Cost estimation of structural skeleton using an interactive automation algorithm: A conceptual approach

This paper addresses a new concept for generating bills of quantities (B.o.Qs) using AutoCAD drawings for a building project, which demonstrates the application of Industry Foundation Class (IFC), developed by International Alliance for Interoperability (IAI). The procedure considers types of materials and the structural shapes of the AutoCAD drawings to compute the cost of the structural skeleton elements using interactive automation. The main concept focuses on using layer computation of the AutoCAD drawing after converting it into a drawing inter-exchangeable file format (DXF). Once the coordinates are detected, it is easier to determine the area and volume for any structural shape, including circles and polygons. The extracting method is a new technique for structural engineers and quantity surveyors to estimate required material for beam, columns, slabs and foundations. The algorithm extracts and recognizes the layers and objects from a two dimensional DXF drawing along with the coordinates information. The results obtained using this technique are more accurate and reliable than manual procedures or any other traditional techniques. In this paper, an automated and interactive procedure for B.o.Q computation is demonstrated. The process involves a user-friendly interface, dynamic linking to the structural drawings and tracking of B.o.Q modifications at the same time.     

Consistency of robust estimators in multi-structural visual data segmentation

A theoretical framework is presented to study the consistency of robust estimators used in vision problems involving extraction of fine details. A strong correlation between asymptotic performance of a robust estimator and the asymptotic bias of its scale estimate is mathematically demonstrated where the structures are assumed to be linear corrupted by Gaussian noise. A new measure for the inconsistency of scale estimators is defined and formulated by deriving the functional forms of four recent high-breakdown robust estimators. For each estimator, the inconsistency measures are numerically evaluated for a range of mutual distances between structures and inlier ratios, and the minimum mutual distance between the structures, for which each estimator returns a non-bridging fit, is calculated.     

Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks

This study deals with the neuro-fuzzy (NF) modelling of a real industrial winding process in which the acquired NF model can be exploited to improve control performance and achieve a robust fault-tolerant system. A new simulator model is proposed for a winding process using non-linear identification based on a recurrent local linear neuro-fuzzy (RLLNF) network trained by local linear model tree (LOLIMOT), which is an incremental tree-based learning algorithm. The proposed NF models are compared with other known intelligent identifiers, namely multilayer perceptron (MLP) and radial basis function (RBF). Comparison of our proposed non-linear models and associated models obtained through the least square error (LSE) technique (the optimal modelling method for linear systems) confirms that the winding process is a non-linear system. Experimental results show the effectiveness of our proposed NF modelling approach.     

Towards an effective integration of cellular users to the structured Peer-to-Peer network

Integration of cellular users into the peer-to-peer (P2P) network is still in limbo due to limitations caused by heterogeneity, mobility and time-varying capacities of the wireless channel. If traditional Chord is employed to include users from the cellular networks, users under the same base station scatter in logical topology randomly. In this paper, we present a novel cellular Chord (C-Chord) P2P system that integrates the cellular users into the well-established structured P2P network in a topology-aware fashion. C-Chord offers the cellular users a choice of downloading contents either from the Internet peers at a faster rate or from other cellular users from the same base station avoiding the Internet data penalty. We also incorporate the peer selection module based on stable marriage problem that chooses the appropriate candidate from the discovered potential senders. We conduct extensive simulations to evaluate the performance of the proposed C-Chord P2P system and the peer selection module. Simulation results show that the path-length per lookup query is smaller than that of the traditional Chord system. Overhead due to renewal of routing information is also smaller for the cellular users in the C-Chord system. We also measure the throughput at the cellular receivers to analyze the effects of selecting peers either from same base station or from outside the Internet gateway. Throughput also increases dramatically due to an intelligent selection of peers among the potential senders.     

Hierarchical Fuzzy Systems Integrated with Particle Swarm Optimization for Daily Reference Evapotranspiration Prediction: a Novel Approach

Water Resources Management - Reference evapotranspiration (ET0) is a crucial element for deriving irrigation scheduling of major crops. Thus, precise projection of ET0 is essential for better...     

Converting Daily Rainfall Data to Sub-daily—Introducing the MIMD Method

One of the most important analysis in many hydrological and agricultural studies is to convert the daily rainfall data into sub-daily (hourly) because in many rainfall stations, only the daily rainfall data are available and for a comprehensive rainfall analysis, these data should be converted to sub-daily. Many experimental and analytical methods are available for this conversion but one of the simplest yet accurate ones has been proposed by the Indian Meteorological Department (IMD). Since the IMD method has shown low accuracy in some regions, in this study, the IMD method is modified to a single parameter equation, called Modified Indian Meteorological Department (MIMD) in order to improve the accuracy of the conversion. For this reason, the parameter is calibrated so that the maximum correlation between observed and estimated values is achieved. Five stations in different regions with different climatic conditions were selected so that the daily and sub-daily rainfall data were available in each of them. Then, the parameter of the MIMD method was derived for each station. The results were compared with both observed data and IMD method and it was shown that the mean correlation coefficient of MIMD and IMD methods were 0.9 and 0.73 respectively for 12-h rainfall depth which indicated that the accuracy of the MIMD method in estimation of sub-daily rainfall depths was significantly increased. Moreover, the results showed that the accuracy of the MIMD method decreases as rainfall duration decreases.

