SPICE: a business process diagnostic tool for construction projects

The construction sector is under growing pressure to increase productivity and improve quality, most notably in reports by Latham (1994, Constructing the Team, HMSO, London) and Egan (1998, Rethinking Construction, HMSO, London). A major problem for construction companies is the lack of project predictability. One method of increasing predictability and delivering increased customer value is through the systematic management of construction processes. However, the industry has no methodological mechanism to assess process capability and prioritise process improvements. Standardized Process Improvement for Construction Enterprises (SPICE) is a research project that is attempting to develop a stepwise process improvement framework for the construction industry, utilizing experience from the software industry, and in particular the Capability Maturity Model (CMM), which has resulted in significant productivity improvements in the software industry. This paper introduces SPICE concepts and presents the results from two case studies conducted on design and build projects. These studies have provided further in-sight into the relevance and accuracy of the framework, as well as its value for the construction sector.

Artefact-oriented business process modelling: an ontological dependency approach

Business process modelling can help an organisation better understand and improve its business processes. Most business process modelling methods adopt a task- or activity-based approach to identifying business processes. Within our work, we use activity theory to categorise elements within organisations as being either human beings, activities or artefacts. Due to the direct relationship between these three elements, an artefact-oriented approach to organisation analysis emerges. Organisational semiotics highlights the ontological dependency between affordances within an organisation. We analyse the ontological dependency between organisational elements, and therefore produce the ontology chart for artefact-oriented business process modelling in order to clarify the relationship between the elements of an organisation. Furthermore, we adopt the techniques from semantic analysis and norm analysis, of organisational semiotics, to develop the artefact-oriented method for business process modelling. The proposed method provides a novel perspective for identifying and analysing business processes, as well as agents and artefacts, as the artefact-oriented perspective demonstrates the fundamental flow of an organisation. The modelling results enable an organisation to understand and model its processes from an artefact perspective, viewing an organisation as a network of artefacts. The information and practice captured and stored in artefact can also be shared and reused between organisations that produce similar artefacts.

Using TerraSAR-X data for improved urban flood model validation.

Urban flood inundation models require considerable data for their parameterisation, calibration and validation. TerraSAR-X should be suitable for urban flood detection because of its high resolution in stripmap/spotlight modes. The paper describes ongoing work on a project to assess how well TerraSAR-X can detect flooded regions in urban areas, and how well these can constrain the parameters of an urban flood model. The study uses a TerraSAR-X image of a 1-in-150 year flood near Tewkesbury, UK , in 2007, for which contemporaneous aerial photography exists for validation. The DLR SETES SAR simulator was used in conjunction with LiDAR data to estimate regions of the image in which water would not be visible due to shadow or layover caused by buildings and vegetation. An algorithm for the delineation of flood water in urban areas is described, together with its validation using the aerial photographs.

Aerosol optical depths over North Africa: 2. Modeling and model validation

An operational dust forecasting model is developed by including the Met Office Hadley Centre climate model dust parameterization scheme, within a Met Office regional numerical weather prediction (NWP) model. The model includes parameterizations for dust uplift, dust transport, and dust deposition in six discrete size bins and provides diagnostics such as the aerosol optical depth. The results are compared against surface and satellite remote sensing measurements and against in situ measurements from the Facility for Atmospheric Airborne Measurements for a case study when a strong dust event was forecast. Comparisons are also performed against satellite and surface instrumentation for the entire month of August. The case study shows that this Saharan dust NWP model can provide very good guidance of dust events, as much as 42 h ahead. The analysis of monthly data suggests that the mean and variability in the dust model is also well represented.

EnvironalPlanning: an analytical network process model for environmentally conscious construction planning

Although the construction pollution index has been put forward and proved to be an efficient approach to reducing or mitigating pollution level during the construction planning stage, the problem of how to select the best construction plan based on distinguishing the degree of its potential adverse environmental impacts is still a research task. This paper first reviews environmental issues and their characteristics in construction, which are critical factors in evaluating potential adverse impacts of a construction plan. These environmental characteristics are then used to structure two decision models for environmental-conscious construction planning by using an analytic network process (ANP), including a complicated model and a simplified model. The two ANP models are combined and called the EnvironalPlanning system, which is applied to evaluate potential adverse environmental impacts of alternative construction plans.

On the potential of assimilating meteorological analyses in a global climate model for the purpose of model validation

A simple four-dimensional assimilation technique, called Newtonian relaxation, has been applied to the Hamburg climate model (ECHAM), to enable comparison of model output with observations for short periods of time. The prognostic model variables vorticity, divergence, temperature, and surface pressure have been relaxed toward European Center for Medium-Range Weather Forecasts (ECMWF) global meteorological analyses. Several experiments have been carried out, in which the values of the relaxation coefficients have been varied to find out which values are most usable for our purpose. To be able to use the method for validation of model physics or chemistry, good agreement of the model simulated mass and wind field is required. In addition, the model physics should not be disturbed too strongly by the relaxation forcing itself. Both aspects have been investigated. Good agreement with basic observed quantities, like wind, temperature, and pressure is obtained for most simulations in the extratropics. Derived variables, like precipitation and evaporation, have been compared with ECMWF forecasts and observations. Agreement for these variables is smaller than for the basic observed quantities. Nevertheless, considerable improvement is obtained relative to a control run without assimilation. Differences between tropics and extratropics are smaller than for the basic observed quantities. Results also show that precipitation and evaporation are affected by a sort of continuous spin-up which is introduced by the relaxation: the bias (ECMWF-ECHAM) is increasing with increasing relaxation forcing. In agreement with this result we found that with increasing relaxation forcing the vertical exchange of tracers by turbulent boundary layer mixing and, in a lesser extent, by convection, is reduced.

