Detection of schedule delay risk of empirical construction projects

In Ethiopian construction projects, schedule delay risk is a predominant issue because it is not properly addressed. Although several studies have been focused on the various effects of risk in construction projects, limited efforts have been made to investigate the typical and the overall schedule delay risk. In this study, our aim is to detect the typical and overall schedule delay risk throughout the construction project lifecycle, which consists of the pre-construction, construction, and post-construction stages, and compare the stages with each other. Common criteria, sub-criteria, and attributes were developed for all alternatives for the purpose of making a risk decision. The methodology that was followed integrated the multiple-criteria decision-making (MCDM) model of fuzzy analytic hierarchy process comprehensive evaluation (FAHPCE) and the relative important index (RII). Data were collected from 77 participants, who were selected through purposive sampling from different contracting organizations in Ethiopian construction projects by means of questionnaires that were distributed to experienced experts. The findings showed that there is a typical delay risk either in the type or in the level of the different construction activities. Consequently, the most influenced alternative is the construction stage because of the high-risk responsibility, resource, and contract condition related criteria. The post-construction stage was the second most influenced stage because of the high-risk responsibility-related criteria. The pre-constructed stage was the least influenced stage that consist high-risk criteria of responsibility, resource, and contract condition related. These differences provided noteworthy information about risk mitigation in construction projects by identifying the exact risk level on specific activity to make appropriate decision.     

A quantitative risk assessment methodology for construction project

It is observed that most of the infrastructure projects fail to meet their cost and time constraints, which will lead to a low return on investment. The paper highlights that the present risk management tools and techniques do not provide an adequate basis for response selection in managing critical risks specific to infrastructure projects. This paper proposes a risk quantification methodology and demonstrates its application for an industrial construction project. A case study is used to present an application of the proposed risk management methodology to help organisations efficiently choose risk response strategy and allocate limited resources. The research adopts an integrated approach to prioritize risks using Group Technique for Order Preference by Similarity to Ideal Solution (GTOPSIS) and to quantify risks in terms of overall project delays using Judgemental Risk Analysis Process (JRAP), and Monte Carlo Simulation (MCS). A comparison between the results of qualitative risk analysis using GTOPSIS and quantitative risk analysis i.e., JRAP and MCS is presented. It is found that JRAP along with MCS could provide some powerful results which could help the management control project risks. The crux of this paper is that the risks are highly dependent on project schedule and the proposed methodology could give a better risk priority list because it considers slackness associated with the project activities. The analysis can help improve the understanding of implications of specific risk factors on project completion time and cost, while it attempts to quantify risks. In turn, this enables the project manager to devise a suitable strategy for risk response and mitigation.     

Delivery of pre‐stored videos for mobile clients with insufficient playback buffer and bounded delay guarantee

Abstract

A deterministic service model assisted by a sufficiently large playback buffer space can provide bounded delay guarantees for video packets and simplify network resource management. However, many popular mobile terminals do not have sufficient memory capacity for deterministic video services since the embedded memory is limited and needs to be shared by numerous software programs and masses of personal data. This paper improves the traditional deterministic modeling approach for delivering pre‐stored videos to mobile clients with QoS guarantees. The limitation of playback buffer space, the network delay jitter, the processing load of resource management, and the QoS guarantee are considered in the proposed mechanism. Some traffic smoothing operations are integrated into the proposed mechanism for reducing the playback buffer demand and data rate variation. This paper further proposes a smart video frame skip algorithm, originating at the sender for preventing possible overflow problems due to insufficient playback buffer space. The algorithm can determine the most suitable temporal range for skipping frames and prevent arbitrary discarding from inappropriate video frames such as I‐frames on the client side. Simulation results reveal that the proposed mechanism can effectively remedy situations of insufficient playback buffer space while still maintaining the advantages of deterministic services.     

Predicting Military Construction Project Time Outcomes Using Data Analytics

Abstract

Through its Department of Defense (DoD) agencies, and outside contractors, the USA invests billions of dollars each year in military construction (MILCON) projects. Although construction management expertise is gained and significant amount of data are collected from past projects, completing projects on time remains a challenge. This article uses data from 466 MILCON projects to identify key factors that influence project duration and provide a new model to predict project time outcomes. The model generates accurate results and serves as a useful tool in the early phases of a project life cycle. Another key contribution of this study is the employed methodology, which includes the use of available data, targeting of relevant parameters, and development of the predictive model. The contributed methodology is applicable outside of the MILCON domain with the appropriate data set and by targeting the relevant influential factors to create models to predict time outcomes of future projects.     

