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In recent years, small-area population prediction methods have been widely applied in urban and regional planning. This study aims to empirically compare two small-area population forecasting methods and explains how to choose prediction results through a case study. This study utilizes the geographically weighted regression (GWR) model and the cohort change rate (CCR) method proposed by Inoue to forecast Wuhan's population. In terms of data requirements and the complexity of the prediction process, GWR necessitates considering more drivers of population change and is more effective for population prediction at the county administrative unit or on smaller scales. The model requires substantial input data and involves integrating statistical and geospatial data. Unlike GWR, the CCR method requires population data for only two statistical years, necessitating a smoothing of population data for each area. Consequently, it is generally unsuitable for predicting populations of small geoadministrative units. With regard to forecasting results, the CCR results suggest that Wuhan's future population will continue to experience relatively rapid growth. In contrast, the GWR results indicate that Wuhan's future population will be more stable. Given China's demographic shifts, urbanization trends, and Wuhan's recent population changes, this study concludes that GWR produces results more consistent with reality. The value of this study lies in using a case study to provide researchers and planning practitioners with a better understanding of GWR and CCR and how to choose prediction results for planning. Practical Applications: The study's findings demonstrate that the cohort change rate (CCR) method proposed by Inoue overestimates the actual population, whereas the geographically weighted regression (GWR) method underestimates it. These population prediction methods may produce different results in other cases due to variations in the situations, as observed in Wuhan, China. In the actual planning project research process, multiple prediction methods are often employed to obtain interval prediction results, the median of which is then used to approximate the actual result. To obtain age-specific population prediction results, only the CCR method may be used for planning projects or research. However, the choice of method may be constrained by data availability, requiring results based on the researcher's specific circumstances. [ABSTRACT FROM AUTHOR]

When projects suffer noncompletion, state highway agencies (SHAs) may trigger liquidated damage (LD) provisions, and at times, contractors may challenge their enforceability. Normally, to be compensated for the costs expected during the delay, states design the LD schedules by associating LD rates to specific contract sizes. A review of the literature found that states varied significantly in the LD schedule designs. However, no studies have explored the relationship between the LD rates and contract sizes (LDR-CS) across states. Exploring this relationship could lead to identifying the underlying model of the relationship, explaining how states vary the LD rates against contract sizes, running comparative analyses of the states' LD schedules, and designing optimal LD schedules, among other potential benefits. To realize these benefits, the objectives of this work were to (1) statistically explore and characterize the LDR-CS relationship, and (2) develop a representative model of the relationship. To achieve these objectives, descriptive, box–whisker, and cluster analyses were performed to characterize the relationship, and regression analysis was utilized to search for the best-fit representation model. The relationship was found to be challenging; it had a unique L-shape, and was modeled successfully only using transformed nonlinear regression. The research results could facilitate performing LD comparative analysis among states, help SHAs assess and build confidence in their LD rates, and help update and optimize LD schedule designs. This work contributes to the body of knowledge with new statistical dimensions to comprehend the LDR-CS relationship, providing tables, charts, and transformed nonlinear models. [ABSTRACT FROM AUTHOR]

Pavement roughness has long been linked to both vehicle fuel efficiency and pavement structural degradation. Perceptions of pavement roughness, however, may vary among users of different sociodemographic features and therefore potentially impact the allocation of resources for highway maintenance and rehabilitation when equity is considered. It is necessary to investigate the major factors that influence the user perception of pavement roughness. Considering the complexity in structure and relationship of field data, this study applied three state-of-the-art machine learning and statistical methods, including a classification tree, a random-parameter ordered-probit model with a random effect (Model 1), and a correlated random-parameter ordered-probit model (Model 2), to the analysis of user perceived roughness. Data were collected from a prior study that conducted in-vehicle tests involving individual user, pavement, and vehicle. The analysis identified more key factors influencing roughness ranking than previous research and accounted for the heterogeneity in individuals and interactions among random parameters. The results indicate that, whereas physical measurements of pavement roughness (e.g., International Roughness Index), visible distresses such as patches and faulting, and joints provided a strong indication of user roughness ranking, other factors (i.e., particular regularly used route, participants' age, income, and gender, number of household infants, and interior vehicle noise levels) were also statistically significant. Two-way group random effects were statistically significant in the data, which should be accounted for in future studies. Results from this study fill an important gap between making accurate prediction and uncovering underlying causality in research of physical infrastructure measurement with user perceptions of infrastructure conditions. [ABSTRACT FROM AUTHOR]

Accurate project duration estimation is crucial for effective scheduling, budgeting, resource allocation, and overall construction management. Leveraging historical data from completed projects is an effective strategy to achieve this. In heavy industrial projects, where scaffolding activities can span from thousands to millions of hours, refining the estimation of scaffolding time is vital during the planning phase. This study undertook the analysis of data from a completed heavy industrial scaffolding project, aiming to propose a methodology and models for predicting future projects durations. The proposed methodology not only aids in improved duration but also contributes to cost estimation, scheduling, and project delivery of similar future endeavors. Commencing with data cleaning and categorizing the data based on activity types, the scatter plots of person-hours versus task weight within each category revealed a linear relationship. Consequently, linear models for each category were developed. Statistical factors such as data size, coefficient of determination, and mean absolute error were then utilized to calculate a score for each model, guiding the model selection process which substituted low score models with a parent category with a higher score. The data analysis and modeling were performed five times to ensure robustness and consistency in the results. On average, the initial models yielded a project duration estimate of only 0.36% higher than the actual duration, while the selected models increased this deviation to 4.14%. The scoring and selection process enhances estimation accuracy while maintaining proximity to actual project durations. This research makes three significant contributions: (1) introducing a categorical linear regression approach for scaffolding activity duration prediction, (2) presenting a novel normalization and scoring method that scores models based on statistical factors, and (3) implementing a practical model selection process to substitute weaker models with stronger ones, ultimately strengthening the reliability of activity and project duration predictions. [ABSTRACT FROM AUTHOR]

Variables that can be considered in experimental investigations of internal instability of earth dam materials are often bounded by practicality and feasibility constraints. To overcome these shortcomings, a total of 164 previous published experimental results were compiled to study statistically the relative significance of vertical effective stress, grain size ratio (D15/d85), fines content, coefficient of uniformity (Cu), and specimen length in the mechanism of internal instability. The present statistical results agreed with previous experimental findings on the combined dominant roles played by fines content and stress in the internal instability. However, the stress reduction factor was found to be inversely correlated with the vertical effective stress, which was not reported previously. The scale effect of specimen size has significant positive correlations with both the stress reduction factor and critical hydraulic gradient, whereas the influences of Cu and D15/d85 were minimal, in contrast to previous experimental findings. An improved theoretical hydromechanical envelope for predicting internal instability of cohesionless soil in the space of stress-gradient–stress reduction factor was proposed in this study. The unstable envelope moved with increasing applied hydraulic gradient until the state of material touching the envelope. [ABSTRACT FROM AUTHOR]

