Simulating pavement structural condition using artificial neural networks

This article aims to investigate the feasibility of incorporating of an artificial neural network (ANN) as an innovative technique for modelling the pavement structural condition, into pavement management systems. For the development of the ANN, strain assessment criteria are set in order to characterise the structural condition of flexible asphalt pavements with regards to fatigue failure. This initial task is directly followed with the development of an ANN model for the prediction of strains primarily based on in situ field gathered data and not through the usage of synthetic databases. For this purpose, falling weight deflectometer (FWD) measurements were systematically conducted on a highway network, with ground-penetrating radar providing the required pavement thickness data. The FWD data (i.e. deflections) were back-analysed in order to assess strains that would be utilised as output data in the process of developing the ANN model. A paper exercise demonstrates how the developed ANN model combined with the suggested conceptual approach for characterising pavement structural condition with regard to strain assessment could make provisions for pavement management activities, categorising network pavement sections according to the need for maintenance or rehabilitation. Preliminary results indicate that the ANN technique could help assist policy decision makers in deriving optimum strategies for the planning of pavement infrastructure maintenance.     

Encoder–decoder with pyramid region attention for pixel-level pavement crack recognition

Timely and accurate extraction of pavement crack information is crucial to maintain service conditions and structural safety for infrastructures and reduce further road maintenance costs. Currently, deep learning techniques for automated pavement crack detection are far superior to traditional manual approaches in both speed and accuracy. However, existing deep learning models may easily lose crack details when processing images containing complex background textures or other noises. Although many studies have alleviated this challenge by introducing attention mechanisms, especially the non-local (NL) block, which has the ability to efficiently capture long-range dependencies to facilitate crack pixel capture, the huge computational cost of NL makes the inference time of the model too long, which is not conducive to practical implementation. In this study, a new module, namely, the pyramid region attention module (PRAM), was developed by combining the pyramid pooling module in the pyramid scene parsing network and optimized NL, which can achieve global multi-scale context integration and long-range dependencies capture at a relatively lower computational cost. By applying PRAM to deep skip connections in the modified U-Net, an effective crack segmentation model called CrackResU-Net was developed. The test results on the existing CrackForest dataset showed that CrackResU-Net not only achieved an F1 score of 0.9580 but also took only 25.89 ms to process an image with a resolution of 480 × 320, which had advantages in accuracy and speed, compared with several other state-of-the-art crack segmentation approaches. It was fully demonstrated that this approach could realize automatic fast and high-precision recognition of pavement cracks for engineering purposes.     

A multi-objective optimisation approach for sustainable pavement management

Addressing the multidimensional challenges involved in advancing the sustainability of pavement systems requires the development of optimisation-based decision support system (DSS) for pavement management with the capability to identify optimally sustainable pavement maintenance and rehabilitations (M&R) strategies. The main objective of this research work is to develop a multi-objective optimisation framework that hosts a comprehensive and integrated pavement life cycle costs–life cycle assessment model that covers the pavement’s whole life cycle, from the extraction and production of materials to construction and maintenance, transportation of materials, work-zone traffic management, usage and end-of-life. The capability of the proposed DSS is analysed in a case study aiming at investigating, from a full life cycle perspective, the extent to which a number of pavement engineering solutions are efficient in improving the environmental and economic aspects of pavement sustainability, when applied in the management of a road pavement section. Multiple bi-objective optimisation analyses considering accordingly agency costs, user costs and greenhouse gas emissions were conducted based on a multi-objective genetic algorithm. Pareto fronts were obtained for each analysis, originating a set of non-dominated maintenance and rehabilitation solutions. Posteriorly, a multi-criteria decision analysis method was used to find the best compromise solution for pavement management.     

谈路面维护和修复管理系统的离散优化模型

随着公路建设和高速公路网在的迅速发展,路面维护和修复活动的管理已变得尤为重要。论文提出了涉及管理体制4个不同群体的离散优化模型,不同群体分别为:政府、高速公路代理人、承包商和普通用户。这4个最优决策模型的制定及二元决策变量的线性整数规划问题。目标函数和约束条件都是基于路面状况指数。数值实验对四川省的公路系统的数据进行分析,其表明了该模型的可行性和有效性。     

Enhancing life cycle cost analysis with a novel cost classification framework for pavement rehabilitation projects