基于可视化仿真的隧道工程施工进度分析与控制研究

介绍了基于GIS的隧道工程施工进度可视化仿真方法仿真模型;提出了基于可视化仿真的隧道工程 施工进度风险分析及决策方法,使施工资源优化;论述了基于可视化仿真的隧道工程施工进度S型曲线实时管 理与控制方法;最后进行了实例研究。     

Risk Identification in the Chilean Tunneling Industry

This article aims to identify the main risk factors that are threats to meet the objectives of tunnel construction projects by using the Chilean construction industry as a case study and proposes a methodology to evaluate risk factors in this type of project. Surveys were used to generate probability and severity indexes to rank 36 risk factors. Data were collected from 14 companies that are members of the International Tunneling and Underground Space Association. The results indicate that the main risk factors that hinder achieving the tunneling construction project objectives are (1) inaccurate cost estimation or lack of detail in budget preparation, (2) unexpected geological conditions, (3) inaccurate deadline estimation or insufficient breakdown of the project schedule, (4) frequent malfunction of construction equipment, (5) excessive delays in approval processes by government entities, and (6) unexpected soil conditions and water table. Furthermore, a comprehensive literature review is provided to compare these results to international perspectives to confirm the existence of risks inherent to tunnel construction projects. This article concludes with revisiting the risk factors, which are common for the construction industry in general.     

Stability and resource allocation in project planning

The majority of resource-constrained project scheduling efforts assume perfect information about the scheduling problem to be solved and a static deterministic environment within which the precomputed baseline schedule is executed. In reality, project activities are subject to considerable uncertainty, which generally leads to numerous schedule disruptions. In this paper, we present a resource allocation model that protects a given baseline schedule against activity duration variability. A branch-and-bound algorithm is developed that solves the proposed resource allocation problem. We report on computational results obtained on a set of benchmark problems.     

An uncertain multidisciplinary design optimization method using interval convex models

This article proposes an uncertain multi-objective multidisciplinary design optimization methodology, which employs the interval model to represent the uncertainties of uncertain-but-bounded parameters. The interval number programming method is applied to transform each uncertain objective function into two deterministic objective functions, and a satisfaction degree of intervals is used to convert both the uncertain inequality and equality constraints to deterministic inequality constraints. In doing so, an unconstrained deterministic optimization problem will be constructed in association with the penalty function method. The design will be finally formulated as a nested three-loop optimization, a class of highly challenging problems in the area of engineering design optimization. An advanced hierarchical optimization scheme is developed to solve the proposed optimization problem based on the multidisciplinary feasible strategy, which is a well-studied method able to reduce the dimensions of multidisciplinary design optimization problems by using the design variables as independent optimization variables. In the hierarchical optimization system, the non-dominated sorting genetic algorithm II, sequential quadratic programming method and Gauss–Seidel iterative approach are applied to the outer, middle and inner loops of the optimization problem, respectively. Typical numerical examples are used to demonstrate the effectiveness of the proposed methodology.     

Dynamic single-machine scheduling under distributed decision-making

This paper presents a deterministic dynamic model for the single-machine scheduling problem. The model uses forecasts of future job arrivals with the current data to extract job interactions over time, updating the information on rolling basis. The model is implemented in a distributed structure with both the machine and the jobs involved in decision-making to create a schedule. The decision-making is modelled similar to an auction with a theoretical basis for problem decomposition, bid construction and bid evaluation. Numerical results indicate that the model outperforms other distributed implementations in both static and dynamic implementations for a wide range of single-machine scheduling problems.     

Reliability‐based topology optimization of structures under stress constraints

In this paper, we propose an approach for reliability‐based design optimization where a structure of minimum weight subject to reliability constraints on the effective stresses is sought. The reliability‐based topology optimization problem is formulated by using the performance measure approach, and the sequential optimization and reliability assessment method is employed. This strategy allows for decoupling the reliability‐based topology optimization problem into 2 steps, namely, deterministic topology optimization and reliability analysis. In particular, the deterministic structural optimization problem subject to stress constraints is addressed with an efficient methodology based on the topological derivative concept together with a level‐set domain representation method. The resulting algorithm is applied to some benchmark problems, showing the effectiveness of the proposed approach.     

