State-of-the-Practice Review on the Field-Curing Methods for Evaluating Strength of Concrete Test Specimen

The purpose of this research was to build up the understanding of the current state –of –the practice for field-curing methods of concrete specimens. Specifically, a comprehensive literature review and questionnaire survey were prepared to identify the selection criteria and details of field-curing methods correspondingly. The comparison of literature data and survey outcomes shows that most transportation agencies use field-cured cylinders followed by the maturity method for the decision on when to open pavement to traffic or remove form/falsework. The most commonly used field method was curing near (or on) the casted concrete in the same manner as concrete item represented. The cylindrical specimens are mostly field cured in insulated boxes such as a cooler or under burlap/insulation near the concrete item. On the other hand, beams are mostly field-cured in a damp sandpit or under burlap/insulation near the concrete item. The information provided in this paper could be used by transportation agencies for determining an appropriate cost-effective field-curing technique which is representative of strength gain of the in-place concrete item.  &nbsp

Structure of a Formal User Model for Construction Information Retrieval

Information science researchers and developers have spent many years addressing the problem of retrieving the exact information needed and using it for analysis purposes. In informationseeking dialogues, the user, i.e. construction project manager or supplier, often asks questions about specific aspects of the tasks they want to perform. But most of the time it is difficult for the software systems to unambiguously understand their overall intentions. The existence of information tunnels (Tannenbaum 2002) aggravates this phenomenon. This study includes a detailed case study of the material management process in the construction industry. Based on this case study, the structure of a formal user model for information retrieval in construction management is proposed. This prototype user model will be incorporated into the system design for construction information management and retrieval. This information retrieval system is a user-centered product based on the development of a user configurable visitor mechanism for managing and retrieving project information without worrying too much about the underlying data structure of the database system. An executable UML model combined with OODB is used to reduce the ambiguity in the user's intentions and to achieve user satisfaction

Granular Partition and Concept Lattice Division Based on Quotient Space

In this paper, we investigate the relationship between the concept lattice and quotient space by granularity. A new framework of knowledge representation - granular quotient space - is constructed and it demonstrates that concept lattice classing is linked to quotient space. The covering of the formal context is firstly given based on this granule, then the granular concept lattice model and its construction are discussed on the sub-context which is formed by the granular classification set. We analyze knowledge reduction and give the description of granular entropy techniques, including some novel formulas. Lastly, a concept lattice constructing algorithm is proposed based on multi-granular feature selection in quotient space. Examples and experiments show that the algorithm can obtain a minimal reduct and is much more efficient than classical incremental concept formation methods

Analysis of Construction Cost and Investment Planning Using Time Series Data

Construction costs and investment planning are the decisions made by construction managers and financial managers. Investment in construction materials, labor, and other miscellaneous should consider their huge costs. For these reasons, this research focused on analyzing construction costs from the point of adopting multivariate cost prediction models in predicting construction cost index (CCI) and other independent variables from September 2021 to December 2022. The United States was selected as the focal country for the study because of its size and influence. Specifically, we used the Statistical Package for Social Sciences (SPSS) software and R-programming applications to forecast the elected variables based on the literature review. These forecasted values were compared to the CCI using Pearson correlations to assess influencing factors. The results indicated that the ARIMA model is the best forecasting model since it has the highest model-fit correlation. Additionally, the number of building permits issued, the consumer price index, the amount of money supply in the country, the producer price index, and the import price index are the influencing factors of investments decisions in short to medium ranges. This result provides insights to managers and cost planners in determining the best model to adopt. The improved accuracies of the influencing factors will help to enhance the control, competitiveness, and capability of futuristic decision-making of the cost of materials and labor in the construction industry

Coherent manipulation of spin wave vector for polarization of photons in an atomic ensemble

We experimentally demonstrate the manipulation of two-orthogonal components of a spin wave in an atomic ensemble. Based on Raman two-photon transition and Larmor spin precession induced by magnetic field pulses, the coherent rotations between the two components of the spin wave is controllably achieved. Successively, the two manipulated spin-wave components are mapped into two orthogonal polarized optical emissions, respectively. By measuring Ramsey fringes of the retrieved optical signals, the \pi/2-pulse fidelity of ~96% is obtained. The presented manipulation scheme can be used to build an arbitrary rotation for qubit operations in quantum information processing based on atomic ensembles

Determining construction method patterns to automate and optimise scheduling – a graph-based approach

Construction projects have been experiencing project delays for decades. As an executive guide to construction activities, construction schedules can mitigate delay risks and are essential to project success. Yet, creating a quality construction schedule is often the outcome of experienced schedulers, and what makes it harder is the fact that historic information including decision reasoning was not documented and disseminated for future use. This study proposes a graph-based method to find the time- and risk-efficient construction method patterns from historic projects to help schedulers improve productivity and accuracy. The method leverages schedule data (including activity names, Work Breakdown Structure, and start and finish date) that were obtained from a Tier-1 contractor for this study. The method was validated for excavation activities. The results indicate that the most time-efficient excavation activities can be done in 0.6% of total project time. The proposed method can help industry professionals standardise scheduling guidelines and automate the generation of construction schedules for critical subtasks