Cost overruns (COs) in public sector social housing programs (PSSHPs) will continue unabated without immediate intervention to resolve the ad hoc amalgamation of root cause ontologies. This study applied a fuzzy synthetic evaluation (FSE) multicriteria decision support model to evaluate and prioritize the overall cost overrun risk in PSSHPs. In a Caribbean Small Island Developing State (SIDS) context, a questionnaire survey measuring 41 perceived critical risk factors (CRFs) was used to derive the risk impact and normalized impact values from 150 professional participants. Twenty-two normalized CRFs and four principal risk groups' (PRGs) membership functions were determined using fuzzy synthetic knowledge-based inference procedures. Politically linked contractors and intentionally inferior building contracts caused most PSSHP cost overruns. The FSE risk model reveals that political PRG is the major root cause of COs, followed by socioeconomic, technical, and psychological PRGs. The study found that the overall risk level (ORL) of PSSHPs is high, meaning that government investment in public housing is a high-risk endeavor and ill-informed choices could lead to "wicked" social problems. Defuzzifying cost risks provided deeper insights into the nature and scope of the risk and related management implications that lead to unsustainable decision-making practices and provide opportunities for adequate control strategies to maintain economic viability. This research evidenced the fusion of different CO ontologies and raises awareness of the hidden political risk factors that should be prioritized to achieve sustainable PSSHP decision-making practices. This study adds to the PSSHP's cost overrun knowledge by merging known root causes and criticalities into a practical risk model that gives empirical risk values at several tiers, from individual critical factors to root causes and the overall risk assessment. Many theories and ontologies explain CO causes (e.g., political root causes grounded on agency theory and relativism ontology, psychological root causes based on prospect theory and relativism ontology, and economic root causes based on transaction cost theory and realism ontology). Subjectivity and a growing divide between top researchers and practitioners result from the ad hoc amalgamation of these ontologies. The study expands the theoretical foundations of CO research by tackling the ontological issues through a structured, methodical grouping of root causes based on their ontologies. The defuzzification method applied in this research eliminated subjectivity and bias in SIDS risk assessment. Therefore, construction experts can use this research to identify strategically hidden risks, analyze their effects on the project and society, and design control strategies to limit their economic impacts. This pragmatic tool can be used in the tendering and contract award processes and across the supply and logistic chain to optimize project cost performance and benefit society with value-driven, circular economy–friendly social projects. [ABSTRACT FROM AUTHOR]

Some recent studies have used actual water end-use data to inform the peak demand design of plumbing systems in residential buildings, addressing the problem of overestimation in many long-standing plumbing codes and standards. Vast amounts of fixture-specific data from each household are required to determine the frequency each fixture is used during peak water consumption periods (fixture-use probability). However, obtaining such data can be difficult, and the processing is costly and time-consuming. The current study presents a new approach for determining the fixture-use probability for peak water demand design of premise plumbing systems. A stochastic water demand model is developed using only the high-level statistical information from water end-use studies presented in the public domain, offsetting the need for the original water end-use data sets. The stochastic model is then used to form easy-to-use formulas for determining the probability of use for various fixture groups, which consider both the number of apartments and building occupancy. The approach is validated by comparing the estimated peak demand values with the corresponding values determined from actual water consumption observations in three Australian residential apartment buildings and one mixed-use building. The new approach enables a much more accurate estimation of the peak demand compared with the conventional approach suggested in the current Australian plumbing standard. The proposed approach can be used by researchers and practitioners in other countries to determine their region-specific fixture-use probability values for more accurate peak demand estimation, contributing to improved premise plumbing system design. [ABSTRACT FROM AUTHOR]

A novel time domain method is proposed to estimate the time-varying statistics of nonstationary random pressure load from structural strain response samples. Orthogonal polynomials and Karhunen–Loève expansion are adopted to represent the spatial distribution of the stochastic pressure load and the random structural strain data, respectively, which transform the random pressure load identification problem to a problem of estimating a few deterministic time-varying coefficients. Numerical simulation on a plate structure subjected to nonstationary random fluctuating load is conducted to validate the proposed method, and the influence of the load's correlation length on the identification accuracy is also discussed. To show the application perspective of the proposed method, the nonstationary gust-induced random pressure load on a wing structure is reconstructed from structural strain data. Some practical aspects, such as the number of response samples, the level of noise in the strain response, the order of orthogonal polynomial used, and the regularization method used, on the performance of the load identification method are also discussed. [ABSTRACT FROM AUTHOR]

The existence of weak interlayers (WIs) in slopes results in slope failure with multiple possibilities. The most likely failure modes include sliding along a curved slip surface, linear slip surface, and composited slip surface. As such, WIs are highly sensitive to slope stability results, and dense slice division is necessary for limit-equilibrium (LE) stability analysis of the slopes with WIs. However, considering the stability of a complex slope dependent on the dense slice division, the traditional rigorous LE methods have the problem of nonconvergence calculation for possible interference from unreasonable interslice moment results. Thus, this study established a novel LE method based on increments in interslice forces (ISFs). The proposed method had good convergence with the introduction of the overall moment equilibrium condition and rigorously satisfied the end boundary conditions of both normal and shear ISFs. Thereafter, the reliability of the proposed method was verified by comparing and analyzing several examples. Furthermore, stability charts for slopes with WIs were realized based on a large amount of statistical data on typical occurrence and strength parameters to provide guidance for slope design. [ABSTRACT FROM AUTHOR]

This study presents landslide susceptibility mapping by using coupled GIS statistical analysis and interferometry synthetic aperture radar (InSAR) data applied to a road corridor in West Sulawesi, Indonesia. Landslide-contributing factors in the road corridor including slope angle, distance to drainage, lithology, distance to road, distance to tectonic fault, and rainfall, were selected based on knowledge of landslide triggering mechanisms, numerically analyzed with the multicriteria decision of the analytical hierarchy process (AHP), and controlled with the variance inflation factor (VIF) in the multicollinearity analysis. Then, the data associated with slope angle, the distance of the road to the slope, and the distance of the road to natural drainage were obtained from the digital elevation model (DEM) of the road corridor. In addition, the data of the lithological type of the area in the road corridor and the distance of the road to the tectonic faults were derived from a geology map, whereas the data of 10 years of daily rainfall were collected from three rainfall stations in the proximity of the road corridor and employed as rainfall data. Causal relations between those contributing factors and landslide occurrences were identified, statistically analyzed with the AHP, and numerically converted as landslide ratings into a GIS statistics–based landslide susceptibility map (LSM). In a parallel way, the interferogram of Sentinel-1's synthetic aperture radar images of the road corridor was also generated to observe the ground movement rate. The observed ground movement rates were then numerically converted into landslide ratings in the InSAR-based LSM. By overlaying these maps, a coupled GIS statistics and InSAR-based LSM of the road corridor was generated. To validate its accuracy, the landslide density index (R -index) was calculated, in which the highly and very highly susceptible zones in the LSM were compared with the actual sliding areas in the landslide inventory data. In comparison with the GIS-AHP-based LSM from a prior study, which had an R -index of 91.03%, the GIS statistical analysis and InSAR-based LSM's R -index was determined to be 97.09%, suggesting high accuracy and improved prediction. The results indicated that creating a landslide susceptibility map using GIS statistical analysis and InSAR would be beneficial in reducing the risk of landslides for road infrastructure. [ABSTRACT FROM AUTHOR]

