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This letter proposes an effective method to improve object location in Chamfer template matching (CTM) based object detection using image segmentation. In our method, object bounding boxes are iteratively adjusted to fit with the object images obtained from image segmentation in a probabilistic model. The proposed method was tested with state-of-the-art CTM-based object detectors. Experimental results have shown the proposed method improved the location accuracy of the object detectors and reduce the false alarms rate.

This paper presents an experimental study on the stress-strain relation of confined concrete that considers the corrosion effects of transverse reinforcement. The main variables are the corrosion level of transverse reinforcement, cross sectional shape of confined concrete, as well as arrangement and configuration of confining transverse reinforcing bars. The test results revealed that four key parameters of the complete stress-strain relation are significantly affected due to the corrosion of transverse reinforcement, including the maximum concrete strength and corresponding axial concrete strain, maximum concrete strain at the fracture of the first hoops, and descending branch of the stress-strain curve after exceeding the maximum strength. Based on the test data and regression analysis, the empirical equations to estimate these key parameters are proposed, and a complete stress-strain model for confined concrete with corroded transverse reinforcement is developed. The proposed model showed good correlation with the test data of both circular and square specimens with various corrosion levels and subjected to compression axial loading.

International audience; An effective model, which jointly captures shape and motion cues, for dynamic texture (DT) description is introduced by taking into account advantages of volumes of blurred-invariant features in three main following stages. First, a 3-dimensional Gaussian kernel is used to form smoothed sequences that allow to deal with well-known limitations of local encoding such as near uniform regions and sensitivity to noise. Second , a receptive volume of the Difference of Gaussians (DoG) is figured out to mitigate the negative impacts of environmental and illumination changes which are major challenges in DT understanding. Finally, a local encoding operator is addressed to construct a discriminative descriptor of enhancing patterns extracted from the filtered volumes. Evaluations on benchmark datasets (i.e., UCLA, DynTex, and DynTex++) for issue of DT classification have positively validated our crucial contributions.

Convolutional neural networks (CNNs) have become the power method for many computer vision applications, including image classification and action recognition. However, they are almost computationally and memory intensive, thus are challenging to use and to deploy on systems with limited resources, except for a few recent networks which were specifically designed for mobile and embedded vision applications such as MobileNet, NASNet-Mobile. In this paper, we present a novel efficient algorithm to compress CNN models to decrease the computational cost and the run-time memory footprint. We propose a strategy to measure the redundancy of parameters based on their relationship using the covariance and correlation criteria, and then prune the less important ones. Our method directly applies to CNNs, both on convolutional and fully connected layers, and requires no specialized software/hardware accelerators. The proposed method significantly reduces the model sizes (up to 70%) and thus computing costs without performance loss on different CNN models (AlexNet, ResNet, and LeNet) for image classification on different datasets (MNIST, CIFAR10, and ImageNet) as well as for human action recognition (on dataset like the UCF101).

Steel reinforcement corrosion has been recognized as a major deterioration issue for the performance and safety of reinforced concrete (RC) structures. In this paper, the behavior of corroded RC columns under the seismic loading was studied using a three-dimensional (3D) non-linear Finite Element (FE) analysis, considering the material properties deterioration of reinforcement and concrete induced by corrosion. The experimental results of nine reinforced concrete (RC) columns in three experimental studies in literature were selected to verify the accuracy of the proposed 3D non-linear FE model. Thereafter, an extensive parametric investigation, including the FE models of 240 RC columns subjected to the simulated seismic loading was performed to study the influence of various crucial parameters on the seismic performance of corroded RC columns, particularly their lateral load resistance and ultimate drift capacity deterioration. Finally, these key parameters were incorporated into two prediction equations of the lateral load resistance and ultimate drift capacity for corroded RC columns.

