Weighbridge Research Articles

The Effect of Weigh Bridge on Logistic Timeliness The Case of Dar Es Salaam - Tunduma Route

The Effect of Weigh Bridge on Logistic Timeliness The Case of Dar Es Salaam - Tunduma Route

Post-Tensioned Hollow-Core Concrete Slabs with Unbonded Tendons for Truck Scale Platforms: Design Assumptions and Tests.

At Cracow University of Technology, attempts were made to develop national truck scale platforms with a capacity of 60 tons, made from prestressed concrete. For this work, we designed slabs partially prestressed with unbonded tendons featuring a cross-section of 1.00 × 0.28 m and a span of 5.94 m. To reduce the weight of the slabs, four channels made from commonly used ø110 × 2.2 mm PVC pipes were used. In this way, we created post-tensioned hollow-core slabs. Due to the unpredictable behavior of slabs operating in a cracked state under a repetitive load, two slabs were subjected to cyclic loads amounting to 1,000,000 cycles with different load values. This paper presents the basic design principles and design details of the slabs, as well as the methodology and results of the research conducted. Lastly, we provide appropriate conclusions directed at further optimizing the slabs.

Weighing Error Compensation for Truck Scales Based on IGWO–LMBP Neural Network

Weighing Error Compensation for Truck Scales Based on IGWO–LMBP Neural Network

Multifactorial Analysis of the Axle Load of Truck Sets during the Transport of Sawmill By-Products

The aim of the study was to investigate the actual axle loads of vehicles used for the transport of sawmill by-products, resulting from the total weight of the transport set (GVW) at different times of the year and depending on the type of sets and transported assortment. Sawmill by-product loads were divided into groups: wood chips, sawdust, slabs and recycled wood. The research was carried out at the recipient of the sawmill by-products as well as at the producer. The GVW was determined on the basis of weighing the entire set on a stationary scale at the premises of the companies. The load on the individual axles of high tonnage truck units was measured using Model DINI ARGEO WWSD portable truck scales with a 3590M309 weighing terminal. Almost 230 transports were analyzed. It was shown that the average GVW is 39.25 t for transport sets with a load of 22.44 t of sawmill by-products with the usage of the semi-trailer capacity of 0.85. For all transport sets, the average load on the individual axles range from 6.72 t (axle 5 in a six-axle truck and trailer-TT) to 10.29 t for axle 2 in a truck and semi-trailer (TST), where the highest occurs on axle 2 of the truck (drive axle). The influence of the type of transport set (TST or TT) and the type of the assortments on the axle load is shown, and the existing correlations are at the level of 0.604–0.669 for axles 3–5 in the TST set. The static axle loads of the transport sets for the sawmill by-products and the distribution of the total weight of the set among the axles are different from those for roundwood transport.

Research on the Verification Method of Electronic Truck Scale

Research on the Verification Method of Electronic Truck Scale

Finite element analysis for the lectotype of the large electronic truck scale structure

The stiffness of truck scales is the main influence factor on its measurement accuracy. Three kinds of SCS-100T large electronic truck scales are different in size arrangement of U-beam. Based on the finite element method, the models of the truck scales were established. The stiffness and strength of the scales were compared on account of the analysis results. The results show that U-beam arrangement of the low at both ends but high in the middle structure for the truck scale not only can save material but also meet the requirement of stiffness. Simulation analysis provides the theoretical basis for the structural design of truck scales.

Analysis of the Driving & Loading Pattern of the Construction Waste Collecting Trucks Using IoT On-Board Truck Scale System

Analysis of the Driving & Loading Pattern of the Construction Waste Collecting Trucks Using IoT On-Board Truck Scale System

Analysis on influence of static calibration on the axle-group weigh-in-motion system accuracy

The axle-group weigh-in-motion system has two functions: static weighing and dynamic weighing. According to the weighing model, the accuracy of dynamic weighing is affected by the static performance. This paper analyses the size of various factors affecting the static performance, such as sensor tilt installation, platform deformation, platform tilt installation, and these errors will lead to sensor swing, bearing head tilt, gravity line of action and sensor axis direction is not consistent, thus affecting the static weighing accuracy. However static calibration is the best way to reduce or even eliminate the above errors. The dynamic truck scale of different manufacturers with or without static calibration is used in the test process. The results show that the dynamic performance index can meet the requirements only after the static calibration is used.

Zero-point fault detection of load cells in truck scale based on recursive principal component analysis and comprehensive evaluation method

A zero-point fault of load cells in truck scale is a typical small fault and it is difficult to be detected online. This paper proposes a new method for detecting zero-point fault online by combining a recursive principal component analysis (RPCA) with a comprehensive evaluation method (CEM). In this new method, firstly, the principal component model is updated online and the estimation of load cells is obtained by the principal recursive algorithm based on rank-1 modification, and then the four statistics, i.e., the Hotelling’s T2 statistic, the squared prediction error (SPE) statistic, the Hawkins TH2 statistic, and the principal component related variable residual (PVR) statistic, are used to construct a comprehensive evaluation method (CEM) for fault detection. This proposed method is applied to detecting the zero-point fault of load cells in truck scale, and the experimental results show that the accuracy of the zero-point fault detection is far higher than that of the traditional method (i.e., it is detected by only using any one of the four statistics, such as SPE, T2, TH2, PVR), which proves the effectiveness of this proposed method.

