Introducing an innovation promotion model for construction projects

Purpose - This research is on investigating and modeling the complex dynamics of innovation enablers in construction projects aiming to develop a framework identifying actions for clients to promote innovation. Design/methodology/approach – Through a comprehensive literature review, a conceptual model was previously derived to explain the client-driven enablers in promoting innovation. This was tested using the data from 131 Australian construction projects. Statistical analysis was conducted on the data using factor analysis and correlation analysis to test the model, which was validated using the case study approach. The testing and validating aspects are explained in this paper. Findings – The constructs of the recommended model are idea harnessing; relationship enhancement; incentivization; and project team fitness. Research limitations/implications - The difficulty of analyzing the complex dynamics happening within projects in relation to innovation has been a barrier to progress research in this area. The introduction of this model would pave the way for researchers to explore this area with ease. Originality/value – As revealed in the detailed literature review undertaken, this is the first time that a comprehensive study has been conducted to identify client-led innovation enablers for construction projects. The results would benefit industry practitioners to achieve enhanced project outcomes in construction projects through innovation

Rotary Driven Pipe Piles for a 14-Story Building in New York City

Rotary driven pipe piles are a unique solution for foundation construction in constrained urban areas. These piles consist of a closedend, steel casing with sacrificial drill tip. The casing and drill tip are rotated into the ground using a fixed-mast drill rig. Three hundred sixty two 12.75-inch diameter, rotary driven pipe piles were installed to support a 14-story building in the upper east side of Manhattan. The soils consisted of uncontrolled fill, organic silts, and peat over stiff, saturated, varved silts and clays. A novel mathematical relationship between capacity, installation crowd, and torque was used to develop initial pile installation criteria. A simple discrete element model showed the piles would exhibited considerable freeze. This was verified by successive torque readings over time. Four compression, one tension, and one lateral load test were performed. Torque measurements, load test results, and installation observations are presented. All piles performed exceptionally well during the test program in terms of total pile head deflection. Overall, field measurements matched predictions. Careful coordination and communication between all parties allowed pile installation to proceed rapidly; the foundation was completed on schedule and budget. Each pile was fitted with a geothermal conduit loop to create ‘energy piles’, which will be instrumented for future case study research

Integration and test plans for complex manufacturing systems

The integration and test phases that are part of the development and manufacturing of complex manufacturing systems are costly and time consuming. As time-to-market is becoming increasingly important, it is crucial to keep these phases as short as possible, whilemaintaining system quality. This is especially true for the time-to-market driven semiconductor industry and for companies providing manufacturing systems to this industry such as ASML, a provider of lithographic systems. The Tangram research project has the goal, to shorten integration and test time by a model-based integration and test approach. The Ph.D. project described in this thesis is part of the Tangram project. To achieve integration and test time reduction, we developed three methods that each solve one of the following three integration and test problems: • Construction of an optimal test plan with respect to time, cost and/or quality. • Construction of an optimal integration plan with respect to time, cost and/or quality. • Construction of an optimal integration and test plan with respect to time, cost and/or quality. The test plan optimization method consists of two steps. The first step is the definition of a model of the test problem. This model consists of tests that can be performed with associated cost and duration, possible faults that can reside in the system with associated fault probability and impact (importance), and the relation between the tests and the possible faults, also denoted as the test coverage for each possible fault. The second step consists of calculating the optimal test plan based on this test model given an objective function and possible constraints on time, cost and/or risk, which is a parameter for the quality of the system. By constructing an AND/OR graph of the problem, where AND nodes denote tests and OR nodes denote system states represented by the ambiguous faults, all possible test sequences of this problem are obtained. An algorithm selects the best solution from this AND/OR graph. This solution is a set of test sequences, where the test sequence that is followed depends on the outcome (pass/fail) of the previous tests. The integration plan optimization method consists of the same two steps as the test plan optimization method. The integration model consists of modules with their development times, interfaces that denote which modules can be integrated with each other, and test phases with their durations. Furthermore, the model consists of the relation between test phases and modules indicating which modules should be integrated before the test phase may start. Also for this problem, an AND/OR graph is constructed. The AND nodes denote integration actions and the OR nodes denote system states represented by the modules that are integrated. An algorithm selects the optimal solution from this AND/OR graph. The optimal solution has the shortest possible integration time. The solution is a tree of integration actions and test phases indicating, for each module, the sequence of integration actions and test phases. The integration and test planning method is a combination of the two previously mentioned methods and also consists of two steps. The integration and test model is a combination of the test model and the integration model, with additional relations between modules and possible faults describing in which modules these possible faults are inserted. During the construction of the integration AND/OR graph, a test AND/OR graph is constructed for each integration AND node. This test AND/OR graph represents the test phase that is performed after that integration action. The start and stop moments of these test phases are determined by the test phase positioning strategy. We developed several test phase positioning strategies according to which test phases are started, for example periodically or when a certain risk level is reached. We applied the methods developed to industrial case studies in ASML to investigate the benefits of these methods. From a case study performed in the manufacturing of lithographic machines, we learned that the duration of a test phase may be reduced by approximately 20% when using the test plan optimization method instead of creating a test plan manually. From a case study performed in the integration phase of a new prototype system, we learned that using the integration planning method may reduce integration time by almost 10% compared to a manually created integration plan. From a case study performed in the integration and test phase of a software system, we learned that the final test phase durationmay be reduced by approximately 40% when applying a risk-based test phase positioning strategy instead of the currently used periodic test phase positioning strategy. We conclude that the methods developed can be used to construct optimal integration and test plans. These optimal integration and test plans are often more efficient than manually created plans, which reduces the time-to-market of a complex system while maintaining the same final system quality. Future research should indicate how to incorporate the methods developed in the complete integration and test process, and how to obtain the information needed to create the integration and test models