Monotonic Metamodels for Deterministic Computer Experiments

In deterministic computer experiments, it is often known that the output is a monotonic function of some of the inputs. In these cases, a monotonic metamodel will tend to give more accurate and interpretable predictions with less prediction uncertainty than a nonmonotonic metamodel. The widely used Gaussian process (GP) models are not monotonic. A recent article in Biometrika offers a modification that projects GP sample paths onto the cone of monotonic functions. However, their approach does not account for the fact that the GP model is more informative about the true function at locations near design points than at locations far away. Moreover, a grid-based method is used, which is memory intensive and gives predictions only at grid points. This article proposes the weighted projection approach that more effectively uses information in the GP model together with two computational implementations. The first is isotonic regression on a grid while the second is projection onto a cone of monotone splines, which alleviates problems faced by a grid-based approach. Simulations show that the monotone B-spline metamodel gives particularly good results. Supplementary materials for this article are available online.     

Robust and reactive project scheduling: a review and classification of procedures

The vast majority of the research efforts in project scheduling over the past several years has concentrated on the development of exact and suboptimal procedures for the generation of a baseline schedule assuming complete information and a deterministic environment. During execution, however, projects may be the subject of considerable uncertainty, which may lead to numerous schedule disruptions. Predictive-reactive scheduling refers to the process where a baseline schedule is developed prior to the start of the project and updated if necessary during project execution. It is the objective of this paper to review possible procedures for the generation of proactive (robust) schedules, which are as well as possible protected against schedule disruptions, and for the deployment of reactive scheduling procedures that may be used to revise or re-optimize the baseline schedule when unexpected events occur. We also offer a framework that should allow project management to identify the proper scheduling methodology for different project scheduling environments. Finally, we survey the basics of critical chain scheduling and indicate in which environments it is useful.     

Scheduling Multi-Mode Resource-Constrained Projects Using Heuristic Rules Under Uncertainty Environment

Project scheduling is a key objective of many models and is the proposed method for project planning and management. Project scheduling problems depend on precedence relationships and resource constraints, in addition to some other limitations for achieving a subset of goals. Project scheduling problems are dependent on many limitations, including limitations of precedence relationships, resource constraints, and some other limitations for achieving a subset of goals. Deterministic project scheduling models consider all information about the scheduling problem such as activity durations and precedence relationships information resources available and required, which are known and stable during the implementation process. The concept of deterministic project scheduling conflicts with real situations, in which in many cases, some data on the activity' s durations of the project and the degree of availability of resources change or may have different modes and strategies during the process of project implementation for dealing with multi-mode conditions surrounded by projects and their activity durations. Scheduling the multi-mode resource-constrained project problem is an optimization problem whose minimum project duration subject to the availability of resources is of particular interest to us. We use the multi-mode resource allocation and scheduling model that takes into account the dynamicity features of all parameters, that is, the scheduling process must be flexible to dynamic environment features. In this paper, we propose five priority heuristic rules for scheduling multi-mode resource-constrained projects under dynamicity features for more realistic situations, in which we apply the proposed heuristic rules (PHR) for scheduling multi-mode resource-constrained projects. Five projects are considered test problems for the PHR. The obtained results rendered by these priority rules for the test problems are compared by the results obtained from 10 well-known heuristics rules rendered for the same test problems. The results in many cases of the proposed priority rules are very promising, where they achieve better scheduling dates in many test case problems and the same results for the others. The proposed model is based on the dynamic features for project topography.     

Resource Allocation of a Multi-Resource Project for Variable Resource Availabilities

An algorithm for resource allocation of multi-resource projects is described for the assumption of unlimited resource availabilities. The basis of the allocation is a binary enumeration of all feasible activity combinations, from which the daily optimum is obtained. Optimality is defined by the minimal 'schedule deviation,' which measures the weighted deviation squares between future resource loads and currently assigned resource levels. The algorithm simplifies both scheduling and project control, since the next periods schedule depends only on the current schedule status. Appropriate changes in levels for all resources over the project duration are consistent with the needs of project managers in practice.     

Application of the novel composite earth retaining structure method to urban excavations: a constructability analysis

ABSTRACT

The objective of this study is to propose and evaluate a novel composite earth retaining structure method to be applied in urban excavations in terms of construction safety, cost, and duration. The novel composite structure is a multipurpose structure, which may counteract uplift pressure acting on the foundation base, provide lateral support at ground level, and function as king posts during excavation. To evaluate the feasibility and advantages of utilizing the composite earth retaining structure in deep excavations, the case of a commercial building development in Taipei has been studied. In this project, the original excavation design employed top-down construction for the foundation and basement. Two alternative designs employing the composite earth retaining structure method were later proposed to the owner for evaluation and selection. The analyses of the two alternatives include a three-dimensional geotechnical finite element analysis using PLAXIS3D and an analysis of time using PROJECT 2013 as well as a study of cost impacts. The results from the analyses show that the novel method can be superior to the original design in terms of construction safety, project cost, and construction duration.     