This paper presents an experimental study carried out on eight full-scale uncorroded and corroded reinforced concrete columns that were subjected to simulated seismic loading to investigate the effects of reinforcement corrosion on the seismic behavior of columns that failed in flexure. The main variables are the corrosion levels of reinforcement and applied axial force ratios. The seismic performance of test specimens was assessed in terms of crack pattern, hysteretic response, yield displacement, lateral load resistance, drift capacity, displacement ductility, and energy dissipation capacity. The results highlighted that the corroded columns suffered a significant degradation of strength and lateral drift capacity as compared to uncorroded columns. In addition, it is noteworthy to observe that the failure mode of corroded columns switched from flexural failure to flexural-shear failure and sudden axial failure when they are highly corroded and subjected to high axial force. An empirical equation based on the maximum pit depth of corroded longitudinal reinforcing bars was proposed to predict the residual flexural strength of corroded columns. Furthermore, based on the shear strength model proposed in ASCE/SEI 41-13, methods were proposed to estimate the shear strength of corroded columns failed in flexural-shear failure and axial failure, which consider the contribution reductions to shear strength of transverse reinforcement and concrete due to corrosion. Among these methods, the shear strength of corroded columns estimated based on the corrosion mass loss and average residual cross-sectional area was not conservative as compared to the test results. In contrast, the shear strength of corroded columns predicted using the minimum residual cross-sectional area of transverse reinforcing bars showed good and reliable correlation with the experimental results. Published version

This paper presents an experimental study carried out on eight full-scale uncorroded and corroded reinforced concrete short columns subjected to the simulated seismic loading to investigate...

Steel reinforcement corrosion has been recognized as a major issue for the performance and safety of reinforced concrete (RC) structures. This paper presents an experimental study carried out on six full-scale un-corroded and corroded RC short columns subjected to simulated seismic loadings to investigate the effects of reinforcement corrosion on the seismic behavior of short columns failed in shear. The main variables are the corrosion level of reinforcement and applied axial force ratio. The seismic behavior of test specimens was evaluated in terms of the crack pattern, hysteretic response, initial stiffness, shear strength, drift capacity, and energy dissipation capacity. The experimental results highlighted that the corroded RC columns displayed a significant degradation of shear strength and deformation capacity compared to un-corroded RC columns, especially when they are highly corroded and subjected to high axial load ratio.

International audience; This paper proposes a new local descriptor for action recognition in depth images using second-order directional Local Derivative Patterns (LDPs). LDP relies on local deriva- tive direction variations to capture local patterns contained in an image region. Our proposed local descriptor combines different directional LDPs computed from three depth maps obtained by representing depth sequences in three orthogonal views and is able to jointly encode the shape and motion cues. Moreover, we suggest the use of Sparse Coding-based Fisher Vector (SCFVC) for encoding local descriptors into a global representation of depth sequences. SCFVC has been proven effective for object recognition but has not gained much attention for action recognition. We perform action recognition using Extreme Learn- ing Machine (ELM). Experimental results on three public benchmark datasets show the effectiveness of the proposed approach.

Reinforced concrete (RC) structural walls with coupling beams are widely used as the primary lateral-load-bearing elements in high-rise building structures. Many researches have shown that there is uncertainty in the estimation of the effective stiffness of RC coupling beams. In an attempt to develop rational approaches regarding the stiffness of these structural components, this paper presents the analytical approaches, considering the influence of flexural and shear deformations, to determine the effective stiffness of RC coupling beams. A comprehensive parametric study, including 144 combinations for the conventionally reinforced concrete coupling beam (CCB) and 48 combinations for the diagonally reinforced concrete coupling beam (DCB), is carried out and two equations to estimate the effective stiffness of RC coupling beams are proposed each as a function of aspect ratio, transverse reinforcement ratio, longitudinal reinforcement ratio, diagonal reinforcement ratio and concrete compressive strength, on the basis of these parametric case studies. The proposed analytical approaches and the equations for assessing the effective stiffness of CCBs and DCBs are then verified by comparison with experimental results obtained from literature. (C) 2014 Elsevier Ltd. All rights reserved.

Within the domain of texture classification, a lot of effort has been spent on local descriptors, leading to many powerful algorithms. However, preprocessing techniques have received much less attention despite their important potential for improving the overall classification performance. We address this question by proposing a novel, simple, yet very powerful biologically-inspired filtering (BF) which simulates the performance of human retina. In the proposed approach, given a texture image, after applying a difference of Gaussian (DoG) filter to detect the edges, we first split the filtered image into two maps alongside the sides of its edges. The feature extraction step is then carried out on the two maps instead of the input image. Our algorithm has several advantages such as simplicity, robustness to illumination and noise, and discriminative power. Experimental results on three large texture databases show that with an extremely low computational cost, the proposed method improves significantly the performance of many texture classification systems, notably in noisy environments. The source codes of the proposed algorithm can be downloaded from https://sites.google.com/site/nsonvu/code .