Characterising the Actual Thermal Performance of Buildings: Current Results of Common Exercises Performed in the Framework of the IEA EBC Annex 58-Project

AbstractSeveral studies have shown that actual thermal performance of buildings after construction may deviate significantly from that anticipated at design stage. As a result, there is growing interest in full scale testing of components and whole buildings. The IEA EBC Annex 58-project ‘Reliable Building Energy Performance Characterisation Based on Full Scale Dynamic Measurements’ is developing the necessary knowledge and tools to achieve reliable in-situ dynamic testing and data analysis methods that can be used to characterise the actual thermal performance and energy efficiency of building components and whole buildings. The research within this project is driven by case studies. As a first simple case, an experiment on testing and data analysis is performed on a round robin test box. This test box can be seen as a scale model of a building, built by one of the participants, with fabric properties unknown to all other participants. Full scale measurements have been performed on the test box in different countries under real climatic conditions. The obtained dynamic data are distributed to all participants who have to try to characterise the thermal performance of the test box's fabric based on the provided data.This paper presents the first results obtained on the round robin exper ment. It is shown how different techniques can be used to characterise the thermal performance of the test box, ranging from a simple stationary analysis to advanced dynamic data analysis methods

Modeling and performance evaluation of stealthy false data injection attacks on smart grid in the presence of corrupted measurements

The false data injection (FDI) attack cannot be detected by the traditional anomaly detection techniques used in the energy system state estimators. In this paper, we demonstrate how FDI attacks can be constructed blindly, i.e., without system knowledge, including topological connectivity and line reactance information. Our analysis reveals that existing FDI attacks become detectable (consequently unsuccessful) by the state estimator if the data contains grossly corrupted measurements such as device malfunction and communication errors. The proposed sparse optimization based stealthy attacks construction strategy overcomes this limitation by separating the gross errors from the measurement matrix. Extensive theoretical modeling and experimental evaluation show that the proposed technique performs more stealthily (has less relative error) and efficiently (fast enough to maintain time requirement) compared to other methods on IEEE benchmark test systems.Comment: Keywords: Smart grid, False data injection, Blind attack, Principal component analysis (PCA), Journal of Computer and System Sciences, Elsevier, 201

Data-Driven Model Reduction for the Bayesian Solution of Inverse Problems

One of the major challenges in the Bayesian solution of inverse problems governed by partial differential equations (PDEs) is the computational cost of repeatedly evaluating numerical PDE models, as required by Markov chain Monte Carlo (MCMC) methods for posterior sampling. This paper proposes a data-driven projection-based model reduction technique to reduce this computational cost. The proposed technique has two distinctive features. First, the model reduction strategy is tailored to inverse problems: the snapshots used to construct the reduced-order model are computed adaptively from the posterior distribution. Posterior exploration and model reduction are thus pursued simultaneously. Second, to avoid repeated evaluations of the full-scale numerical model as in a standard MCMC method, we couple the full-scale model and the reduced-order model together in the MCMC algorithm. This maintains accurate inference while reducing its overall computational cost. In numerical experiments considering steady-state flow in a porous medium, the data-driven reduced-order model achieves better accuracy than a reduced-order model constructed using the classical approach. It also improves posterior sampling efficiency by several orders of magnitude compared to a standard MCMC method

Learning to Recognize Actions from Limited Training Examples Using a Recurrent Spiking Neural Model

A fundamental challenge in machine learning today is to build a model that can learn from few examples. Here, we describe a reservoir based spiking neural model for learning to recognize actions with a limited number of labeled videos. First, we propose a novel encoding, inspired by how microsaccades influence visual perception, to extract spike information from raw video data while preserving the temporal correlation across different frames. Using this encoding, we show that the reservoir generalizes its rich dynamical activity toward signature action/movements enabling it to learn from few training examples. We evaluate our approach on the UCF-101 dataset. Our experiments demonstrate that our proposed reservoir achieves 81.3%/87% Top-1/Top-5 accuracy, respectively, on the 101-class data while requiring just 8 video examples per class for training. Our results establish a new benchmark for action recognition from limited video examples for spiking neural models while yielding competetive accuracy with respect to state-of-the-art non-spiking neural models.Comment: 13 figures (includes supplementary information

Industry-driven innovative system development for the construction industry: The DIVERCITY project

Collaborative working has become possible using the innovative integrated systems in construction as many activities are performed globally with stakeholders situated in various locations. The Integrated VR based information systems can bind the fragmentation and provide communication and collaboration between the distributed stakeholders n various locations. The development of these technologies is vital for the uptake of these systems by the construction industry. This paper starts by emphasising the importance of construction IT research and reviews some future research directions in this area. In particular, the paper explores how virtual prototyping can improve the productivity and effectiveness of construction projects, and presents DIVERCITY, which is th as a case study of the research in virtual prototyping. Besides, the paper explores the requirements engineering of the DIVERCITY project. DIVERCITY has large and evolving requirements, which considered the perspectives of multiple stakeholders, such as clients, architects and contractors. However, practitioners are often unsure of the detail of how virtual environments would support the construction process, and how to overcome some barriers to the introduction of new technologies. This complicates the requirements engineering process