The propagation of construction vibration to nearby buildings is a highly complex process. Real-time vibration monitoring is a straightforward means to minimize vibration impact on nearby buildings. Although traditional monitoring systems are typically designed for structural safety, safeguarding the normal operation of vibration-sensitive ultraprecision equipment represents an emerging but increasing need. By developing a tailor-made vibration monitoring system for a hospital, this paper presents the monitoring and assessment of vibration impact on ultraprecision equipment throughout the whole construction period of a hospital expansion project, which, to the best of the authors' knowledge, has not been reported in the literature. The developed system could monitor construction-induced vibration levels, assess the impact on ultraprecision equipment, and issue alarm signals, if necessary, in a continuous and real-time manner. The application of the developed system in a hospital expansion project together with vibration mitigation measures guaranteed the normal operation of ultraprecision equipment throughout the whole construction period. Recorded vibration data were processed and analyzed to evaluate the vibration impact in different construction stages. The empirical prediction formulas for rotary minipiling and demolition work based on statistical analysis results can provide valuable guidance on corresponding construction operations to mitigate excessive environmental impact. [ABSTRACT FROM AUTHOR]

Portland cement production is an energy-intensive process, and more sustainable substitutes are needed, e.g., alkali-activated binders originated from industrial wastes. Thus, this paper analyzes the combination of sugarcane bagasse ash (SCBA) and hydrated eggshell lime (HEL) as precursors for an alkali-activated binary system, a combination, to our best knowledge, not studied in past research. The mechanical and microstructural behavior of the SCBA-HEL alkali-activated pastes has been discussed through unconfined strength tests, and x-ray fluorescence, x-ray diffraction (XRD), Fourier transform infrared spectroscopy, and scanning electron microscopy (SEM) and energy-dispersive detector (EDS) microstructural analysis. In addition, an analysis of variance was applied to investigate the impact of a three-factor combination, i.e., SCBA/HEL ratio, NaOH concentration, and water/binder ratio (W/B), on the paste's compressive strength. The highest compressive strength is associated with 80% of SCBA and 20% of HEL (ratio equals 4), 1 M molarity, and W/B relation of 0.8 (2.61% of Na2O). A C─ (N)─ A─ S─ H gel is observed in the form of an amorphous hump through the XRD pattern. SEM images show that the material synthesized from alkali-activation has a cementing effect, with a structure less dense and more porous than that of conventional cementing materials. The EDS display areas are rich in Ca, Si, Na, and Al. The bands found for the alkali-activated paste are consistent with vibrations characteristic of C─ A─ S─ H and N─ A─ S─ H gels. [ABSTRACT FROM AUTHOR]

The existing equations for the discharge coefficient of Piano key weirs (PKWs) use a limited range of experimental data, which means that they are inappropriate for wide parametric ranges that might lead to significant errors. This study aimed to propose a reformed empirical equation using a wide range of data points gathered from previous experimental studies. Further, the appropriateness to use the existing equations for the collected data points, and the related errors, were investigated in detail using graphical and statistical analyses. The proposed equation predicted the discharge coefficients with <5% absolute errors for 83.5% data points and with <10% absolute errors for 100% data points, and the mean absolute error was 2.9%. Such variations may be attributed to the differences in experimental conditions that exist among the previous studies. The correlation indices were higher for the proposed equation as compared to the same for the existing equations, whereas the error indices were lowest for the proposed one. For some very specific parametric ranges, the existing equations still hold better accuracy. Overall, the proposed equation can precisely estimate the discharge coefficient of the basic geometry of Type-A PKWs for a wide parametric range and will be handy in the hydraulic design of such PKWs. [ABSTRACT FROM AUTHOR]

Existing approaches for the estimation of shaft and base resistances of drilled, cast-in-place piles have been compared on the basis of data collected from local geotechnical consultants, from load tests performed as part of foundation projects in Israel over the last decades. Three typical Israeli foundation soils were considered—clay, sand, and kurkar (partially and variably cemented sand). Data from 18 load tests at 13 sites were used for the study. Existing correlations were first used to estimate relevant soil properties from results of in-situ tests [usually standard penetration tests (SPT) or vane shear tests] performed during the project site investigations. These properties were then inserted in existing resistance estimation expressions. The resulting estimated resistances were compared to resistances indicated in the loading tests. A statistical analysis was used to choose the most reliable correlations and resistance estimation expressions for piles embedded in the various soils. [ABSTRACT FROM AUTHOR]

Numerous theories have been presented over the years for quantifying bedload transport in streams and rivers. These theories have been derived using physical processes, statistical methods, and semiempirical and empirical methods and rely on a variety of hydraulic, boundary, and bed material properties. Most of these equations represent instantaneous bedload transport related to temporal hydraulic and bed conditions and are not well-suited to estimate long-term average bedload over a broad range of channels. Using a pure empirical analysis, the author has developed a dimensionally homogeneous regime channel bedload transport equation not requiring bedload measurements for calibration or verification. The new equation was developed using 45 data sets and verified using 29 additional data sets encompassing a very broad range of channel conditions and was found to rely solely on stream power and bank-full stream power or, for practical applications, flow and bank-full flow. Statistical metrics indicated better performance by the new equation than by other bedload transport equations evaluated using the same source data and statistical methods. [ABSTRACT FROM AUTHOR]

Several approximations are used in deriving the National Oceanic and Atmospheric Administration (NOAA) Atlas 14 depth-duration-frequency (DDF) functions from annual maximum series (AMS) of rainfall amounts over various durations. Analyses reported in this paper show that the return periods in Atlas 14 DDF functions for Florida are likely overestimated at about 10% of the base stations for durations of 15 min, 1 h, and 24 h. This relatively high incidence of return-period overestimation is also evidenced by measurements at several stations having very high Atlas 14-estimated return periods. Comparison of the Atlas 14 generalized extreme value (GEV) distributions of rainfall with sample distributions derived from AMS data show agreement at only 68%, 64%, and 30% of all base stations for durations of 15 min, 1 h, and 24 h, respectively. It is concluded that the expected rainfall amounts for given durations are not always reliable over the Atlas 14 range of return periods. [ABSTRACT FROM AUTHOR]

Well-documented conditions of the country's subway systems suggest that the development of a management strategy for aging infrastructure is an important asset management challenge. The purpose of this work is to address this challenge by developing a statistical data analysis approach for forecasting the aging effect on the time-dependent deterioration rate of an identified trackway cluster. The infrastructure database is the New York City subway system and the trackway that supports the delivery of service. While much focus has been given to the steel rail subcomponent of the trackway, little has been done in the way of statistical analysis on the low-level nonconformances in the support structure of those rails—namely, the invert, ties, fasteners, and plates. Though detectable, these conditions pose no risk to either safety or the serviceability of that asset, yet they do provide a certain degree of awareness that an entirely intuitive lifecycle deterioration process is underway. This paper illustrates the development of a statistical data analysis methodology using available trackway data sets for forecasting the aging effect on the system performance. [ABSTRACT FROM AUTHOR]

The peak load on a borehole wall is a key parameter in calculating the blasting failure range and in numerical simulations of nonfluid–structure coupling. In this study, the characteristics of the peak load in water-coupling blasting are studied theoretically and numerically. First, the interaction between a waterborne shock wave and a borehole wall is analyzed theoretically to reveal the main factors in the peak load on the borehole wall. Then, based on the theoretical calculations and the principle of dimensional homogeneity, a calculation model for the peak load is determined, and a numerical simulation of fluid–structure coupling is carried out to obtain the peak load under different conditions in two types of water-coupling blasting. The theoretical and numerical results are compared, and a correction coefficient is introduced to optimize the theoretical model. The results showed that the peak load increases approximately as a power function with increasing rock wave impedance and decreases approximately as a power function with increasing decoupling coefficient. Furthermore, it is concluded from statistical analysis that the correction coefficient is linearly proportional to the decoupling coefficient. In summary, a method for calculating the peak load on a borehole in water-coupling blasting is proposed, and it is verified against existing stress test data from water-coupling blasting. [ABSTRACT FROM AUTHOR]