International audience; In this paper, we propose a micro-macro feature combination approach for texture classification. The two disparate yet complementary categories of features are combined. By this way, Local Binary Pattern (LBP) plays the role of micro-structure feature extractor while the scattering transform captures macro-structure information. In fact, for extracting the macro-type features, coefficients are aggregated from three different layers of the scattering network. It is a handcrafted convolution network which is implemented by computing consecutively wavelet transforms and modulus non-linear operators. By contrast, in order to extract micro-structure features which are rotation-invariant, relatively robust to noise and illumination change, the completed LBP is utilized alongside the biologically-inspired filtering (BF) preprocessing technique. Overall, since the proposed framework can exploit the advantages of both feature types, its texture representation is not only invariant to rotation, scaling, illumination change but also highly discriminative. Intensive experiments conducted on many texture benchmarks such as CUReT, UIUC, KTH-TIPS-2b, and OUTEX show that our framework has a competitive classification accuracy.

International audience; Regarding texture features, Local-based methods such as Local Binary Pattern (LBP) and its variants are computationally efficient high-performing but sensitive to noise, and suffering global structure information loss. By contrast, filter-based counterparts, the Scattering Transform for instance, are tolerant to noise and translation but often lack of small local structure information. In this paper we propose an integration of those to take full advantages of both local and global features. In this way, LBP is used for extracting local features while the Scattering Transform feature plays the role of a global descriptor. In addition to the combination of these two state-of-the-art features, we further integrate a new preprocessing technique called biologically-inspired filtering (BF) as well as an efficient PCA classifier. Intensive experiments conducted on many texture benchmarks such as CUReT, UIUC, KTH-TIPS2b, and OUTEX show that our combined method not only outweighs each one which stands alone but also competes with state-of-the-art on the experimented datasets.

International audience; In this paper, we propose a combined feature approach which takes full advantages of local structure information and the more global one for improving texture image classification results. In this way, Local Binary Pattern is used for extracting local features, whilst the Scattering Transform feature plays the role of a global descriptor. Intensive experiments conducted on many texture benchmarks such as ALOT, CUReT, KTH-TIPS2-a, KTH-TIPS2b, and OUTEX show that the combined method outweigh each one which stands alone in term of classification accuracy. Also, our method outperforms many others, whilst it is comparable to state of the art on the experimented datasets.

International audience; Entropy Congealing is an unsupervised joint image alignment method, in which the transformation parameters are obtained by minimizing a sum-of- entropy function. Our previous work presented a forward formulation of entropy Congealing to estimate all the transformation parameters at the same time. In this paper, we propose an inverse compositional Lucas-Kanade formulation of entropy Congealing. This yields constant parts in Jacobian and Hessian which can be precomputed to decrease the computational complexity. Moreover, we combine Congealing with POEM descriptor to catch more information about face. Experimental results indicate that the proposed algorithm performs better than other alignment methods, regarding several evaluation criteria on different databases. Concerning the complexity, the proposed algorithm is more efficient than other considered approaches. Also, compared to the forward formulation, the inverse method produces a speed improvement of 20%.

International audience; A large number of car crashes are caused by drowsiness every year. The analysis of eye blinks provides reliable information about drowsiness. This paper proposes to study the relation between electrooculogram (EOG) and video analysis for blink detection and characterization. An original method to detect and characterize blinks from a video analysis is presented here. The method uses different filters based on the human retina modelling. A illumination robust filter is first used to normalize illumination variations of the video input. Then, Outer and an Inner Plexiform Layer filters are used to extract energy signals from eye area. The eye detection is processed mixing gradient and projection methods which makes it able to detect even closed eyes. The illumination robust filter makes it possible to detect the eyes even in night conditions, without using external lighting. The video analysis extracts two signals from the eye area measuring the quantity of static edges and moving edges. Blinks are then detected and characterized from these two signals. A comparison between the features extracted from the EOG and the same features extracted from the video analysis is then performed on a database of 14 different people. This study shows that some blink features extracted from the video are highly correlated with their EOG equivalent: the duration, the duration at 50%, the frequency, the percentage of eye closure at 80% and the amplitude velocity ratio. The frame rate influence on the accuracy of the different features extracted is also studied and enlightens on the need of a high frame rate camera to detect and characterize accurately blinks from a video analysis.

