An Estimation of the Safety Risk Factors Encountered During Tower Crane Installation and Dismantling on Construction Sites in Vietnam

The construction field has an important and meaningful role in the economy of any nation, especially developing nations. However, construction is one of the most dangerous fields and has the highest rate of accidents, including deaths and disabling injuries in the world. The construction field uses many tower cranes, especiallyfor constructing multi-storey buildings, factories, or in urban areas, and requires an increasing number of tower cranes. Tower cranes certainly contribute to the high rate of construction injuries and fatalities, in which tower crane installation and dismantling take a high rate. Accidents during tower crane installation and dismantling can kill people (workers and citizens) as well as delays in construction schedules of project and/or damage to buildings and machines under construction. As a result, the cost of costruction projects can increase. In  this paper, safety risk factors during tower crane installation and dismantling on construction sites in Vietnam are estimated by showing their likelihood of occurrence, degree of influence, and risk levels. A suitable-structured questionnaire was produced and sent to get data that had their likelihood of occurrence and degree of influence by applying a five-point Likert scale. The results showed that “Time constraints are requested by investor, principal contractor or employer” is the most likely factor with a mean value of 3.60 and “Break of a wire rope occurring on dismantling” has the highest degree of influence, with a mean value of 4.18. The result also showed that there are 15/21 factors with a moderate risk level that is acceptable but, requires suitable controls to maintain a safe working condition of tower crane installation and dismantling. The results of the paper may help managers as well as practicians with good understandings of how to advance the safety of tower crane installation and dismantling on construction sites in Vietnam

A Back Propagation Neural Network Model with the Synthetic Minority Over-Sampling Technique for Construction Company Bankruptcy Prediction

Improving model accuracy is one of the most frequently addressed issues in bankruptcy prediction. Several previous studies employed artificial neural networks (ANNs) to improve the accuracy at which construction company bankruptcy can be predicted. However, most of these studies use the sample-matching technique and all of the available company quarters or company years in the dataset, resulting in sample selection biases and between-class imbalances. This study integrates a back propagation neural network (BPNN) with the synthetic minority over-sampling technique (SMOTE) and the use of all of the available company-year samples during the sample period to improve the accuracy at which bankruptcy in construction companies can be predicted. In addition to eliminating sample selection biases during the sample matching and between-class imbalance, these methods also achieve the high accuracy rates. Furthermore, the approach used in this study shows optimal over-sampling times, neurons of the hidden layer, and learning rate, all of which are major parameters in the BPNN and SMOTE-BPNN models. The traditional BPNN model is provided as a benchmark for evaluating the predictive abilities of the SMOTE-BPNN model. The empirical results of this paper show that the SMOTE-BPNN model outperforms the traditional BPNN

Modified sunflower optimization for network reconfiguration and distributed generation placement

This paper proposed modified sunflower optimization (MSFO) for the combination of network reconfiguration and distributed generation placement problem (NR-DGP) to minimize power loss of the electric distribution system (EDS). Sunflower optimization (SFO) is inspired form the ideal of sunflower plant motion to get the sunlight and its reproduction. To enhance the performance of SFO, it is modified to MSFO wherein, the pollination and mortality techniques have been modified by using Levy distribution and mutation of the best solutions. The results are evaluated on two test systems. The efficiency of MSFO is compared with that of the original SFO and other algorithms in literature. The comparisons show that MSFO outperforms to SFO and other methods in obtained optimal solution. Furthermore, MSFO demonstrates the better statistical results than SFO. So, MSFO can be a powerful approach for the NR-DGP problem

SPATIAL-SPECTRAL FUZZY K-MEANS CLUSTERING FOR REMOTE SENSING IMAGE SEGMENTATION

Spectral clustering is a clustering method based on algebraic graph theory. The clustering effect by using spectral method depends heavily on the description of similarity between instances of the datasets. Althought, spectral clustering has been significant interest in recent times, but the raw spectral clustering is often based on Euclidean distance, but it is impossible to accurately reflect the complexity of the data. Despite having a well-defined mathematical framework, good performance and simplicity, it suffers from several drawbacks, such as it is unable to determine a reasonable cluster number, sensitive to initial condition and not robust to outliers. In this paper, we present a new approach named spatial-spectral fuzzy clustering which combines spectral clustering and fuzzy clustering with spatial information into a unified framework to solve these problems, the paper consists of three main steps: Step 1, calculate the spatial information value of the pixels, step 2 applies the spectral clustering algorithm to change the data space from the color space to the new space and step 3 clusters the data in new data space by fuzzy clustering algorithm. Experimental results on the remote sensing image were evaluated based on a number of indicators, such as IQI, MSE, DI and CSI, show that it can improve the clustering accuracy and avoid falling into local optimum.