Flood frequency estimation in the United States has been primarily driven by statistical analysis for the past one hundred years. For ungauged locations, the regionalized equations are developed to provide annual exceedance probability discharge estimates. These equations establish a relationship between discharge quantiles and drainage area through statistical regression. Predictors consisting of catchment physical properties can also be included based on minimization of residuals. For Iowa, only one-third of developed regional equations use a climatic parameter (i.e., precipitation), which is a critical driver of hydrologic processes. The authors explore an alternative approach to regional flood quantile estimation analysis by analyzing the performance of the Iowa Flood Center's physically based, calibration-free, and spatially distributed Hillslope-Link Model (HLM). They conducted continuous simulations for a 40-year period across the state of Iowa. Compared to the observations, the HLM can accurately capture the observed magnitude of annual maximum discharge, making it a viable physically based alternative to regional regression. In the study, regional flood quantile estimation is conducted at 445 ungauged communities to compare flood frequency estimates using HLM simulations with regionally developed regression equations. The results show similar discharge values between simulation and regional regression models for all annual exceedance probability where regional equations contain rainfall as a predictor. However, in areas where regional equations are only based on catchment properties, regional regression equations overestimated discharge for all quantiles. These results highlight inconsistencies in current regional regression equations for flood quantile estimates in Iowa and provide support for the reevaluation of flood quantile estimates with physically based hydrologic models. [ABSTRACT FROM AUTHOR]

The statistical law on mechanical parameters of rock mass with size is the key to the study of the size effect. Since the existing statistical methods of the size effect only consider the continuity of the size but not the difference between the samples of each size, a new statistical method that considers single size and multisize is proposed. Based on the proposed method, the true representative elementary volume (TREV) value is used to determine the size effect stability threshold. Combined with the analysis of the three major size effect theories, the unified size effect law model and variation index k are proposed. The results are validated by statistical test data on the uniaxial compressive strength (UCS) obtained in realistic failure process analysis (RFPA) numerical test and from other works, which proves that the proposed method can reflect the variation law of UCS value with size and provide a basis for theoretical research on the size effect in mechanical behavior. [ABSTRACT FROM AUTHOR]

The construction sector is one of the most relevant to the world economy. Within it, the most used material is concrete given its excellent construction characteristics. However, this material suffers from different issues that can severely affect its performance and thus a construction project's productivity. Of these issues, cracking is one of the most common and significant. It can affect the three most important aspects of construction: cost, deadline, and quality. Although its importance is recognized within the construction industry, to this day no clear vision exists as to the specific impact that cracking can have on a project. Consequently, this study aimed to determine that impact on mining, electrical, and civil infrastructure construction projects through a statistical analysis of 13 completed projects that suffered cracking. To obtain this information, companies involved in the construction of those projects in Chile were contacted. Additionally, this statistical analysis was complemented by a survey targeting professionals in the field who have dealt with cracking situations, with the objective of comparing the statistical results with the personal/professional survey responses. Analysis of project records indicated that concrete cracks represented a low percentage of total construction cost. This correlated with the surveyed professionals' perceptions; however, the lack of proper recordkeeping by construction companies creates annual losses of several million in rehabilitation work, besides possibly hiding the real impact of cracking, creating an iceberg effect. [ABSTRACT FROM AUTHOR]

Uncertainty is the general state of modern project scheduling, and fuzzy numbers are the main theme to resolve uncertain project scheduling problems. Traditional fuzzy numbers can represent uncertain information values of construction projects, but cannot reflect the accuracy and reliability of such information. The Z -number can express the uncertain construction project information reasonably via the first component, and use the second component to describe objectively the accuracy and reliability of information values, thus effectively overcoming the existing disadvantages of traditional fuzzy numbers. This study used historical statistical data and expert opinions to construct the Z -number, which effectively overcomes the drawbacks of conventional fuzzy numbers in disposing the uncertain activity information. A Z -number quantization scheme based on the fuzzy expected value and agreement index algorithm effectively eliminates a large amount of information loss caused by the quantization or defuzzification of the fuzzy number, and achieves the evolution and optimization from Z -numbers to AI fuzzy numbers. The objectivity of the research was ensured by fully considering the probability of completion of the activity and the risk preference of the decision makers. Numerical experimental results verified the effectiveness of the proposed method. Taken together, this study provides a new framework for exploring fuzzy logic–based solutions to the uncertain project scheduling problem, and fills the gap of Z -number-based quantitative expression of activity information in uncertain project scheduling. [ABSTRACT FROM AUTHOR]

Gradation information can be really useful when evaluating the performance of pavement, especially when the local climate condition or traffic volume have special requirements on gradations of asphalt pavement. However, due to poor management or long construction time, it may be difficult to accurately acquire the asphalt gradation type on site. To solve this problem, this study aimed to analyze the grading characteristics of both asphalt concrete (AC) and stone matrix asphalt (SMA) mixtures through statistics and to propose a quantitative indicator that could distinguish them rapidly. Methodologically, passing rate difference (PRD) between two adjacent sieve sizes was used to characterize the gradation and to distinguish AC from SMA. Five sieve sizes were considered in this study, namely, 16 mm, 13.2 mm, 9.5 mm, 4.75 mm, and 2.36 mm, and four PRDs were computed and analyzed. A total of 854 sets of gradation data were collected from 437 papers. To study the influence of nominal maximum particle size (NMPS), the data were further categorized into three types, namely, gradation data with NMPS of 16 mm, 13 mm, and regardless of NMPS. As a result, in all cases of NMPS, critical PRDs were determined to distinguish between SMA and AC mixtures. Moreover, when a critical PRD was determined, for a given gradation, the confidence level to correctly identify an AC gradation was generally higher regardless of NMPS. [ABSTRACT FROM AUTHOR]

Corrosion failure generally results in pipeline leakages during oil production. This paper systematically investigated the pipeline failure events in an oil treatment station of Tahe oilfield in the last 20 years were to identify the key factors causing localized corrosion of pipeline steel. Obviously, the failure events propagated along the spatial locations of transportation and there was a delay of several months from the oil pipelines to the water pipelines. Intensive failure of oil pipelines in 2012 deteriorated the water chemistry of the production fluids in the downstream pipelines. The spatial transmission of pipeline failures was attributed to the possible ingress of oxygen or other aggressive species during the frequent leakage and maintenance of the pipelines, locally damaging the integrity of the preformed corrosion product layers on pipeline steel. This understanding of pipeline failure based on a historical analysis provides guidance for future pipeline design and maintenance work. [ABSTRACT FROM AUTHOR]

