Queue performance measures in construction simulation models containing subjective uncertainty

In recent decades, discrete event simulation (DES) has been widely used for analyzing construction projects. Recently, fuzzy discrete event simulation (FDES), which is an integration of fuzzy set theory with DES, has been proposed for simulating construction projects. FDES provides a framework to consider subjective uncertainty (uncertainty due to vagueness, subjectivity, and linguistic expression of knowledge) in construction simulation models. Current FDES frameworks only calculate simulation time (e.g., project completion time) as the simulation output. However, queue performance measures (e.g., average queue length and waiting time)—though important simulation model outputs for decision making, finding bottlenecks, and optimizing construction resources—are not analyzed in current FDES methodologies. Using fuzzy logic to consider the subjective uncertainty of service time and the inter-arrival time of systems' queues may improve such simulation models by more realistically representing their results. This paper provides a novel methodology to consider subjective uncertainty in analyzing the fuzzy queues in construction FDES models. Incorporating fuzzy queuing theory with FDES methodology as proposed in this paper enhances the applicability of FDES in construction projects. The proposed methodology is validated through mathematically solved queueing examples, and its practical aspects are illustrated using an example of an asphalt paving operation.     

Queuing networks and facility planning

Open, closed, and mixed networks of queues are shown to be viable techniques for modelling public buildings. The optimal access and egress problems of public buildings together with the necessary assumptions, concepts and mathematical details for employing these queuing network models are presented. The paper concludes with an example comparison of a single-story and a three-story public building modelled as a queuing network. The demonstration indicates how queuing networks can quantify system bottlenecks, measures of congestion and overall sojourn time for the different customer classes intent on utilizing the facility.     

Improving concrete placement simulation with a case-based reasoning input

Discrete-event simulation has been used extensively in the past to analyse construction operations and has been shown to be an effective tool for improving construction process planning. Unfortunately, the widespread application of simulation has been prevented, in part, by the requirement for the developer and user to understand the stochastic features of the process. Further, if the stochastic inputs are not representative of the real system, the simulation output will be misleading. This article proposes that case-based reasoning (CBR) could be used to: improve the input and output data of a simulation model and remove the requirement for a user to have specialist statistical knowledge of the process. Case-based reasoning works by solving new cases from knowledge stored in a case base.

Two models are presented in this article: a discrete-event simulation model, MatSim, and a hybrid CBR-simulation model, CBRSim. Both models were developed using real construction data. The models were compared to measure any improvements in simulation output, by using a CBR suggested input. Data from an independent construction project were used to test both models, and the results indicate that CBRSim can achieve estimates of observed values that are more accurate and reliable than those from MatSim.     

Uncertainty Processing and Risk Monitoring in Construction Projects Using Hierarchical Probabilistic Relational Models

Construction projects do not often reach their expected results regarding time, cost, and quality, due to the internal and external environment variations. Despite a substantial literature about risk management, no generic approach is proposed to represent construction project considering together technical and human dimensions or sustainability with their uncertainties. Modeling complex dynamical systems from heterogeneous pieces of knowledge varying in precision and reliability is a challenging task. This article proposes an innovative generic and versatile approach, based on the formalism of hierarchical probabilistic relational models to analyze and to propagate uncertainty in construction project regarding different levels of knowledge. The aim is to obtain a flexible, portable, and versatile model able to simulate the behavior of complex system's entities involved in any construction project at different levels of detail while taking uncertainty into account. To illustrate and highlight this approach, an academic example and a real case are proposed.     

Antithetic Method‐Based Particle Swarm Optimization for a Queuing Network Problem with Fuzzy Data in Concrete Transportation Systems

The aim of this article is to develop an antithetic method‐based particle swarm optimization to solve a queuing network problem with fuzzy data for concrete transportation systems. The concrete transportation system at the Jinping‐I Hydropower Project is considered the prototype and is extended to a generalized queuing network problem. The decision maker needs to allocate a limited number of vehicles and unloading equipment in multiple stages to the different queuing network transportation paths to improve construction efficiency by minimizing both the total operational costs and the construction duration. A multiple objective decision‐making model is established which takes into account the constraints and the fuzzy data. To deal with the fuzzy variables in the model, a fuzzy expected value operator, which uses an optimistic–pessimistic index, is introduced to reflect the decision maker's attitude. The particular nature of this model requires the development of an antithetic method‐based particle swarm optimization algorithm. Instead of using a traditional updating method, an antithetic particle‐updating mechanism is designed to automatically control the particle‐updating in the feasible solution space. Results and a sensitivity analysis for the Jinping‐I Hydropower Project are presented to demonstrate the performance of our optimization method, which was proved to be very effective and efficient compared to the actual data from the project and other metaheuristic algorithms.     