Speedup of Interval Type 2 Fuzzy Logic Systems Based on GPU for Robot Navigation

As the number of rules and sample rate for type 2 fuzzy logic systems (T2FLSs) increases, the speed of calculations becomes a problem. The T2FLS has a large membership value of inherent algorithmic parallelism that modern CPU architectures do not exploit. In the T2FLS, many rules and algorithms can be speedup on a graphics processing unit (GPU) as long as the majority of computation a various stages and components are not dependent on each other. This paper demonstrates how to install interval type 2 fuzzy logic systems (IT2-FLSs) on the GPU and experiments for obstacle avoidance behavior of robot navigation. GPU-based calculations are high-performance solution and free up the CPU. The experimental results show that the performance of the GPU is many times faster than CPU

Refinement of an inverse analysis procedure for estimating tensile constitutive law of UHPC

As regard to cementitious composite materials added a certain dosage of fiber, estimation of tensile constitutive law through inverse analysis methods is no longer extraordinary. However, development or improvement to achieve an effective method for estimating such a tensile behavior of fiber reinforced concrete (FRC) or Ultra high-performance concrete (UHPC) is still an interesting topic to researchers. In this respect, the paper presents a development of inverse analysis method developed by Lopez to obtain the stress-strain behavior of UHPC from the four-point bending test. By applying optimization algorithm into the iterative procedure of method, an improvement could be obtained for the inverse analysis with a high degree of automation in calculation. A post-process treatment for inverse analysis results is also proposed to bring a finer agreement between the tensile behavior curve obtained by the inverse analysis and result curve of uniaxial tensile test (UTT). The effectivity of process is shown through a comparison between the result obtained by the proposed method and the result in Lopez’s public paper

A Novel Self-organizing Fuzzy Cerebellar Model Articulation Controller Based Overlapping Gaussian Membership Function for Controlling Robotic System

This paper introduces an effective intelligent controller for robotic systems with uncertainties. The proposed method is a novel self-organizing fuzzy cerebellar model articulation controller (NSOFC) which is a combination of a cerebellar model articulation controller (CMAC) and sliding mode control (SMC). We also present a new Gaussian membership function (GMF) that is designed by the combination of the prior and current GMF for each layer of CMAC. In addition, the relevant data of the prior GMF is used to check tracking errors more accurately. The inputs of the proposed controller can be mixed simultaneously between the prior and current states according to the corresponding errors. Moreover, the controller uses a self-organizing approach which can increase or decrease the number of layers, therefore the structures of NSOFC can be adjusted automatically. The proposed method consists of a NSOFC controller and a compensation controller. The NSOFC controller is used to estimate the ideal controller, and the compensation controller is used to eliminate the approximated error. The online parameters tuning law of NSOFC is designed based on Lyapunov’s theory to ensure stability of the system. Finally, the experimental results of a 2 DOF robot arm are used to demonstrate the efficiency of the proposed controller

Speedup of Interval Type 2 Fuzzy Logic Systems Based on GPU for Robot Navigation

As the number of rules and sample rate for type 2 fuzzy logic systems (T2FLSs) increases, the speed of calculations becomes a problem. The T2FLS has a large membership value of inherent algorithmic parallelism that modern CPU architectures do not exploit. In the T2FLS, many rules and algorithms can be speedup on a graphics processing unit (GPU) as long as the majority of computation a various stages and components are not dependent on each other. This paper demonstrates how to install interval type 2 fuzzy logic systems (IT2-FLSs) on the GPU and experiments for obstacle avoidance behavior of robot navigation. GPUbased calculations are high-performance solution and free up the CPU. The experimental results show that the performance of the GPU is many times faster than CPU