In earthquake engineering, seismic ground motions are most often modelled as a nonstationary Gaussian process. A few studies indicated that seismic ground motions should be treated as a nonstationary non-Gaussian process, by showing that the kurtosis coefficient of the historical ground motion records is much greater than three. These findings and conclusions are queried in the present study, which analyzes a large number of historical ground motion records. Our results indicate that while the mixture marginal distribution of the acceleration of the records is non-Gaussian with a heavy distribution tail, the mixture marginal distribution of the standardized record, defined by the time-varying record to its standard deviation, is only mildly non-Gaussian. We point out that the mixture marginal distribution of a nonstationary Gaussian process may not be Gaussian. The implication of these observations in simulating records is explained. The sampled nonstationary Gaussian/non-Gaussian records are used to compare the responses of single-degree-of-freedom systems. The results indicate that the error introduced by adopting the Gaussian assumption is small, suggesting that the ground motions could be assumed to be a nonstationary Gaussian process with sufficient accuracy, especially if structures that are not very stiff are considered. [ABSTRACT FROM AUTHOR]

Significant research has been conducted on the practice of bridge asset management (BAM). Although bridge site inspections yield observations on current conditions, their usefulness in a bridge asset management strategy requires a sound historical and statistical basis. Therefore, the present practice of bridge site inspection can only support a reactive mode in bridge asset life cycle management and is raising a critical need for a risk analysis methodology that will effectively support decision making in a forecasting mode. This research addressed that challenge by employing a series of relatively simple, well-established statistical techniques as a precursor to a forecasting methodology. A knowledge-based risk assessment method (RAM) is recommended for optimizing BAM of the New York City Department of Transportation (NYCDOT) bridge database. The limitations of the current practice of bridge condition rating are reactive in nature. Reactive approaches do not provide analytical tools for forecasting the aging effect on the time-dependent bridge infrastructure deterioration for the prioritization of BAM. The development of statistical data analysis can be done by using the aging effect on the corrosion rate as a health monitoring parameter of the girder. The results of this research provide bridge asset managers with a statistical data-driven analysis approach to the time-dependent aging effect as a decision support tool for preemptive prioritization and a prespecified vulnerability level in BAM planning. The infrastructure base selected for this study was 92 steel girder bridges with concrete decks (single-span/multispan) managed by NYCDOT, which were clustered into eight major age groups. There were multiple primary member degradation indicators, e.g., corrosion, section loss, cracks, and others. This research analyzed the corrosion rate of primary members. It illustrated the application of statistical data analysis for the development and application of RAM for forecasting the effect of aging on the corrosion rate as a structural degradation indicator for the preemptive planning of a BAM strategy. [ABSTRACT FROM AUTHOR]

Recent studies have provided explicit analytical equations that can be used to quantify directly the hydrologic performance statistics of green roofs, such as runoff-reduction ratios. These equations were obtained based on simplified representations of the hydrologic and hydraulic processes occurring on and inside green roofs, as well as stochastic models describing local rainfall characteristics. To simplify derivations, these studies considered only saturation-excess runoff and neglected infiltration-excess runoff that may be generated from green roofs. We develop a method for considering both saturation- and infiltration-excess runoff; the proposed analytical equations can be used to directly quantify the performance statistics of any type of green roof. Systematic comparisons of analytical and numerical simulation results were also conducted to demonstrate the accuracy of the analytical equations. As an alternative to numerical simulations, the analytical equations can be used by engineers to more conveniently quantify the performances of alternative green roof configurations. [ABSTRACT FROM AUTHOR]

An asymptotic approximation methodology for solving standard random eigenvalue problems is generalized herein to account for structural systems with singular random parameter matrices. In this regard, resorting to the concept of the Moore–Penrose matrix inverse and generalizing expressions for the rate of change of the eigenvalues, novel closed-form expressions are derived for the joint moments of the system natural frequencies. Two indicative examples pertaining to multiple-degree-of-freedom structural systems are considered for demonstrating the reliability of the methodology. Comparisons with pertinent Monte Carlo simulation data are included as well. [ABSTRACT FROM AUTHOR]

This paper presents the results of statistical analyses on a database comprising 592 deep excavations in Shanghai soft clays from 1995 to 2018. First, the general trends and patterns over time are outlined for seven different excavation methods and seven types of earth-supporting systems. Then, the major design factors are characterized in a statistical manner. Based on analyses of the collected data, the frequency distributions of δhm/He data (δhm = maximum lateral wall displacement; He = final excavation depth) corresponding to each excavation method and each earth-supporting system are presented; furthermore, the correlations of δhm/He with the major design factors are explored. In this new database, 90% of δhm/He data featured magnitudes no more than 1.0%, which frequently lie within 0.10%–0.55%, indicating better performance than other databases worldwide. Via extensive comparisons, both the optimum construction method corresponding to each type of earth-supporting system and the optimum earth-supporting system corresponding to each excavation method for controlling excavation performance were determined. Last, the major design and construction defects as well as natural hazards pertaining to abnormally large δhm/He=1.01−7.27% tracked at 23 individual excavations are identified and summarized. These include excessive excavation duration, long wall exposure lengths, retaining walls toed in soft muddy clays, overexcavation at deep levels, surcharges behind retaining walls, inadequate supporting system stiffness, and intense rainfall; among these factors, walls toed in soft muddy strata are shown to be detrimental to the performance of excavations. For a deep excavation that is to be carried out in sensitive clayey strata, safety redundancy can be enhanced substantially by accounting for the degradation of soil strengths due to construction disturbance and/or rainfall infiltration in design. The findings and lessons from this study will be useful to professionals worldwide in efficiently achieving cost-effective designs and/or making reliable evaluations regarding the performance of deep excavations in soft clays. [ABSTRACT FROM AUTHOR]

An experimental and statistical study on the impact resistance of ultrahigh-performance concrete (UHPC) is presented. The impact resistance was evaluated by conducting the drop-weight impact test with a hammer of 10 kg following ACI Committee 544 (ACI 544.9R-17). Three mixture types, namely ordinary concrete, nonfibrous UHPC matrix, and UHPC were designed, and each type was prepared with 40 cylindrical specimens with dimensions of Φ150×63.5 mm for impact test. The commonly used distribution functions for material properties (normal distribution and Weibull distribution) were selected to examine the test data, and the goodness of fit of these two distributions was evaluated using a Anderson-Darling test. Results indicate that the designed UHPC endured an average of 623 drops of impact weight before the first crack, while the ordinary concrete and nonfibrous UHPC matrix showed the first crack only after 26 drops and 17 drops, respectively. The higher-strength nonfibrous UHPC matrix broke with a fewer number of drops under impact load than the ordinary concrete (with coarse aggregate) due to the greater brittleness of the UHPC matrix. Both the normal and Weibull distribution can describe the impact resistance of ordinary concrete, while the nonfibrous UHPC matrix and UHPC showed poor fitness to either the normal or Weibull distribution. Statistical analysis results indicate large scatters in the impact resistance of UHPC, and tests with small sample size may result in unreliable judgment. In this paper a baseline of UHPC drop-impact resistance to inspire proper qualification of UHPC members designed to withstand impact loading is provided. [ABSTRACT FROM AUTHOR]