利用排队论确定挖土机数量及其在BIM 中的应用

土方工程是很多工程项目中不可缺少的一部分,制定合理的土方挖运车辆调度方案对于工程的各个方面都具有十分重要的意义。基于排队论,建立算法并且利用Matlab 计算出最合理的挖土机数量,使单位时间内工程的总成本降到最低。阐述了基于BIM 的施工模拟的理论来源及其优势,以及利用排队论中的数据和一些BIM 软件来制作施工过程模拟动画的具体步骤。由排队论模型运算得出的与汽车运动状态有关的时间参数,可以用来丰富BIM 模型,使每一辆自卸卡车所对应的 Animation 动画更加精确完整,导出的施工模拟更加真实,符合实际工程情况。数值计算软件与建筑施工软件之间的联系充分体现了施工仿真过程中信息一体化的特点。研究成果具有广泛的实用性,可以应用到土木工程管理、高校专业教学等多方面领域。     

Quantitative assessment of groundwater vulnerability using index system and transport simulation, Huangshuihe catchment, China

Groundwater vulnerability assessment has been an increasingly important environment management tool. The existing vulnerability assessment approaches are mostly index systems which have significant disadvantages. There need to be some quantitative studies on vulnerability indicators based on objective physical process study. In this study, we tried to do vulnerability assessment in Huangshuihe catchment in Shandong province of China using both contaminant transport simulations and index system approach. Transit time of 75% of hypothetical injected contaminant concentration was considered as the vulnerability indicator. First, we collected the field data of the Huangshuihe catchment and the catchment was divided into 34 sub areas that can each be treated as a transport sub model. Next, we constructed a Hydrus1D transport model of Huangshuihe catchment. Different sub areas had different input values. Thirdly, we used Monte-Carlo simulation to improve the collected data and did vulnerability assessment using the statistics of the contaminant transit time as a vulnerability indicator. Finally, to compare with the assessment result by transport simulation, we applied two index systems to Huangshuihe catchment. The first was DRASTIC system, and the other was a system we tentatively constructed examining the relationships between the transit time and the input parameters by simply changing the input values. The result of comparisons between the two index systems and transport simulation approach suggested partial validation to DRASTIC, and the construction of the new tentative index system was an attempt of building up index approaches based on physical process simulation.     

Planning linear construction projects: automated method for the generation of earthwork activities

Earthworks planning for road construction projects is a complex operation and the planning rules used are usually intuitive and not well defined. An approach to automate the earthworks planning process is described and the basic techniques that are used are outlined. A computer-based system has been developed, initially to help planners use existing techniques more efficiently. With their input, the system has been extended to incorporate a knowledge base and a simulation of the earthworks processes. As well as creating activity sets in a much shorter time, the system has shown that for a real project, the model is able to generate activity sets that are comparable to those generated by a project planner.     

分形在建筑造型设计中的应用

基于分形算法中的L Systems算法、Shape Grammar和空间曲面方程理论,提出了一种支持创新的自动化、智能化计算机辅助建筑造型设计方法。通过计算机程序在较短的时间内衍生出多个具有一定分形特征的建筑地坪、墙体及建筑穹顶等三维实体,并保存到构件库中,利用构件库的可扩充性实现了设计知识的积累。建筑设计师依据设计需求,选择构件库中的三维实体,然后进行人工组装,设计完成复杂的建筑造型。以一个建筑造型设计为例,验证了方法的有效性。     

A Simple Efficient Engineering Data Input System

An input system is presented which has been tailored for engineering data. The system makes use of a problem-oriented language organized as a tree structure. Input data is parsed line by line and placed in appropriate queues. The application program then extracts this queued data using logical functions. A sample finite element problem is included to demonstrate the use of the input system. The input system requires only 17 Kb of memory and operates very efficiently. The system is ideal for microcomputer applications and provides input methods that were once only available in extremely large applications programs.     