International audience; This work proposes an unsupervised jointalignment framework, referred to as ‘‘Gradient CorrelationCongealing,’’ which aligns an image ensemble bymaximizing a sum of gradient correlation coefficientfunction defined over all images. We, respectively, developtwo different formulations to optimize the objective functionregarding the role of ‘‘template.’’ While most existingface alignment methods suffer from outliers, e.g., occlusions,the proposed algorithms are able to align faces undergoingpartial occlusions. Moreover, our algorithms cancope with nonuniform illumination changes (even extremelydifficult ones), and also, they do not require anypredefined templates. We test the novel approaches againstfour typical joint alignment methods including Least-Squares Congealing, Learned-Miller Congealing, Lucas–Kanade entropy Congealing, and RASL using three challengingface databases: AR, Yale B, and LFW. Experimentalresults prove the efficiency of our approachesunder different conditions, especially when faces are partiallyoccluded, and the proposed algorithms perform muchbetter than all considered methods

International audience; The intent of 3D-models classification is to find categories of similar objects according to their shapes. This task is a challenging and important problem in 3D-mining and shape processing. In this paper, we present a novel method to categorize 3D-objects based on view-based descriptors. The proposed method goes into two stages. The first stage corresponds to the training in which 3D-objects in the same category are processed and a set of representative 2D views is selected, The second stage corresponds to the labelling in which unknown objects are classified using a belief based classifier. The experimental results obtained on the Shrec07 datasets show that the system efficiently performs in categorizing 3D-models.

International audience; A new action model is proposed, by revisiting local binary patterns for dynamic texture models, applied on trajectory beams calculated on the video. The use of semi dense trajectory field allows to dramatically reduce the computation support to essential mo-tion information, while maintaining a large amount of data to ensure robustness of statistical bag of features action models. A new binary pattern, called Spatial Motion Pattern (SMP) is proposed, which captures self similarity of velocity around each tracked point(particle), along its trajectory. This operator highlights the geometric shape of rigid parts of moving objects in a video sequence. SMPs are combined with basic velocity in-formation to form the local action primitives. Then, a global representation of a space × time video block is provided by using hierarchical blockwise histograms, which allows to efficiently represent the action as a whole, while preserving a certain level of spa-tiotemporal relation between the action primitives. Inheriting from the efficiency and the invariance properties of both the semi dense tracker Video extruder and the LBP based representations, the method is designed for the fast computation of action descrip-tors in unconstrained videos. For improving both robustness and computation time in the case of high definition video, we also present an enhanced version of the semi dense tracker based on the so called super particles, which reduces the number of trajectories while improving their length, reliability and spatial distribution.

International audience; A new method for action recognition is proposed by revisit-ing LBP-based dynamic texture operators. It captures the similarity of motion around keypoints tracked by a realtime semi-dense point track-ing method. The use of self-similarity operator allows to highlight the geometric shape of rigid parts of foreground object in a video sequence. Inheriting from the efficient representation of LBP-based methods and the appearance invariance of patch matching method, the method is well designed for capturing action primitives in unconstrained videos. Action recognition experiments, made on several academic action datasets vali-date the interest of our approach.

This paper presents improvements for face recognition methods that use LBP descriptor as a main technique in encoding micro features of face images. Our improvements are focused on the feature extraction and dimension reduction steps. In feature extraction, we use a variant of Local Binary Pattern (LBP) so-called Elliptical Local Binary Pattern (ELBP), which is more efficient than LBP for extracting micro facial features of the human face. ELBP of one pixel is built by thresholding its gray value with its P neighboring pixels on a horizontal ellipse. ELBP operator is applied in Pattern of Oriented Edge Magnitudes (POEM) to build Elliptical POEM (EPOEM) descriptor. The dimension reduction step is conducted by using Singular Value Decomposition (SVD) based Whitened Principal Component Analysis (WPCA). For performance evaluation of our improvements, we compare them with LBP based, POEM based approaches and other popular face recognition systems. The experimental results on state-of-the-art FERET and AR face databases prove the advantages and effectiveness of our improvements.

International audience; Describing efficiently faces is a task of mounting importance. Most of existing algorithms do not address all the three criteria, i.e., robustness, distinctiveness and low computational cost. Inspired by recent features so-called POEM (Patterns of Oriented Edge Magnitudes) which is argued balancing well the three concerns, we first provide an improvement to it and then propose novel features so-called self-POEM considering the relations between edge distributions along different directions of one region itself. Self-POEM provides the complementary strength which is not present in POEM. Combing them together, a more robust algorithm is obtained and by experiments we prove that our approach is more efficient than contemporary ones.