Concrete highway bridge remaining service life has been at the forefront of bridge owners' concerns for several years in terms of growing bridge maintenance needs. Distresses in concrete bridge components occur due to physical, chemical, and mechanical factors that manifest as observable symptoms of deterioration. Deterioration models realistically link the observable defects to the several explanatory variables affecting the deterioration that aid in the planning of appropriate corrective actions. Only a limited number of explanatory variables have been considered in the published literature in the development of bridge component deterioration models. This study investigates the effect of multiple relevant explanatory variables affecting the bridge component condition including aging, loads and environmental conditions, and design characteristics using a multivariate regression analysis approach. Deterioration models for both superstructure and substructure of reinforced and prestressed concrete bridges are developed and validated in the present study. The proposed models will aid the bridge engineers to predict the concrete bridge component conditions based on bridge characteristics for planning preventive maintenance. [ABSTRACT FROM AUTHOR]

Freight train accidents can damage infrastructure and rolling stock, disrupt operations, and possibly cause casualties and harm the environment. Understanding accident risks associated with major accident causes is an important step toward developing and prioritizing effective accident prevention strategies. This paper developed a negative binomial regression model to estimate freight-train derailment frequency on Class I railroad mainlines, accounting for derailment accident cause, traffic exposure, railroad, and season. The primary focus is to quantitatively measure the seasonal effect on freight-train derailment frequencies given traffic exposure. For model illustration, the analysis focused on three common derailment causes on freight railroads: broken rails, broken wheels, and track buckling, using the empirical Federal Railroad Administration (FRA)-reportable freight railroad derailment data on mainlines gathered between 2000 and 2016. The modeling results show that it tends to have high derailment rates in winter due to broken rails and broken wheels (double that of summer), whereas summer has the highest likelihood of buckling-caused derailment of all of the seasons (e.g., 6 times that of spring and 10 times that of fall). These analytical results can contribute to the risk-based optimization of rail and wheel inspection frequency. The statistical modeling methodology developed in this paper can be adapted to other types of train accidents or accident causes, ultimately supporting the optimal allocation of train safety improvement resources. [ABSTRACT FROM AUTHOR]

Determining a reasonable project duration is one of the most critical activities required by project owner agencies for successful project letting and delivery. Most owner agencies, specifically in the highway sector, mainly rely on schedulers' judgment and experience in determining the sequence of construction activities to estimate the required amount of time of a project. A vast amount of historical project performance data available in owner agencies' databases provide rich and reliable resources that can significantly improve the current process to produce a consistent and repeatable quality of construction logic determination. This study proposes a novel data-driven process model utilizing pattern mining, statistical analysis, and network analysis techniques that can detect pairwise logical relationships among construction activities (e.g., Start-Start and Finish-Start) and develop knowledge networks of as-built construction sequence patterns to improve the scheduling process. Three algorithms are proposed to apply the knowledge networks to sequencing a new project: finding immediate predecessors and successors of an activity or ordering a given set of activities. Ten years of historical project data obtained from a state department of transportation were used in this research. A case study reveals that the process model developed in this study can successfully build the most reasonable construction sequences of a highway project, which can significantly improve the scheduling and contract time determination process. [ABSTRACT FROM AUTHOR]

This study investigated uncoupled crosswind and alongwind responses of base-isolated tall buildings under nonstationary wind excitations. The base isolation system has hysteretic restoring force characteristics represented in the Bouc–Wen model, while the upper building is modeled as a linear elastic multiple-degrees-of-freedom shear building. The nonstationary story wind forces are modeled in terms of evolutionary power spectra. Response time-history analysis was performed to characterize the response statistics including mean, standard deviation (STD), and extreme value. This work included an analytical solution of nonstationary time-varying response statistics. The time-varying mean of alongwind base displacement is determined from the solution of the state-space equation. The time-varying STDs of both alongwind and crosswind responses were estimated from the statistical linearization with Gaussian assumption combined with evolutionary spectra analysis. The numerical examples show that the transient effect of nonstationary excitations declines when yielding becomes significant, which leads to an increase in system damping. In the case of alongwind response, the transient effect on the time-varying mean of base displacement is more significant. This study not only presents an effective analytical approach but also sheds new insights toward improved understanding of inelastic responses of base-isolated tall buildings under nonstationary winds, contributing to a safer and more economical design of base-isolated tall buildings. [ABSTRACT FROM AUTHOR]

In this study, analysis of real-scale field pile loading test results on 20 fully instrumented steel pipe test piles will be carried out, to propose a modified empirical formula to estimate the bearing capacity of the steel pipe prebored and precast pile. In addition, to overcome the limited consideration of the surrounding soil capacity of the existing formula, the proposed formula will be based on an expanded standard penetration test (SPT) N value that will consider the realistic conditions of the ground. The formula will be proposed based on a statistical approach to the data points from the field pile loading test, to ensure safe engineering practice and find a reliable formula. The statistical analysis of the data points indicated that the existing formula underestimated the bearing capacity of the steel pipe prebored and precast pile. The proposed formula estimated a 20% higher pile tip bearing capacity and shaft resistance compared with the existing formula. The accuracy and the stability of the proposed formula will be verified by comparing the estimated results with additional field test data. The verification process showed that the proposed formula gave more realistic results and did not exceed the actual bearing capacity of the test piles. [ABSTRACT FROM AUTHOR]

This paper presents a novel Bayesian machine-learning approach for unsupervised and simultaneous soil stratigraphic interpretation of cone penetration test (CPT) soundings at the site scale. The proposed approach interprets numerous CPT soundings in a joint manner, and it leverages the statistical similarity of the measured sounding data in feature space (i.e., the Robertson chart) and the spatial constraints induced from spatial correlations of the sounding data both vertically along a single CPT sounding and horizontally across multiple soundings in physical space. The mathematical core of the proposed approach consists of the following two parts: (1) a quasi-3D (or 3D axial-symmetric) hidden Markov random field (HMRF) model describing both the statistical and spatial patterns of the CPT soundings; and (2) a model inference process, in which the statistical and spatial patterns are extracted from the dataset using a Bayesian unsupervised learning algorithm. The joint interpretation strategy of the proposed approach facilitates the use of rich statistical information and spatial constraints contained in an ensemble of CPT soundings to enhance the accuracy and consistency of a stratigraphic interpretation at the site scale. The proposed approach has been tested in a real-world case consisting of 44 CPT soundings collected from a geotechnical investigation site. The interpretation results show that the proposed approach can extract the soil spatial and statistical patterns from multiple CPT soundings and significantly increase the accuracy and consistency of the soil stratigraphic interpretation results. [ABSTRACT FROM AUTHOR]

An autoregressive (AR) model for the simulation of modulus of elasticity (MOE) fluctuations along hardwood boards without pronounced knot periodicity is presented. The model is calibrated based on contiguous, localized (100 mm gauge length) empirical MOEs from European white oak boards. The calibration involves the computation of the sample autocorrelation function (SACF). For this computation, the stationary data resulting from a two-step transformation method are used: (1) first, board-internal MOE normalization suppresses the interboard bias; and (2) the rather left-skewed normalized data are described by a log-gamma distribution, which is then mapped to N(0,1) to obtain the needed stationary data. A first-order AR process suffices to accurately describe the data and is thus used as a basis to simulate MOE profiles, followed by the sketched transformation procedure in reverse order. The quality of the simulated MOE profiles and of the methodological approach is shown by comparison with the experimental MOE profiles. It was found that the simulation model correctly reproduces the statistical information of the MOE variations while also producing very plausible profiles, with similar characteristics as the empirical MOE profiles. This model can be used to better study the size effect of glued-laminated timber by means of numerical simulations. [ABSTRACT FROM AUTHOR]