PST: Plant segmentation transformer for 3D point clouds of rapeseed plants at the podding stage

Segmentation of plant point clouds to obtain high-precise morphological traits is essential for plant phenotyping. Although the fast development of deep learning has boosted much research on segmentation of plant point clouds, previous studies mainly focus on the hard voxelization-based or down-sampling-based methods, which are limited to segmenting simple plant organs. Segmentation of complex plant point clouds with a high spatial resolution still remains challenging. In this study, we proposed a deep learning network plant segmentation transformer (PST) to achieve the semantic and instance segmentation of rapeseed plants point clouds acquired by handheld laser scanning (HLS) with the high spatial resolution, which can characterize the tiny siliques as the main traits targeted. PST is composed of: (i) a dynamic voxel feature encoder (DVFE) to aggregate the point features with the raw spatial resolution; (ii) the dual window sets attention blocks to capture the contextual information; and (iii) a dense feature propagation module to obtain the final dense point feature map. The results proved that PST and PST-PointGroup (PG) achieved superior performance in semantic and instance segmentation tasks. For the semantic segmentation, the mean IoU, mean Precision, mean Recall, mean F1-score, and overall accuracy of PST were 93.96%, 97.29%, 96.52%, 96.88%, and 97.07%, achieving an improvement of 7.62%, 3.28%, 4.8%, 4.25%, and 3.88% compared to the second-best state-of-the-art network PAConv. For instance segmentation, PST-PG reached 89.51%, 89.85%, 88.83% and 82.53% in mCov, mWCov, mPerc90, and mRec90, achieving an improvement of 2.93%, 2.21%, 1.99%, and 5.9% compared to the original PG. This study proves that the deep-learning-based point cloud segmentation method has a great potential for resolving dense plant point clouds with complex morphological traits.Comment: 46 pages, 10 figure

Multivariate Regression and Variance in Concrete Curing Methods: Strength Prediction with Experiments

Because concrete strengths and quality are affected by various factors, multivariate regression models are often used to analyze the differences between predicted and target outputs. However, the variableness of a predicted output and how individual input parameters affect prediction reliabilities are still uncertain in practical applications, especially for the prediction of compressive strengths of concrete. This study aims to develop multivariate models for predicting concrete strengths and providing the variance analysis of prediction results by comparisons with experiment outcomes. First, this paper provides an in-depth examination of established variance analysis methods in the context of commonly used multivariate regression models. Then, based on Gaussian process regression, this study melds principal component analysis (PCA), linear discriminant analysis (LDA), and multivariate analysis of variance (MANOVA) to assess the variability in concrete strength prediction using different curing methods. This innovative approach proves effective in evaluating the precision of the correlation and regression models (R-squared values ≥ 0.9049). The comparison between prediction results and experiment outcomes shows that retaining heat in cylinders can make them become too hot and overestimate in-place concrete strength. This study improves the methodologies of regression modeling for variance analysis and improves the reliability of concrete strength prediction. Additionally, the outcomes of this research can help save a substantial amount of financial resources and time that are required to obtain experimental data on the strengths of concrete components

Design and validation of a fiber optic point probe instrument for therapy guidance and monitoring

Abstract in Undetermined ABSTRACT. Optical techniques for tissue diagnostics currently are experiencing tremendous growth in biomedical applications, mainly due to their noninvasive, inexpensive, and real-time functionality. Here, we demonstrate a hand-held fiber optic probe instrument based on fluorescence/reflectance spectroscopy for precise tumor delineation. It is mainly aimed for brain tumor resection guidance with clinical adaptation to minimize the disruption of the standard surgical workflow and is meant as a complement to the state-of-the-art fluorescence surgical microscopy technique. Multiple light sources with fast pulse modulation and detection enable precise quantification of protoporphyrin IX (PpIX), tissue optical properties, and ambient light suppression. Laboratory measurements show the system is insensitive to strong ambient light. Validation measurements of tissue phantoms using nonlinear least squares support vector machines (LS-SVM) regression analysis demonstrate an error of <5% for PpIX concentration ranging from 400 to 1000 nM, even in the presence of large variations in phantom optical properties. The mean error is 3% for reduced scattering coefficient and 5% for blood concentration. Diagnostic precision of 100% was obtained by LS-SVM classification for in vivo skin tumors with topically applied 5-aminolevulinic acid during photodynamic therapy. The probe could easily be generalized to other tissue types and fluorophores for therapy guidance and monitoring

An Experimental Study of the Variability of the Shielding Effectiveness of Circuit Board Shields

The paper examines the variability of the Shielding Effectiveness of board level shields measured in a reverberation chamber at frequencies from 200MHz to 20GHz. Results show that at any particular frequency the Shielding Effectiveness exhibits a typical variability of +/-20dB about the mean value