Cost-Effective Inspection of Rebar Spacing and Clearance Using RGB-D Sensors

The quality assurance of constructing reinforced concrete (RC) structures in compliance with their design plays a key role in the durability, serviceability, and sustainability of the built RC elements. One area of concern in the quality control of constructing RC structures is examining the position and dimension of the rebars before pouring fresh concrete. Currently, this is accomplished by visual inspection and individually by hand with limited time available between construction stages. Over the past decades, structural health and monitoring during the construction period has applied remote sensing technologies. However, little research has focused on the use of such technologies to inspect and evaluate rebar placement prior to concrete pouring as quality control. In this study we develop an algorithm that facilitates inspecting the positions of rebars and the cover of concrete using a new-generation low-cost RGB-D sensor to find incorrect rebar placement. The proposed method is evaluated using a typical 5 × 5 two-layer rebar cage in the laboratory by comparing the proposed technique with traditional inspection methods. The results show that the RGB-D sensor can achieve cost-effective inspection for rebar spacing and clearance with an acceptable tolerance. The evaluation of rebar spacing results shows that the maximum standard deviation for rebar spacing is 0.34 inch (8.64 mm) between longitudinal rebar 2 and 3, which is the same as the rebar construction and traditional tape measurement results. The concrete cover estimation results show that the maximum standard deviation for rebar cage concrete cover is 0.19 inch (4.83 mm) for longitudinal rebar 3. The issues of new RGB-D sensor scan settings and the test results will be helpful for practitioners in improving construction quality.

Implementasi Graph Clustering Algorithm Modification Maximum Standard Deviation Reduction (MMSDR) dalam Clustering Provinsi di Indonesia Menurut Indikator Kesejahteraan Rakyat

<p>The population of Indonesia from year to year has increased. The increase in population must also be accompanied by increased economic growth in Indonesia. The increase in economic growth in Indonesia is marked by the reduction in the number of poor people in Indonesia. In addition, the increase in economic growth is reflected in the equitable distribution of public income in the country. Even though there are still many Indonesian people who are not yet prosperous in economic terms. To overcome, it is necessary to have clustering and characteristics of 34 provinces in Indonesia by implementing the Modification Maximum Standard Deviation Reduction (MMSDR) graph clustering algorithm. The data used are indicators of public welfare in 2017 obtained from the Central Statistics Agency. There are 9 indicators of community welfare used in this research. There are four stages in the MMSDR algorithm namely the "MST", "Subdivide", "Biggest Stepping" and "Create Clusters" processes. The results of this study can be seen from the distance between the nodes or between one province and another province produced 22 clusters. From the cluster results obtained using the MMSDR algorithm on welfare data, there are many clusters formed with cluster members formed at most two nodes (province).</p><p> Keywords: MMSDR, Clustering, Welfare of People</p>

Grade functions

Student grade processing using Stata is more reliable than methods like spreadsheets and saves the user timeh, especially when courses are repeated. In this article, I introduce functions that automate some useful grade calculations: the functions curve grades according to combinations of a target grade mean, maximum, standard deviation, and percentile cutoff; convert between numerical grades and letter grades; and convert between 0–100 grades and 0–4 grades (grade point average). The functions can also convert between other grading scales, such as those used in other countries.

The Impact of Vibration on the Accuracy of Digital Surveying Instruments

A digital surveying instrument has a crucial and effective role in civil engineering. These digital surveying instruments have contributed to providing quick and simplified solutions to solve many surveying problems: particularly accuracy, saving time, and effort .Therefore, the main objective of this research is the study of the vibrations effect on digital devices efficiency during the observation process, which occur frequently especially when the devices occupy the bridges during observation or when the occupation of the device is set nearby the railways, as well as in construction sites with heavy equipment movement. Although most digital surveying instruments contain a compensator device, this research find out through the experimental test that the effect of vibration on the accuracy of observation results and the noticed errors may extend to many centimeters. In case of using the digital level devices (SOKKIA SDL-30) under exposure to vibration (up to 20 KHZ/Sec), the average error of elevation was 36.9 mm in 80 m distance and the maximum standard deviation elevation error was 18.26 mm. But in the case of using the reflector-less total station (SOKKIA SET330RK) under exposure to vibration (from 7.5 to 15 KHZ/Sec), the average error of positioning was 79.95 mm in 85 m distance and the maximum standard deviation positioning error was 43.41 mm.