Uncertainty in the thermal conductivity of insulation materials

Increasing attention is being paid to the application of uncertainty and sensitivity analysis methods to model validation and thermal systems simulation. The idea is to let users to apply uncertainty bands to their model input data. These bands are then propagated through the model to determine the uncertainty bands of the simulation results. One of the main difficulties the practitioner finds when trying to apply these techniques is the lack of information on the uncertainty that affects to typical input variables. This paper is a contribution to fill this gap. It quantifies the uncertainty that can be expected in the thermal conductivity of insulation materials in the lack of specific experimental measurements. Results are presented for specific materials and for groups of materials, in order to consider situations in which the material under consideration is not well defined. The proposed conductivities are derived from a large set of measurements that was compiled in a previous European project headed by the BRE Scottish Laboratory. To illustrate how the uncertainty bands can be used in practice, an example is discussed on the validation of the mathematical model of a solar thermal collector.     

Risk-based optimization of the debt service schedule in renewable energy project finance

Project finance is used in capital intensive renewable energy projects worldwide. Financial entities such as large banks and institutional investors are active in providing syndicated loans for infrastructure projects and compete to offer better terms to the sponsors of these projects. The literature is full of research on capital structure optimization. We propose a novel stochastic framework for optimizing the debt service schedule with due regard to the probability of default of the project company. The applicability of the proposed method is demonstrated for a 10 MW solar photovoltaic project employing a genetic algorithm (GA) as the optimization tool. The NASA SSE dataset is used to collect irradiation data, and PVsyst software is employed to simulate the performance of the project. The uncertainties are accounted for using Monte Carlo simulation, and the revenue generated, its corresponding free cash flow and the debt service coverage ratio are simulated as random variables. The proposed optimization framework enables lenders to offer an optimized debt service that maximizes the shareholder's profitability index. A particle swarm optimization is also employed to validate the stability and usefulness of GA optimization.     

An integrated modeling mechanism for optimizing the simulation model of the construction operation

A planner may use the discrete-event simulation to analyze and design the construction operation process that optimizes the overall performance of a construction system. Normally, the basic elements used in construction operation process simulation system, such as CYCLONE (CYCLic Operation NEtworks), are “activity” and “queue.” Activity is used to model the task which consumes resources and takes time to perform. Queue acts as a storage location for resources entering an idle state. In the simulation system, queues have to be created according to the ways of assigning resources to activities. Conventionally, planner defines queues according to his/her judgment by determining which and what amount of resources should be allocated to which activity. Consequently, various modeling schemes have to be examined to obtain the best simulation model. However, such a process of creating queues and activities is time consuming and requires iterations. This paper introduces a Genetic Algorithms (GA)-based modeling mechanism to automate the process of selecting the optimal modeling scheme. Case study shows that this new modeling mechanism along with the implemented computer program not only can ease the process of developing the optimal resource combination but also improve the system performance of the simulation model.     

Forecasting enterprise resource planning software effort using evolutionary support vector machine inference model

Despite significant advances in procedures that facilitate project management, the continued reliance of software managers on guesswork and subjective judgment causes frequent project time overruns. This study uses an Evolutionary Support Vector Machine Inference Model (ESIM) for efficiently and accurately estimating the person-hour of ERP system development projects. The proposed ESIM is a hybrid intelligence model integrating a support vector machine (SVM) with a fast messy genetic algorithm (fmGA). The SVM mainly provides learning and curve fitting while the fmGA minimizes errors. The analytical results in this study confirm that, compared to artificial neural networks and SVM, the proposed ESIM provides preliminary prediction at early phase of ERP software development effort for the manufacturing firms with superior accuracy, shorter training time and less overfitting. Future research can develop user-friendly expert systems with window or browser interfaces that can be used by planning personnel to flexibly input related variables and to estimate development effort and corresponding project time/cost.     

A sematic and prior‐knowledge‐aided monocular localization method for construction‐related entities

The real‐time location of construction‐related entities is some of the most useful basic information for automated construction management. However, the implementation of most existing localization methods is limited due to the weak adaptability to construction sites. In this paper, we enhance the monocular vision technique for the localization of construction‐related entities by a sematic and prior knowledge‐based method. A deep learning algorithm is employed to segment the sematic instance in the images, and then the prior knowledge model specifies projection strategies for entities corresponding to various scenarios. Results show that the proposed method achieves satisfying positioning accuracy, is robust in low‐ratio occlusions, and can help facilitate safety early warning, activity recognition, and productivity analysis.     