Highway agencies use significant financial resources to operate traffic monitoring programs. Conventional traffic monitoring practices are operated only during weather-permissible periods, leading to inefficient traffic monitoring caused by restricted sample sizes. None of the previous studies considered the possibility of executing a traffic monitoring program throughout the entire year including wsevere winter weather conditions. The objective of this research was to diagnose the influence of snowfall and temperature on traffic volume changes according to vehicle class by employing two phases of statistical approaches and to propose unique traffic investigation programs for a different type of vehicle. This research used huge vehicular records archived for 6 years at the weigh-in-motion site operated in Leduc City, Alberta, Canada. Three vehicle classes classified from the raw data were used in the analysis. Three classes were total traffic, passenger cars, and truck traffic. Both layers of the statistical approach reported that passenger vehicles are impacted by climate elements. However, the truck was not significantly affected by snowfall and temperature. The results of this investigation will support highway agencies in developing evidence-based programs for traffic counting and road maintenance in winter. [ABSTRACT FROM AUTHOR]

Public agencies or owners that are responsible for the construction and maintenance of built infrastructure or facilities are working toward the digitalization of the project administration and delivery process, especially in the construction phase. However, implementation is still an ongoing effort despite technology availability. While contractors have readily adopted digital systems, owners lag in their implementation. This lag in implementation could be for different reasons, including lack of knowledge regarding challenges and barriers. Therefore, this research identifies and ranks potential influencing factors to enable successful implementation of digital construction-phase information management (DCIM) systems by project owners. Twenty-eight potential influencing factors were identified from the literature and ranked following statistical analysis of survey responses from industry professionals. While the results show that all identified potential influencing factors are important at different levels for the successful implementation of DCIM systems, their ranking enables decision-making based on perceived importance to the owner. Furthermore, responses from the two studied owner subpopulations indicated that there is general agreement on their importance despite significant statistical differences regarding the importance of six factors. The findings of this research can enhance understanding of the challenges and barriers present in the industry, especially at public agencies, to the implementation of digital technologies. [ABSTRACT FROM AUTHOR]

The hybrid bioelectrochemical systems (BES), a self-sustaining novel technology, was tested for evaluating its efficiency for removal of hexavalent chromium from electroplating industry wastewater. The BES reactor was designed and optimized for different parameters, such as substrate concentration, mixed liquor volatile suspended solids (MLVSS), hydraulic retention time (HRT), and chromium concentration. The concentration of hexavalent chromium [Cr(VI)] in the electroplating industries was found in the range of 0.43-48.7 mg/L. Whereas in the experiments Cr(VI) concentration, it varied from 10 to 50 mg/L (10, 20, 30, 40, and 50 mg/L). The BES demonstrated 87.88% of Cr(VI) removal at an influent concentration of 50 mg/L and HRT of 24 h. Correlation analysis revealed that chromium removal efficiency (CRE) was directly proportional to substrate consumption, voltage generation, MLVSS, and HRT, and had an inverse relation with chromium concentration in the cathode chamber. Principal component analysis (PCA) extracted two principal components that together explained approximately 82.15% of data variability (PC1: 62.73%; PC2: 19.41%). The kinetics of substrate degradation and chromium removal was assessed, revealing that substrate degradation and chromium removal can be better described by a first-order kinetic model. [ABSTRACT FROM AUTHOR]

The objective of this paper is the creation of a suitable methodology capable of deploying elements that characterize the existing infrastructure on rural two-lane highways regarding the location and geometric features of intersections and evaluating the built-in road safety. The target is the utilization of these elements to lead to evaluation coefficients that show the hazard level of every intersection and every road section, highlighting the intersections of the road network with reduced safety level. In this context, a hierarchy of critical parameters influencing the risk of an intersection was made, along with appropriate mathematical equations based on the current regulations and corresponding crash reduction factors (CRFs) that have been presented in the literature. Initially, case studies were conducted, and special diagrams were produced to assess the hazard level of each parameter. Then, basic statistical analysis of the results was made using regression factors to develop an appropriate mathematical model for the estimation of the hazard level. A corresponding procedure was performed for each parameter separately, and appropriate coefficients were sought. These coefficients were summed up, and a total score characterizing each intersection has been calculated, leading to a ranking in respect with the hazard level. Finally, data were collected on a significant sample of the existing road network of Greece, about 1,000 km of road network with more than 4,000 intersections (unpaved road intersections are also included), in order to evaluate the results of the proposed methodology. Part of the results has been interpreted with real accident data collected from the police department, showing that there is a correlation between them. This correlation must be confirmed in further research by a statistical analysis between the results of the present methodology and the actual recorded accidents. [ABSTRACT FROM AUTHOR]

Biannual bridge inspections are federally mandated for hundreds of thousands of bridges in the United States. This represents a significant expenditure of labor and equipment resources. Lean engineering is a framework to improve the efficiency of processes, which was applied to the bridge inspection process in this work. Thirty-two routine bridge inspections were shadowed and analyzed as a four-stage process (consisting of document review, mobilization and demobilization, onsite inspection, and report writing) that included 52 distinct activities. Each activity that occurred during a shadowed bridge inspection was categorized as value added or by type of waste. Most of the time was considered value added, but several types of waste were identified for possible reduction. Statistical analysis showed significant differences in the duration of bridge inspection for certain bridge types and inspection equipment utilized. With the many aging bridges in the United States, utilizing lean engineering to identify areas for further improvement has the potential for reducing the effort of routine bridge inspection. [ABSTRACT FROM AUTHOR]

The present study aims to quantify the fatigue characteristics of nanoclay-modified binders, considering different analysis procedures in the linear amplitude sweep (LAS) test. Four unmodified and modified asphalt binders were subjected to the LAS test at 10°C, 20°C, and 30°C. The fatigue life obtained using five different analytical methods were assessed and compared. The effect of controlled strain rate (CSR) used in the LAS test was not found to be significant in changing the fatigue ranking of asphalt binders. Different analytical methods produce different fatigue curves with no change in the relative ranking of the binders at each temperature. The model parameters (a and b) obtained by fitting the relationship between average released pseudostrain energy (GR) and the number of cycles to failure (Nf) can significantly impact fatigue life, resulting in a discrepancy in the results. This discrepancy, across different temperatures and binder types, can be removed by smoothening of the model with b=−2. The unified GR–R-based analysis, as is proposed in this study, results in a lower value of fatigue life at lower strain susceptibility in the standard fatigue model. Statistical analysis reveals that there is no distinct influence of nanoclay on the fatigue life of asphalt binders. Inclusion of nanoclay results in either similar or marginally higher fatigue life at all the temperatures. The analysis and results support the use of a GR–R-based approach for quantifying the fatigue life of asphalt binders. [ABSTRACT FROM AUTHOR]