Applying a hybrid simulated annealing and tabu search approach to non-permutation flowshop scheduling problems

Researchers have indicated that a permutation schedule can be improved by a non-permutation schedule in a flowshop with completion time-based criteria, such as makespan and total completion time. This study proposes a hybrid approach which draws on the advantages of simulated annealing and tabu search for the non-permutation flowshop scheduling problem, in which the objective function is the makespan of the schedule. To verify the effectiveness of the proposed hybrid approach, computational experiments are performed on a set of well-known non-permutation flowshop scheduling benchmark problems. The result shows that the performance of the hybrid approach is better than that of other approaches, including ant colony optimisation, simulated annealing, and tabu search. Further, the proposed approach found new upper bound values for all benchmark problems within a reasonable computational time.     

A Lagrangian modeling approach with the direct simulation Monte-Carlo method for inter-particle collisions in turbulent flow

A Lagrangian modeling approach, which combines the direct simulation Monte-Carlo (DSMC) method and a Reynolds-averaged Navier-Stokes model to account for inter-particle collisions and turbulence characteristics of the carrier fluid, respectively, is proposed. The wall-bounded turbulent particle-laden flows in which the experimental data are available are chosen as the test problems for demonstration. Results obtained with the deterministic method accounting for inter-particle collisions are used as a basis for validating the proposed stochastic Lagrangian model. Good agreement between the predictions obtained separately with the deterministic and DSMC methods is achieved. The benefit of saving computational expenditure when using the DSMC method becomes more remarkable than the deterministic method as the number of particles loaded in the flow is increased. In addition, the study demonstrates that τ_P/τ_C is a proper parameter to monitor the role of inter-particle collisions in the physical processes of particle-laden flows.     

Reliable design space and complete single-loop reliability-based design optimization

Reliability-based design optimization (RBDO) has been intensively studied due to its significance and its conceptual and mathematical complexity. This paper proposes a new method for RBDO on the basis of the concept of reliable design space (RDS), within which any design satisfies the reliability requirements. Therefore, a RBDO problem becomes a simple, deterministic optimization problem constrained by RDS rather than its deterministic feasible space. The RDS is found in this work by using the partial derivatives at the current design point as an approximation of the derivatives at its corresponding most probable point (MPP) on the limit state function. This work completely resolves the double loop in RBDO and turns RBDO into a simple optimization problem. Well-known problems from the literature are selected to illustrate the steps of the approach and for result comparison. Discussions will also be given on the limitation of the proposed method, which is shown to be a common limitation overlooked by the research community on RBDO.     

A novel shape function approach of dynamic load identification for the structures with interval uncertainty

Aiming at uncertain structures, a computational inverse approach is proposed to identify the dynamic load on the basis of the shape function method and interval analysis. The forward model for an uncertain structure is established through the relationship between the uncertain load vector and the assembly matrix of the uncertain responses of the shape function loads in each discrete element in time domain. The uncertainty is characterized by the interval with a closed bounded set of uncertain parameters. On the basis of interval analysis method, the load identification for uncertain structures can be transformed into two kinds of deterministic inverse problems, namely the deterministic dynamic load identification and the first order derivatives of the unknown load to each parameter both at the midpoints of the uncertain parameters. In order to eliminate the ill-posedness of inversion, the regularization method is adopted to solve the deterministic equations. Two numerical examples demonstrates the effectiveness of the proposed method, and example one also gives the identified result using Monte Carlo method to compare with that using the proposed method.     

重大水利水电工程施工实时控制关键技术及其工程应用

高心墙堆石坝和高混凝土坝的施工质量与施工进度控制是工程建设的重要技术问题。针对常规人工控制手段难以确保高心墙堆石坝施工质量,以及高强度连续施工下高混凝土坝施工进度难以实时控制等问题,提出了高心墙堆石坝填筑碾压质量实时监控技术、坝料上坝运输过程实时监控技术和施工质量动态信息PDA实时采集技术,实现了大坝填筑碾压全过程的全天候、精细化、在线实时监控;在建立高混凝土坝施工进度实时控制数学模型基础上,提出了施工进度实时预警与动态调整方法,为高混凝土坝施工进度的实时预测、适时预警、动态调整与优化提供了分析手段;提出了网络环境下数字大坝系统集成技术,研制开发了数字大坝系统,为大坝竣工验收、安全鉴定及运行管理提供了支撑平台。成果已应用于10余项重大工程,取得了显著的经济效益和社会效益。