     

A Probabilistic Multiperiod Simulation–Optimization Approach for Dynamic Coastal Aquifer Management

Combined simulation–optimization (CSO) schemes are common in the literature to solve different groundwater management problems, and CSO is particularly well-established in the coastal aquifer management literature. However, with a few exceptions, nearly all previous studies have employed the CSO approach to derive static groundwater management plans that remain unchanged during the entire management period, consequently overlooking the possible positive impacts of dynamic strategies. Dynamic strategies involve division of the planning time interval into several subintervals or periods, and adoption of revised decisions during each period based on the most recent knowledge of the groundwater system and its associated uncertainties. Problem structuring and computational challenges seem to be the main factors preventing the widespread implementation of dynamic strategies in groundwater applications. The objective of this study is to address these challenges by introducing a novel probabilistic Multiperiod CSO approach for dynamic groundwater management. This includes reformulation of the groundwater management problem so that it can be adapted to the multiperiod CSO approach, and subsequent employment of polynomial chaos expansion-based stochastic dynamic programming to obtain optimal dynamic strategies. The proposed approach is employed to provide sustainable solutions for a coastal aquifer storage and recovery facility in Oman, considering the effect of natural recharge uncertainty. It is revealed that the proposed dynamic approach results in an improved performance by taking advantage of system variations, allowing for increased groundwater abstraction, injection and hence monetary benefit compared to the commonly used static optimization approach.

     

An improved volleyball premier league algorithm based on sine cosine algorithm for global optimization problem

Volleyball premier league (VPL) simulating some phenomena of volleyball game has been presented recently. This powerful algorithm uses such racing and interplays between teams within a season. Furthermore, the algorithm imitates the coaching procedure within a game. Therefore, some volleyball metaphors, including substitution, coaching, and learning, are used to find a better solution prepared by the VPL algorithm. However, the learning phase has the largest effect on the performance of the VPL algorithm, in which this phase can lead to making the VPL stuck in optimal local solution. Therefore, this paper proposed a modified VPL using sine cosine algorithm (SCA). In which the SCA operators have been applied in the learning phase to obtain a more accurate solution. So, we have used SCA operators in VPL to grasp their advantages resulting in a more efficient approach for finding the optimal solution of the optimization problem and avoid the limitations of the traditional VPL algorithm. The propounded VPLSCA algorithm is tested on the 25 functions. The results captured by the VPLSCA have been compared with other metaheuristic algorithms such as cuckoo search, social-spider optimization algorithm, ant lion optimizer, grey wolf optimizer, salp swarm algorithm, whale optimization algorithm, moth flame optimization, artificial bee colony, SCA, and VPL. Furthermore, the three typical optimization problems in the field of designing engineering have been solved using the VPLSCA. According to the obtained results, the proposed algorithm shows very reasonable and promising results compared to others.

     

Fuzzy reliability analysis of nanocomposite ZnO beams using hybrid analytical-intelligent method

A hybrid analytical-intelligent approach is proposed for fuzzy reliability analysis of the composite beams reinforced by zinc oxide (ZnO) nanoparticle. The fuzzy reliability index corresponding to buckling failure mode of nanocomposite beam under thickness-direction external voltage is computed based on three-levels: (1) fuzzy analysis, (2) reliability analysis and (3) analytical buckling analysis. In fuzzy analysis level, an improved gravitational search algorithm has been applied to determine uncertainty interval for membership levels of reliability index. The adaptive formulation with a dynamical self-adjusting process is used for reliability analysis level based on conjugate first-order reliability method (FORM). The self-adjusting term in conjugate sensitivity vector is used to satisfy the sufficient descent condition for controlling instability of FORM formula while the proposed conjugate scalar factor is computed less than the original conjugate FORM, thus it may be provided with the efficient results for the convex problem. The new and previous sensitivity vectors obtained by conjugate and steepest descent vectors dynamically adjusted the proposed conjugate factor. In the buckling analysis level, an exponential theory in conjunction with the method of energy is utilized. Fuzzy random variables including applied voltage, the volume fraction of ZnO, thickness of beam, spring constant and shear constant of the foundation are considered in studied nanocomposite beam. Survey results indicated that the proposed method can provide stable and acceptable fuzzy membership functions for parametric study. Moreover, the ratio of length to thickness and spring constant of foundation are the more sensitive parameters which affect fuzzy reliability index significantly.

     

A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques

Shear connectors play a prominent role in the design of steel-concrete composite systems. The behavior of shear connectors is generally determined through conducting push-out tests. However, these tests are costly and require plenty of time. As an alternative approach, soft computing (SC) can be used to eliminate the need for conducting push-out tests. This study aims to investigate the application of artificial intelligence (AI) techniques, as sub-branches of SC methods, in the behavior prediction of an innovative type of C-shaped shear connectors, called Tilted Angle Connectors. For this purpose, several push-out tests are conducted on these connectors and the required data for the AI models are collected. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to identify the most influencing parameters on the shear strength of the tilted angle connectors. Totally, six different models are created based on the ANFIS results. Finally, AI techniques such as an artificial neural network (ANN), an extreme learning machine (ELM), and another ANFIS are employed to predict the shear strength of the connectors in each of the six models. The results of the paper show that slip is the most influential factor in the shear strength of tilted connectors and after that, the inclination angle is the most effective one. Moreover, it is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction. It is also demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.