江苏省建筑工程安全生产事故浅析

基于江苏省建筑工程安全生产事故统计数据(2016年9月～2019年8月),从工程事故的数量、时间段、类型、地区几方面着手,分析建筑业安全生产事故发生率居高不下的原因,提出了工程安全事故负向循环形成机理,并从人员激励与管理、机械设备管控、材料质量把关、安全的施工方法、改变安全生产投入方式等方面提出相应对策,以期为工程项目...     

A Computer simulatin Interface

Abstract: Computer simulation can be a powerful analytical tool. Often, however, practitioners who know most about the problem domain may have limited knowledge of the simulation software. Recent research has concentrated on bridging the gap between the practitioner and the simulatin software by designing interfaces to make computer simulation more accessible for the infrequent or inexperienced user. Emerging artificial intelligence techniques sch as knowledge-based expert systems offer some promise for an interface. This paper describes a prototype interface which converts information on a process to be simulated into computer input for an existing computer simulation system. The elements of the interface include a domain knowledge processor using a small knowledge-based system to establish types of activities and resources based on minimal user input, a model logic builder to identify moves between activities and to establish network logic, and a computer code translator to convert the network into CYCLONE computer source code. Recommendations for future research are included.     

Application of genetic algorithm for modeling of dense packing of concrete aggregates

Sequential Packing Algorithm (SPA) was developed to model the dense packing of large assemblies of particulate materials (in the order of millions). These assemblies represent the real aggregate systems of portland cement or asphalt concrete. To improve the SPA performance, the program engine was updated with a genetic algorithm (GA) search module. Multi-cell packing procedures, fine adjustment of the algorithm’s parameters, as well as implementation of GA were effective tools to optimize the computational resources, to speed-up the SPA and to pack very large volumes of spherical entities.The developed algorithm generates and visualizes dense packings corresponding to concrete aggregates. The influence of model variables on the degree of packing and the corresponding distribution of particles was analyzed. Based on the simulation results, different particle size distributions of particulate materials are correlated to their packing degree. These packings agreed well with the standard requirements and available research data. The results of the research can be applied to the optimal proportioning of concrete mixtures.     

Substructure Identification and Health Monitoring Using Noisy Response Measurements Only

Abstract:   A probabilistic substructure identification and health monitoring methodology for linear systems is presented using measured response time histories only. A very large number of uncertain parameters have to be identified if one considers the updating of the entire structure. For identifiability, one then would require a very large number of sensors. Furthermore, even when such a large number of sensors are available, processing of vast amount of the corresponding data raises computational difficulties. In this article a substructuring approach is proposed, which allows for the identification and monitoring of some critical substructures only. The proposed method does not require any interface measurements and/or excitation measurements. No information regarding the stochastic model of the input is required. Specifically, the method does not require the response to be stationary and does not assume any knowledge of the parametric form of the spectral density of the input. Therefore, the method has very wide applicability. The proposed approach allows one to obtain not only the most probable values of the updated model parameters but also their associated uncertainties using only one set of response data. The probability of damage can be computed directly using data from the undamaged and possibly damaged structure. A hundred-story building model is used to illustrate the proposed method.     

A cooperative Internet-facilitated quality management environment for construction

The failure of construction projects can be minimized if quality is closely scrutinized and controlled throughout the design and construction stages. However, some organizations treat quality management as a routine exercise and pay little attention to the implementation of quality management systems. As a result, the expected improvement in quality at both project and organization levels has not been truly realized. Concurrent checking, reporting and auditing are necessary to improve the quality of an organization and its works. However, an immense amount of time and effort is required to report, input, retrieve, analyze and disseminate the data instantaneously. Therefore, an automated and user-friendly quality management system would be helpful. A web-based quality management system could be an effective tool for gathering, filtering, managing, assessing, and sharing quality data at project and corporate levels. The aim of this study is to examine the potential for applying the web-based techniques to collect, manage, assess and distribute quality-related data. A prototype system has been developed, and the results indicate that quality management data can be collected from various project participants to improve the transparency and efficiency of a construction project.