Application of Fuzzy C-Mean Clustering Based on Multi-Polar Fuzzy Entropy Improvement in Dynamic Truck Scale Cheating Recognition

In the big data background, the uncertainty of data is increasingly apparent. Multi-polar fuzzy feature of data has been more popularly used by the research community for the purpose of the classification of weighing cheating in dynamic truck scale characteristic and the clustering problem of multi-polar fuzzy feature information. Additionally, the traditional classification method leads to slow speed and inaccuracy because of its difficulties. Therefore, by considering a multi-polar fuzzy feature classification of defects, a fuzzy c-means ( FCM) clustering algorithm based on multi-polar fuzzy entropy is proposed. Firstly, according to the evaluation of available characteristics, the characteristic value of clustering samples is established. Secondly, we calculated the multi-polar fuzzy entropy of clustering samples. Finally, an improved FCM clustering algorithm based on multipolar fuzzy entropy is presented. The experimental results of the data set collected from 5 different types of weighing cheating cars demonstrate that the algorithm improves the classification accuracy of FCM with multi-polar fuzzy feature information clustering and reduces significantly both the number of iterations and the classification time.

An Analysis of Driving Pattern and Transportation Efficiency of Commercial Vehicle using On-board Truck scale

An Analysis of Driving Pattern and Transportation Efficiency of Commercial Vehicle using On-board Truck scale

About Distributed Internal and Surface Strain Measurements within Prestressed Concrete Truck Scale Platforms

Distributed optical fibre measurement technology provides new possibilities in structural technical condition assessment in comparison with traditional spot measurements. It is not only possible to analyse strains continuously over structural member length with spatial resolution starting from as fine as 5 mm, but also the cracks and displacements state within reinforced concrete structures. The article presents pilot studies regarding prestressed concrete track scale platforms with a length of 8 to 16 m. Optical fibres were glued within composite rods attached to the stirrups inside the cross section of the slab on the height of lower and upper prestressing tendons, as well as they were glued to the concrete surface after bonding. That allows for comprehensive analysis of concrete strains state including all local nonlinearities (cracks) starting from hydration process (thermal-shrinkage strains), through prestressing tendons activation (strains regarding the transfer of compression forces from the tendond to the concrete) and finally during laboratory tests, when slabs were mechanically loaded until destruction. The ways of installation and exemplary results from selected phases of the research were presented as well as data interpretation was described and discussed.

The position of speed bump in front of truck scale based on vehicle vibration performance

The position of speed bump in front of truck scale based on vehicle vibration performance

The Effect of Epidural-Spinal Anesthesia on Labor Outcome and Satisfaction in Parturient Mothers: A Randomized Controlled Trial

Introduction: One of the health system concerns is the use of medications for pain relief during labor and its side effects. Therefore, the aim of this study was to investigate the effect of epiduralspinal anesthesia (combined anesthesia [CA]) on labor outcome and satisfaction in pregnant women. Methods: In this randomized controlled trial study, we included 80 nulliparous women who had been admitted to Fatemieh hospital (Hamadan, Iran) during 2015-2016 due to spontaneous onset of labor. They were randomly assigned into 2 groups of 40, one group with CA versus normal vaginal delivery (NVD) group. Data were collected by using of demographic questionnaire, satisfaction questionnaire, and baby truck scales. Data were analyzed by descriptive and analytical statistics in SPSS version 16.0. Results: Average maternal age (mean ± SD) in the CA group was 26.94 ± 4.34 and in the NVD group was 25.89 ±5.18, respectively. There was a significant difference between the 2 groups in terms of length of second stage of labor (P=0.001), headache (P=0.04), and Apgar score (first minute) (P=0.001). Chi-square test showed a significant difference between the 2 groups in terms of satisfaction with childbirth (P=0.004). Conclusion: In this study, labor pain relief by using the spinal-epidural anesthesia (CA) increased the labor satisfaction. Nevertheless, this approach was associated with some maternal and neonatal complications such as: headaches, length of third stage of labor, and low Apgar score. It seems that the use of this method for painless delivery requires further studies.