Life cycle cost analysis (LCCA) procedures have been used over the past decades to justify the choice of one pavement design alternative over the others. However, many ambiguities associated with the life cycle cost input values, such as the discount rate and future cost estimates have questioned the credibility of the analysis results. Another unrecognized source of errors in pavement LCCA is the misunderstanding of pavement treatment costs when historical costs are typically used for estimating those costs. The historical costs of pavement rehabilitation projects typically include a significant amount of non-pavement-related costs, which may result in a wrong LCCA if not treated appropriately. This paper addresses this specific point of error and proposes a solution to eliminating this error by using a novel cost classification framework that successfully differentiates mainline roadway costs from non-pavement cost items. A case study using Monte Carlo simulation is conducted to evaluate the probabilistic LCCA results. The results of the case study indicate that the conventional approach of using total rehabilitation project costs in LCCA may even lead to a wrong investment decision. The findings of this study will help practitioners and researchers better understand the nature of pavement rehabilitation project cost distributions.     

Performance of geosynthetic-reinforced flexible pavements in full-scale field trials

The paper presents the results of a series of full-scale trials carried out in Thailand examining the performance of geosynthetics as reinforcement for flexible pavements. The geosynthetics were embedded at different pavement depths and the structural response was monitored across four test sections by means of strain gauges, pressure sensors, deflection points and deflection plates. The results show that all reinforcement configurations helped reduce the vertical static stresses developed at the base of the pavement by up to 66% and by up to 72% for dynamic stresses. The performance enhancement expected to prolong the lifespan of the base layers. The reinforcement layers closer to the base experienced the highest lateral strains of up to 0.13%, providing evidence that geosynthetics can also effectively reduce lateral spreading. All reinforcement configurations helped enhance rut resistance with maximum traffic benefit ratio (TBR) of 13.70, effectiveness ratio (EF) of 12.70 and minimum rutting reduction ratio (RRR) of 0.74. The best configuration included a geotextile within the asphalt concrete layer and a geogrid under the base layer. Non-linear finite element analyses of the test sections predicted very well the strains and stresses in the pavement. The study provides a benchmark for future studies in this field and concludes that geosynthetics can help increase maintenance periods and extend the lifetime of flexible pavements.     

区间数互反判断矩阵的高速公路 BOT 项目融资风险指标权重

BOT 项目融资模式作为一种越来越重要的新型融资模式不断应用在高速公路项目建设中,对此模式的研究也在不断地探索、实践和创新中。在已有研究的基础上,提出了基于区间数互反判断矩阵的高速公路 BOT 项目融资风险指标权重确定模型。决策者通过两两比较给出了能够反映决策者风险偏好的区间数互反判断矩阵,在此基础上进行一致性检验,对不一致的矩阵,则通过采用一致性逼近方法构造一致性区间数互反判断矩阵,再基于参数取值确定了高速公路 BOT 项目融资风险指标权重。结合实例进行了验证计算,结果表明该方法提高了指标权重确定的科学性,克服了传统评价方法客观性不足等问题     

模糊综合评判法在高层建筑结构优选中的应用

对高层建筑结构选型决策的现状、特点及影响因素作了概括性论述,具体说明了模糊综合评判法在高层建筑结构选型中的应用,详细介绍了如何运用模糊综合评判法进行结构体系的优选,为建筑设计单位提供了科学的决策依据。     

Integration of geographic information systems and computer vision systems for pavement distress classification

The main objective of this research work was to investigate the potential of integration of geographic information system (GIS), global positioning system (GPS) and computer vision system (CVS) for the purpose of flexible pavement distresses classifications and maintenance priorities. The classification process included distress type, distress severity level and options for repair. A system scheme that integrated the above-mentioned systems was developed. The system utilized the data collected by GPS and a PC-based vision system in a GIS environment. GIS Arcview software was used for the purpose of data display, query, manipulation and analysis.The developed system provided a safer pavement condition data collection technique, flexible data storage, archiving, updating and maintenance priorities updating. Maintenance priorities were assigned based on priority indices values computed by priority index (PI) or available budget criterion. This technique was cost-effective and offered wise-based decision making for different maintenance activities and programs.Using average daily traffic (ADT), distance from maintenance unit (R), pavement section area and pavement age, statistical models were developed to forecast pavement distress quantities. It was found that ADT and pavement age variables were the most significant factors in the distresses quantification.     