International audience; Real-world face recognition systems require careful balancing of three concerns: computational cost, robustness, and discriminative power. In this paper we describe a new descriptor, POEM (Patterns of Oriented Edge Magnitudes) by applying a self-similarity based structure on oriented magnitudes and prove that it addresses all three criteria. Experimental results on the FERET databases show that POEM outperforms other descriptors when used with nearest neighbour classifiers. With LFW by combining POEM with GMMs and with multi-kernel SVMs, we achieve comparable results to the state of the art. Impressively, POEM is around 20 times faster than Gabor-based methods

International audience; Good face recognition system is one which quickly delivers high accurate results to the end user. For this purpose, face representation must be robust, discriminative and also of low computational cost in both terms of time and space. Inspired by recently proposed feature set so-called POEM (Patterns of Oriented Edge Magnitudes) which considers the relationships between edge distributions of different image patches and is argued balancing well the three concerns, this work proposes to further exploit patterns of both orientations and magnitudes for building more efficient algorithm. We first present novel features called Patterns of Dominant Orientations (PDO) which consider the relationships between "dominant" orientations of local image regions at different scales. We also propose to apply the whitened PCA technique upon both the POEM and PDO based representations to get more compact and discriminative face descriptors. We then show that the two methods have complementary strength and that by combining the two descriptors, one obtains stronger results than either of them considered separately. By experiments carried out on several common benchmarks, including both frontal and nonfrontal FERET as well as the AR datasets, we prove that our approach is more efficient than contemporary ones.

International audience; A good feature descriptor is desired to be discriminative, robust, and computationally inexpensive in both terms of time and storage requirement. In the domain of face recognition, these properties allow the system to quickly deliver high recognition results to the end user. Motivated by the recent feature descriptor called Patterns of Oriented Edge Magnitudes (POEM), which balances the three concerns, this paper aims at enhancing its performance with respect to all these criteria. To this end, we first optimize the parameters of POEM and then apply the whitened principal-component-analysis dimensionality reduction technique to get a more compact, robust, and discriminative descriptor. For face recognition, the efficiency of our algorithm is proved by strong results obtained on both constrained (Face Recognition Technology, FERET) and unconstrained (Labeled Faces in the Wild, LFW) data sets in addition with the low complexity. Impressively, our algorithm is about 30 times faster than those based on Gabor filters. Furthermore, by proposing an additional technique that makes our descriptor robust to rotation, we validate its efficiency for the task of image matching.

Poster session: Biometrics; International audience; Finding facial features respectively under expression and illumination variations is always a difficult problem. One popular solution for improving the performance of facial point localization is to use the spatial relation between facial feature positions. While existing algorithms mostly rely on the priori knowledge of facial structure and on a training phase, this paper presents an online approach without requirements of pre-defined constraints on feature distributions. Instead of training specific detectors for each facial feature, a generic method is first used to extract a set of interest points from test images. With a robust feature descriptor named Patterns Oriented Edge Magnitude (POEM), a smaller set of these points are picked as candidates. Then we apply a game-theoretic technique to select facial points from the candidates, while the global geometric properties of face are well preserved. The experimental results demonstrate that our method achieves satisfactory performance for face images under expression and lighting variations

Due to its wide variety of real-life applications, ranging from user-authentication (access control, ATM) to video surveillance and law enforcements, face recognition has been one of the most active research topics in computer vision and pattern recognition. Also, it has obvious advantages over other biometric techniques, since it is natural, socially well accepted, and notably non-intrusive. In reality, several reliable biometrics authentication techniques are available and widely used nowadays (such as iris or fingerprint), but they mostly rely on an active participation of the user. On the contrary, facial biometric demands very little cooperation from the user; thanks to this user-friendly capability, face recognition is said to be non-intrusive. Over the last decades, significant progress has been achieved in face recognition area. Since the seminal work of Turk and Pentland (Turk & Pentland, 1991), where the Principal Component Analysis (PCA) is proposed to apply to face images (Eigenfaces), more sophisticated techniques for face recognition appear, such as Fisherfaces (Belhumeur et al., 1997), based on linear discriminant analysis (LDA), Elastic Bunch Graph Matching (EBGM) (Wiskott et al., 1997), as well as approaches based upon Support Vector Machines (SVM) (Phillips, 1999), or Hidden Markov Models (HMM) (Nefian & III, 1998; Vu & Caplier, 2010b), etc. Nevertheless, face recognition, notably under uncontrolled scenarios, remains active and unsolved. Among many factors affecting the performance of face recognition systems, illumination is known to be one of the most significant. Indeed, it was proven, both theoretically (Moses et al., 1994) and experimentally (Adini et al., 1997) that image variation due to lighting changes is more significant than that due to different personal identities. In other words, the difference between two face images of the same individual taken under varying lighting conditions is larger than the difference between any two face images taken under the same lighting conditions, as illustrated in Fig. 1. Inspired by the great ability of human retina that enables the eyes to see objects in different lighting conditions, we present in this chapter a novel method of illumination normalization by simulating the performance of its two layers: the photoreceptors and the outer plexiform layer. Thus, we say the algorithm biologically inspired. The rest of the chapter is structured as follows: Section 2 briefly discusses the related work; Section 3 presents the model of retinal processing and its advantage. In Section 4, the 6