Electric power is a critical commodity of the modern world where demand for electricity has been increasing significantly in the last decades, and it is expected to keep increasing in the future. Several socioeconomic factors affect and get affected by the electric sector development, but the literature falls short in studying such relationships in a holistic approach. The goal of this paper is to study how the electric power sector development (i.e., electrical power consumption per capita and electrical losses during distribution) is associated with the socioeconomic indicators of economic growth, human development, and corruption. The research methodology comprises a series of statistical tests, such as the correlation analysis, linear regression, panel analysis, polynomial regression, and Granger causality testing. The findings show that there is generally a relationship between electrical power consumption on one side as well as economic growth, human development, and corruption on the other side. This relationship emerges when studying all the data from all countries as a whole but not necessarily for each country separately. In general, countries that have healthier economies, more advanced human development, and low corruption tend to have higher electrical consumption per capita. Regarding the causality between the parameters, results show that there is no causality effect on the short term between electrical power consumption and any of the socioeconomic indicators under investigation. Also, it was found that there is generally a positive relationship between low corruption and low electrical power losses. In countries with higher corruption, there are large amounts of electric power that is lost (i.e., the power is generated but is not billed). However, overall, an analysis of the relationship between corruption and electric power losses are mixed and hard to interpret. Ultimately, this research emphasizes that the dynamics of the electrical power sector should be examined from the economic and social perspectives and not only the engineering technical perspective. [ABSTRACT FROM AUTHOR]

Tornadoes create a threat to human life. Knowing the conditions that make people vulnerable to this threat is vitally important. Yet, socioeconomic and demographic data are not consistently available at the tornado level, making it hard to obtain this knowledge. In response to this limitation, here a method to estimate socioeconomic and demographic variables in a consistent manner at the tornado level for historical events is implemented and assessed. The dasymetric method uses data from the 1990 and 2000 Censuses, as well as the 2010 American Community Survey together with tornado reports over the period 1995–2016. Results show that a typical casualty-producing tornado affects 34 people with an interquartile range between 4 and 198 people. Results also show that the Detroit tornado of July 2, 1997, with its 90 known injuries, likely affected nearly 101,752 people. Comparisons between estimates using the actual path and a simplified modeled path show strong correspondence (percentage errors averaging less than 10%) and estimates compare favorably (correlations exceeding 0.90) with known demographic numbers from a sample of tornadoes, indicating the procedure provides useful information for statistical studies of tornado vulnerability. [ABSTRACT FROM AUTHOR]

In order to solve the problem of unclear evolution paths after oil–gas storage and transportation accidents, which lead to a lack of targeted accident emergency, delayed disposal measures, and further deterioration of the accident, based on the existing accident scenario-response theory, this article summarizes 17 basic scenarios in oil–gas storage and transportation by analyzing 116 accident cases. Moreover, the accident scenario expressions are given in time and space dimensions, and general scenario evolution paths of fire accidents for oil–gas storage and transportation are constructed. On this basis, a dynamic scenario deduction network model is established by using a dynamic Bayesian network. In this model, key scenario nodes and their final scenario probabilities are determined in consideration of statistical probability obtained by the actual accident cases, empirical probability given by domain expert's scoring, and state probability calculated by joint probability formula of a dynamic Bayesian network. As a case study, the scenario deduction process and result of a Dalian 7.16 accident are analyzed to verify the rationality and effectiveness of the proposed method. According to the proposed scenario deduction method, it can help decision-makers to make more targeted emergency disposal measures. [ABSTRACT FROM AUTHOR]

Recently, high-resolution data has emerged as an economical means of obtaining detailed information about signal operations. This has enabled the development of metrics to examine progression, such as cyclic flow profiles similar to those used in the TRANSYT model. Those profiles could further be used to predict changes to traffic flows under trial offset adjustments, enabling offset optimization. An algorithm to do so, called "Link Pivot," has been used on numerous signalized arterials in Indiana and elsewhere over the past 8 years. This paper reviews the success of the predicted changes through statistical analysis. A linear regression of 569 predicted and actual percent on green values yields an R2 of 0.90. Next, predicted and actual arrival profiles were compared for 357 approaches for which detailed data was available. This analysis finds that the prediction method was successful in about 95% of the approaches considered in this evaluation. The paper dissects the remaining cases and discusses how they could be accommodated. [ABSTRACT FROM AUTHOR]

There is an increasing interest in signal-timing optimization methods that can consider mobility, safety, and emissions measures simultaneously. The introduction of new models increases the complexity of the required inputs and the relationships between inputs and outputs. This study developed and implemented such a method in an existing computational engine, presenting a sensitivity analysis conducted to provide insight on the effects and order of relevance of 20 key variables on the model's outcomes and the associated trade-offs among mobility, safety, and emissions. This insight will help the designer, signal control engineer, and traffic analyst when designing intersection geometry and signal control. The statistical analysis of the results showed that the effect of each variable on the overall performance of the model is highly dependent on the combination of other variables. The traffic demand and the size of the intersection, defined by the number of lanes on the arterial, were found to be the most significant variables, affecting all performance measures. Mobility improvement performance usually coincides with emissions improvements, but sometimes occurs at the expense of safety. [ABSTRACT FROM AUTHOR]

Output-only modal identification plays an important role in the structural health monitoring of large-scale structures. In recent years, blind source separation (BSS) has achieved great success in structural modal identification. Sparse component analysis (SCA), which is one of the most popular methods of BSS, has the capability to handle nonstationary excitation and underdetermined problems. In the process of SCA, clustering number, which is equal to the number of active modes, plays an important role in the estimation of modal matrix, in which the hierarchical clustering algorithm is used. However, the clustering number is always unknown in the clustering step, which makes application inconvenient. To fill this gap, an improved SCA method, equipped with a process of estimating the clustering number, is proposed in this paper. After transforming the signals into time-frequency (TF) domain, the single-source-points (SSPs) detection process is applied to pick out the TF points at which only one mode makes a contribution to the responses. The clustering technique is preceded by a preprocessing step to determine the clustering number. The key idea is that the clustering number is equal to the number of columns in the modal matrix, which is reflected in the number of lines in the scatter plot of two observations. A normalization method is proposed to distinguish the clusters clearly. The number of clusters is acquired through statistical analysis of the normalized vectors. After obtaining the modal matrix, the smoothed zero-norm algorithm is used to recover the modal responses in order to extract natural frequencies and damping ratios. An experimental cantilever beam and a three degree-of-freedom (DOF) numerical system with closely spaced modes were used to verify the effectiveness of the proposed method. The results showed that the improved SCA could detect the number of active modes for the beam and the numerical system. Full-scale data measured from the Green Building located at the Massachusetts Institute of Technology (MIT) campus and the Tianjin Yonghe Bridge were analyzed to verify the effectiveness of the proposed method in practical applications. [ABSTRACT FROM AUTHOR]

The study uses a 35-year data set, which can be used to provide a long-term assessment for assessing wave power resources, availability for wave energy converters (WECs), and multithreshold accessibility suitable for numerous vessels and important for offshore maintenance operations. The data set demonstrates that winter months have harsher environmental conditions for the Western regions, such as the Spanish coastlines, and the Eastern regions, such as the Aegean Sea, record slightly higher waves during summer months. The data set also identifies the seasons with lower resources, which will have higher accessibility and will benefit offshore construction and maintenance operations for offshore energy activities. It has been shown that the availability for wave energy depends on the operational range of a particular type of WEC; hence, threshold selection affects the availability distribution more than accessibility. Availability varies per region, with Southern Aegean, Southern Italian, and North African coasts having higher monthly values. Accessibility in nearshore areas is constantly over 90%, with deeper waters presenting reduced levels. Statistical analysis performed for this work shows predictable availability due to lower maxima, potentially enhancing WECs operation; this, however, will depend on device properties. Furthermore, the resource analysis indicates that the dominantmetocean conditions yield low wave height range. [ABSTRACT FROM AUTHOR]