基于区间直觉模糊 AHP 法的大坝浇筑方案选取

针对工程方案优选中的模糊问题,基于直觉模糊层次分析法,将同时考虑隶属度、非隶属度及犹豫度的区间直觉模糊数引入层次分析法,对工程方案进行优先排序。通过建立层次结构模型,根据影响因素的重要性差异确定直觉模糊判断矩阵,依据决策者权重,计算各方案判断矩阵的熵权,从而得到各方案在不同属性下的最终熵权,并依据大小排序确定最优方案。将该方法应用于工程案例,通过计算和分析证明了本方法在工程方案优选问题中有效性及可行性。     

Underground mining method selection by decision making tools

Underground mining method selection is one of the most important decisions that mining engineers have to make. Choosing a suitable underground mining method to extract a mineral deposit is very important in terms of economics, safety and productivity of mining operations. In real life, underground mining method selection is one of the multiple attribute decision making (MADM) problems and decision makers have always some difficulties in making the right decision in the multiple criteria environment. Analytic hierarchy process (AHP) and Yager’s methods are the MADM tools and can be used for selection of the best underground mining method by considering the problem criteria. In this study, a computer program (UMMS) based on the AHP and the Yager’s method was developed to analyze the underground mining method selection problems and produce the best underground mining method swiftly for different deposit shapes and ore bodies.     

UNCERTAINTIES OF ASPHALT LAYER THICKNESS DETERMINATION IN FLEXIBLE PAVEMENTS-INFLUENCE DIAGRAM APPROACH

The thickness of pavement layers is an important parameter used in Pavement Management Systems (PMS). Thickness data are used for pavement condition assessment, performance predictions, selection of maintenance strategies and rehabilitation treatments, basic quality assessment, and as input to overlay thickness design. Pavement thickness is usually determined from direct testing such core samples, nondestructive testing such as radar, or historical records such as pavements network database. This paper proposes the use of Bayesian Influence Diagrams as a tool in providing a probabilistic model for thickness determination procedure in flexible pavements. The Bayesian Influence Diagram Model is presented as a framework for addressing uncertainties involved in capturing quantitative and qualitative information in the asphalt layer thickness determination procedures. The model is also used to perform value of information analysis in the determination of pavement layer thickness. The Influence Diagram representation facilitates the assessment of coherent prior distributions and makes it easier for knowledge engineers and other decision makers to express and understand more general kinds of dependency and independency assumptions.     

改进AHP-灰色关联分析的给水厂排泥水处理工艺优选模型

针对目前给水厂排泥水处理工艺流程甑选过程中存在的若干问题,以灰色关联分析为骨架建立给水厂排泥水处理工艺优选模型。通过最优传递矩阵对传统层次分析法进行改进,解决了传统层次分析法判断矩阵一致性修正的难题,并采用改进的层次分析法确定模型权重分配集合。以重庆某电厂净水站排泥水处理工艺设计为实例,介绍该模型的计算流程以及评价方法,并运用模型对工艺流程进行评判,得出适合该电厂净水站排泥水处理的最优工艺。     

小议沥青混凝土路面设计中若干问题

沥青路面设计是一个复杂的体系,在进行沥青混凝土路面设计中,要充分考虑到多种综合因素。本文结合笔者多年的实践经验,对沥青混凝土路面设计中存在的问题以及解决方案进行了全面探讨,希望对同行有所帮助。     

Selection of funding schemes by a borrowing decision model: a Hong Kong case study

In financial decision‐making, a number of mathematical models have been developed for financial management in construction. However, optimizing both qualitative and quantitative factors and the semi‐structured nature of construction finance optimization problems are key challenges in solving construction finance decisions. The selection of funding schemes by a modified construction loan acquisition model is solved by an adaptive genetic algorithm (AGA) approach. The basic objectives of the model are to optimize the loan and to minimize the interest payments for all projects. Multiple projects being undertaken by a medium‐size construction firm in Hong Kong were used as a real case study to demonstrate the application of the model to the borrowing decision problems. A compromise monthly borrowing schedule was finally achieved. The results indicate that Small and Medium Enterprise (SME) Loan Guarantee Scheme (SGS) was first identified as the source of external financing. Selection of sources of funding can then be made to avoid the possibility of financial problems in the firm by classifying qualitative factors into external, interactive and internal types and taking additional qualitative factors including sovereignty, credit ability and networking into consideration. Thus a more accurate, objective and reliable borrowing decision can be provided for the decision‐maker to analyse the financial options.     