International audience; In this paper we present a new architecture for face recognition with a single reference image, which separates the training process from the recognition process completely. In the training stage, by using a database containing various individuals, the spatial relations between face components are represented by two Hidden Markov Models, one of which models the similarity between face images coming from the same individual (within-subject similarities), and the other modelling inter-subject differences. This allows us to take a pair of face images, neither of which has been seen before, and determine whether or not they come from the same individual. Whilst other face-recognition HMMs use Maximum Likelihood, we have tested our approach using both Maximum Likelihood and MAP estimation, and find that MAP estimation provides better results. Importantly, the training database can be entirely separated from the gallery and test images: this means that adding new individuals to the system can be done without re-training. We present results based upon models trained on the FERET training dataset, and demonstrate that these give excellent recognition rates on both the FERET database itself and more impressively the unseen AR database. When compared to other HMM based face recognition techniques, our algorithm is of much lower complexity due to the small size of our observation sequence.

International audience; Performance of face recognition systems drop drastically when blur effect is present on facial images. In this paper, we propose a new approach for blurred face recognition. Our method is based on a measure of the level of blur introduced in the image using a no-reference blur metric. The face recognition process can be performed with any facial feature descriptor to allow the combination of alternative methods for overcoming data acquisition problems introduced in an image. To assess its efficiency, the approach has been applied with Gabor wavelets, Local Binary Patterns (LBP) and Local Phase Quantization (LPQ) facial descriptors on the FERET data-set. Experimental results clearly show the strength of this method at overcoming the problem caused by various forms of blur whatever the facial feature descriptor are implemented.

International audience; The performance of face recognition systems can be dramatically degraded when the pose of the probe face is different from the gallery face. In this paper, we present a pose robust face recognition model, centered on modeling how face patches change in appearance as the viewpoint varies. We present a novel model based on two robust local appearance descriptors, Gabor wavelets and Local Binary Patterns (LBP). These two descriptors have been widely exploited for face recognition and different strategies for combining them have been investigated. However, to the best of our knowledge, all existing combination methods are designed for frontal face recognition. We introduce a local statistical framework for face recognition across pose variations, given only one frontal reference image. The method is evaluated on the Feret pose dataset and experimental results show that we achieve very high recognition rates over the wide range of pose variations presented in this challenging dataset.

Reinforced concrete (RC) structural walls with coupling beams are widely used as the primary lateral-load-bearing elements in high-rise building structures. Many researches have shown that there is uncertainty in the estimation of the effective stiffness of RC coupling beams. In an attempt to develop rational approaches regarding the stiffness of these structural components, this paper presents the analytical approaches, considering the influence of flexural and shear deformations, to determine the effective stiffness of RC coupling beams. A comprehensive parametric study, including 144 combinations for the conventionally reinforced concrete coupling beam (CCB) and 48 combinations for the diagonally reinforced concrete coupling beam (DCB), is carried out and two equations to estimate the effective stiffness of RC coupling beams are proposed each as a function of aspect ratio, transverse reinforcement ratio, longitudinal reinforcement ratio, diagonal reinforcement ratio and concrete compressive strength, on the basis of these parametric case studies. The proposed analytical approaches and the equations for assessing the effective stiffness of CCBs and DCBs are then verified by comparison with experimental results obtained from literature. (C) 2014 Elsevier Ltd. All rights reserved.