, for many items, statistical imprecision in the input data causes significant uncertainty in the service level. On the other hand, some items seem to be more resistant to variations in the input data than others. Research limitations/implications – The case approach, with one simulation model, limits the generalization of the results. The possibility that the simulation model is not totally realistic exists, due to the model's normality assumptions. Practical implications – Margin of error in input data estimation causes a significant risk of not achieving the required service level. It is proposed that managers work to improve the preciseness of the data, while the sensitivity analysis against statistical uncertainty, and a correction mechanism if necessary, should be integrated into optimization models. Originality/value – The output limitations in the optimization, i.e. service level, are typically stated precisely, but the capabilities of the input data have not been addressed adequately. This study provides valuable insights into ensuring the availability of critical materials.

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