Uncertainty Quantification, Rare Events, and Mission Optimization: Stochastic Variations of Metal Temperature During a Transient

Gas turbines are designed to follow specific missions and the metal temperature is usually predicted with deterministic methods. However, in the real life, the mission is subjected to strong variations which can affect the thermal response of the components. This paper presents a stochastic analysis of the metal temperature variations during a gas turbine transient. A Monte Carlo method (MCM) with meta-model is used to evaluate the probability distribution of the stator disk temperature. The MCM is applied to a series of computational fluid dynamics (CFD) simulations of a stator well, whose geometry is modified according to the deformations predicted during the engine cycle by a coupled thermomechanical analysis of the metal components. It is shown that even considering a narrow band for the stochastic output, ±σ, the transient thermal gradients can be up to two orders of magnitude greater than those obtained with a standard deterministic analysis. Moreover, a small variation in the tail of the input probability density function (PDF), a rare event, can have serious consequences on the uncertainty level of the temperature. Rare events although inevitable they are not usually considered during the design phase. In this paper, it is shown for the first time that is possible to mitigate their effect, minimizing the maximum standard deviation induced by the tail of the input PDF. The mission optimization reduces the maximum standard deviation by 15% and the mean standard deviation of about 12%. The maximum thermal gradients are also reduced by 10%, although this was not the parameter used as the goal in the optimization study.

Uncertainty Quantification, Rare Events and Mission Optimization: Stochastic Variations of Metal Temperature During a Transient

Gas turbines are designed to follow specific missions and the metal temperature is usually predicted with deterministic methods. However, in real life the mission is subjected to strong variations which can affect the thermal response of the components. This paper presents a stochastic analysis of the metal temperature variations during a gas turbine transient. A Monte Carlo Method (MCM) with Meta Model is used to evaluate the probability distribution of the stator disk temperature. The MCM is applied to a series of CFD simulations of a stator well, whose geometry is modified according to the deformations predicted during the engine cycle by a coupled thermo-mechanical analysis of the metal components. It is shown that even considering a narrow band for the stochastic output, +/− σ, the transient thermal gradients can be up to two orders of magnitude greater than those obtained with a standard deterministic analysis. Moreover, a small variation in the tail of the input probability density function, a rare event, can have serious consequences on the uncertainty level of the temperature. Rare events although inevitable they are not usually considered during the design phase. In this paper it is shown for the first time that is possible to mitigate their effect, minimizing the maximum standard deviation induced by the tail of the input PDF. The mission optimization reduces the maximum standard deviation by 15% and the mean standard deviation of about 12%. The maximum thermal gradients are also reduced by 10%, although this was not the parameter used as the goal in the optimisation study.

Spectrophotometric Determination of Color Intensity of Whisky: Collaborative Study

A method for measuring color intensity of whisky was developed to replace the present AOAC method, which has become obsolete. The new method was collaboratively studied by 20 persons. Color intensity of whisky was measured as absorbance by a spectrophotometer, using a 1 cm cell, a bandwidth ≤10 nm, and a wavelength of 525 nm. Water was used as reference. Collaborator results appear acceptable for whisky samples that vary in color intensity from 29 to 374 CIU (absorbance X 1000 = Color Intensity Units (CIU)). The maximum standard deviation and coefficient of variation were 5.8 CIU and 6.68%, respectively. The method has been adopted official first action.