Hydro-environmental changes and their influence on the subsurface environment in the context of urban development

The relationship between urban development and hydro-environmental change, particularly with regard to the subsurface environment is examined for three coastal cities affected by Asian monsoons (Tokyo and Osaka in Japan, and Bangkok in Thailand). Major differences in subsurface changes among these cities are closely related to city size, urban structure, and the timing, stage and extent of urbanization as well as the natural environment. The work shows that the urban development has not affected the Bangkok subsurface hydro-environment in the same way it has in Tokyo and Osaka. Three reasons for the difference account for this, (1) Bangkok's abundant annual rainfall, (2) Bangkok has the smallest ratio of impervious pavement surface area, meaning that surface water can more easily infiltrate underground., (3) the degree and extent of urbanization. Bangkok's subsurface hydro-environment has not been heavily affected because underground development has not yet reached deep subterranean areas.By researching yet more cities, at different stages of urbanization to that of Tokyo, Osaka and Bangkok, we plan to quantitatively examine urbanization and its influence on subsurface hydro-environments. This research will help limit damage to developing cities that are not yet experiencing subsurface failures but which are expected to confront these problems in the future.     

Modelling the effect of various factors on the condition of pavement marking

Poorly maintained pavement marking might certainly contribute to road accidents. The cost of road accidents is estimated to be 10–25 billion Canadian dollars annually. Accordingly, it is necessary for municipalities to develop a strategic cost-effective plan in order to renew and restripe pavement markings. Therefore, the objective of the present research is to model the effect of various factors on pavement marking conditions. Data on Alkyd paint pavement marking material are collected from the city of Ottawa, Ontario, Canada. Since the collected data from municipalities in Canada always include input variables and fail to provide output variable(s) (e.g. condition), an unsupervised neural network (UNN) model is first developed to generate the condition of pavement marking (output). Then, regression and neurofuzzy models are developed based upon the results of UNN model. The developed models are validated in which they show satisfactory results. A sensitivity analysis is performed to show the effect of changing the input variables on the developed models’ output(s).     

Algorithm for Spatial Clustering of Pavement Segments

Abstract:   An algorithm is developed to enable the Georgia Department of Transportation (GDOT) to determine pavement preservation project termini by analyzing segment-level pavement condition rating. This article formulates a new spatial search model for determining appropriate pavement preservation project termini. A spatial clustering algorithm using fuzzy c-mean clustering is developed to minimize the rating variation in each cluster (project) of pavement segments while considering minimal project scope (i.e., length) and cost, initial setup cost, and barriers, such as bridges. A case study using the actual roadway and pavement condition data in fiscal year 2005 on Georgia State Route 10 shows that the proposed algorithm can identify more appropriate segment clustering scheme, than the historical project termini. The benefits of using the developed algorithm are summarized, and recommendations for future research are discussed.     

Scheduling system for high rise building construction

A computerized system to schedule high-rise building construction has been developed using line-of-balance technology assisted by an expert system. A review of the recent literature on the techniques available for scheduling and controlling construction projects of a repetitive nature shows that Gantt charts are inadequate, and that there are serious problems with using network methods in such circumstances. There is evidence that the construction of high rise buildings has a decidedly repetitive nature but differs in some respects from other repetitive projects such as pipelines or pavement construction. Two new concepts have been introduced into line-of-balance methodology to accommodate the special conditions encountered in high rise building construction. These two concepts, namely ‘flexible’ unit networks and ‘multi-level’ LOB diagrams have been coded into a scheduling module (‘Lobplans’). A series of databases have been compiled regarding the productivity of resources. An expert system module (Lobex) has been developed to facilitate decision-making at network generation level. The scheduling module, the databases, and the expert system have been organized into an integrated system (Chriss) by means of communication and command routines that interface between the modules and the user input. A 16-storey building project has been used in testing Chriss' performance. The integrated system proved to be user friendly and reliable.     

沥青玛[王帝]脂碎石路面的施工要点

分别从拌合场地的选择、拌合设备的选择、摊铺设备的选择、原材料的选择及沥青混合料的拌和、运输、摊铺、碾压成型等方面提出了沥青玛[王帝]脂碎石路面在施工中应注意的问题,从而使路面具有良好的抗车辙效果和抗滑性能。