Optimization for a Neural Network Based on Input-Vectors Correlation and Its Application to a Truck Scale

It is important and valuable to improve the generalization ability of a neural network (NN) when there is a lack of training samples. This paper presents an approach of optimizing a NN based on input-vectors correlation (IVCNN) to improve the NN's ability in the case of lacking samples, and then uses this method to compensate for the weighing errors of a truck scale. First, we analyze the truck scale's weighing principle and the spatial correlation of the truck scale's input signals, and then use this correlation to construct the constraint conditions and the performance index for training an NN. Finally, we give the detailed training algorithm of IVCNN. In addition, we prove the IVCNN's convergence and analyze its anti-interference performance, convergent speed, and computational complexity. The experimental results demonstrate the effectiveness of IVCNN by comparison with an NN based on the data induction method (DINN, i.e., an NN trained only by data samples, not prior knowledge) and supported vector regression.

Weighing method for truck scale based on neural network with weight-smoothing constraint

This paper proposes a novel approach of optimizing a neural network with weight-smoothing constraint (WSCNN) and a weighing method for a truck scale based on WSCNN. In this method, the truck scales’ prior knowledge, i.e., the correlation of the load cells’ outputs, is used to construct the constraint conditions for optimizing a neural network (NN) in the case of a lack of samples, and then the NN’s performance index is constructed and the detail algorithm of WSCNN is given. The experimental results show that the weighing errors of the truck scale with WSCNN are far less than those of NN with data induction method (DIMNN, it is an algorithm of training an NN only by using the data samples, not the prior knowledge). In addition, the WSCNN’s generalization ability is better than that of DIMNN especially in the case of a lack of samples.

Weighing method for a truck scale based on optional neural network with penalty function

This paper presents a weighing method for a truck scale based on a neural network (NN) with penalty function (PFNN). Firstly, the truck scale’s prior knowledge, i.e. the positive partial derivatives of the truck scale’s input–output function and the distributions of truck scale’s permissible weighing errors, is used to construct the constraints for optimizing the NN. Then, the penalty function is applied to construct the new NN’s performance index, and the detailed algorithm for training this NN is given. Finally, the method for assigning the value of the penalty factors is also discussed. The comparative experimental results show that the PFNN’s generalization ability is better than that of a data-induced NN (DINN) especially with a lack of training samples (the DINN is a method for training an NN only by using the data samples, not prior knowledge), and the weighing errors of the truck scale with PFNN are far less than those of DINN. In addition, the convergence of the PFNN is faster than that of the DINN.

Weighing fusion method for truck scale based on an optimal neural network with derivative constraints and a lagrange multiplier

This paper presents a novel weighing fusion method for a truck scale based on an optimal neural network with partial derivative constraints and a Lagrange multiplier (PD-LMNN). In this proposed method, firstly, the constraints for optimizing neural network (NN) are constructed via the truck scale’s prior knowledge that the partial derivative of the truck scale’s input–output function is positive. Secondly, the neural network’s performance index is created by using the augmented Lagrange function with a multiplier and a penalty factor. Thirdly, the detail algorithm of training the constrained-optimization NN is given. This proposed method can improve the NN’s generalization ability in case of the lacking training samples. The comparative experimental results show that the weighing errors of the truck scale with PD-LMNN are far less than those of DINN (DINN is a method for training a NN only by using the data samples, not the prior knowledge), and the PD-LMNN’s generalization ability is better than that of DINN when the training samples are lacking.

Weighing Fusion Method for Truck Scales Based on Prior Knowledge and Neural Network Ensembles

This paper presents a new approach of compensating truck scale's weighing errors based on prior knowledge and neural network ensembles (PKNNEs). Truck scale is a typical nonlinear system and it is fussy and labor intensive to compensate the weighing errors with the conventional method, leading to low accuracy of the weighing results. The general idea of this proposed approach consists of building individual neural networks (NNs) and designing the constraint conditions for optimizing neural network ensembles (NNEs) with the prior knowledge of the truck scale. First, three uncorrelated individual NNs are created by using the step-distribution characteristics of the truck scale's permissible maximum weighing error. Second, the constraint conditions for training the individual NNs are constructed by using the ideal weighing model and its derivatives, which can significantly improve the generalization ability of NNs, especially when the training samples are few or lacking. The detailed design procedure of this proposed method is given, the weighing principle of truck scale is discussed, and its weighing error models are found in this paper. Experimental results demonstrate the effectiveness of this method, and the testing results of a truck scale with PKNNEs in the field show that it meets the requirement for the weighing accuracy of medium-class scale defined by OIML R76 “nonautomatic weighing instruments.”

A New Truck Scale On-Line Remote Anti-Cheating System Based on Information Technology

Truck scale is one of the important weighing equipment. To on-line remote monitoring truck scale cheating devices, new truck scale on-line remote anti-cheating system is brought out and truck scale on-line remote anti-cheating system is designed. The remote anti-cheating system includes the anti-cheating monitoring terminal, CAN and GPRS transmission module and remote weighing anti-cheating system. Truck scale anti-cheating monitoring terminal collects the anti-cheating data and sent to the remote anti-cheating system through CAN and GPRS transmission module. The experimental system is built and the experiment is carried out. The experimental results show that the on-line remote anti-cheating system of the truck scale can on-line monitor truck scale cheating devices and improves the system security.