A strategy for process-oriented validation of coupled chemistry–climate models

Evaluating CCMs with the presented framework will increase our confidence in predictions of stratospheric ozone change.

Fast identification algorithms for Gaussian process model

A class identification algorithms is introduced for Gaussian process(GP)models.The fundamental approach is to propose a new kernel function which leads to a covariance matrix with low rank,a property that is consequently exploited for computational efficiency for both model parameter estimation and model predictions.The objective of either maximizing the marginal likelihood or the Kullback–Leibler (K–L) divergence between the estimated output probability density function(pdf)and the true pdf has been used as respective cost functions.For each cost function,an efficient coordinate descent algorithm is proposed to estimate the kernel parameters using a one dimensional derivative free search, and noise variance using a fast gradient descent algorithm. Numerical examples are included to demonstrate the effectiveness of the new identification approaches.

The first real-time worldwide ionospheric predictions network: an advance in support of spaceborne experimentation, on-line model validation, and space weather

We report on the first realtime ionospheric predictions network and its capabilities to ingest a global database and forecast F-layer characteristics and "in situ" electron densities along the track of an orbiting spacecraft. A global network of ionosonde stations reported around-the-clock observations of F-region heights and densities, and an on-line library of models provided forecasting capabilities. Each model was tested against the incoming data; relative accuracies were intercompared to determine the best overall fit to the prevailing conditions; and the best-fit model was used to predict ionospheric conditions on an orbit-to-orbit basis for the 12-hour period following a twice-daily model test and validation procedure. It was found that the best-fit model often provided averaged (i.e., climatologically-based) accuracies better than 5% in predicting the heights and critical frequencies of the F-region peaks in the latitudinal domain of the TSS-1R flight path. There was a sharp contrast however, in model-measurement comparisons involving predictions of actual, unaveraged, along-track densities at the 295 km orbital altitude of TSS-1R In this case, extrema in the first-principle models varied by as much as an order of magnitude in density predictions, and the best-fit models were found to disagree with the "in situ" observations of Ne by as much as 140%. The discrepancies are interpreted as a manifestation of difficulties in accurately and self-consistently modeling the external controls of solar and magnetospheric inputs and the spatial and temporal variabilities in electric fields, thermospheric winds, plasmaspheric fluxes, and chemistry.

The BALTIMOS (BALTEX Integrated Model System) fi eld experiments: A comprehensive atmospheric boundary layer data set for model validation over the open and ice- covered Baltic Sea

A comprehensive atmospheric boundary layer (ABL) data set was collected in eight fi eld experiments (two during each season) over open water and sea ice in the Baltic Sea during 1998–2001 with the primary objective to validate the coupled atmospheric- ice-ocean-land surface model BALTIMOS (BALTEX Integrated Model System). Measurements were taken by aircraft, ships and surface stations and cover the mean and turbulent structure of the ABL including turbulent fl uxes, radiation fl uxes, and cloud conditions. Measurement examples of the spatial variability of the ABL over the ice edge zone and of the stable ABL over open water demonstrate the wide range of ABL conditions collected and the strength of the data set which can also be used to validate other regional models.

A design of e-procurement infrastructure model for public service sector

Procurement is one of major business operations in public service sector. The advance of information and communication technology (ICT) pushes this business operation to increase its efficiency and foster collaborations between the organization and its suppliers. This leads to a shift from the traditional procurement transactions to an e-procurement paradigm. Such change impacts on business process, information management and decision making. E-procurement involves various stakeholders who engage in activities based on different social and cultural practices. Therefore, a design of e-procurement system may involve complex situations analysis. This paper describes an approach of using the problem articulation method to support such analysis. This approach is applied to a case study from UAE.

Validation of the FALL3D ash dispersion model using observations of the 2010 Eyjafjallajökull volcanic ash clouds

During April-May 2010 volcanic ash clouds from the Icelandic Eyjafjallajökull volcano reached Europe causing an unprecedented disruption of the EUR/NAT region airspace. Civil aviation authorities banned all flight operations because of the threat posed by volcanic ash to modern turbine aircraft. New quantitative airborne ash mass concentration thresholds, still under discussion, were adopted for discerning regions contaminated by ash. This has implications for ash dispersal models routinely used to forecast the evolution of ash clouds. In this new context, quantitative model validation and assessment of the accuracies of current state-of-the-art models is of paramount importance. The passage of volcanic ash clouds over central Europe, a territory hosting a dense network of meteorological and air quality observatories, generated a quantity of observations unusual for volcanic clouds. From the ground, the cloud was observed by aerosol lidars, lidar ceilometers, sun photometers, other remote-sensing instru- ments and in-situ collectors. From the air, sondes and multiple aircraft measurements also took extremely valuable in-situ and remote-sensing measurements. These measurements constitute an excellent database for model validation. Here we validate the FALL3D ash dispersal model by comparing model results with ground and airplane-based measurements obtained during the initial 14e23 April 2010 Eyjafjallajökull explosive phase. We run the model at high spatial resolution using as input hourly- averaged observed heights of the eruption column and the total grain size distribution reconstructed from field observations. Model results are then compared against remote ground-based and in-situ aircraft-based measurements, including lidar ceilometers from the German Meteorological Service, aerosol lidars and sun photometers from EARLINET and AERONET networks, and flight missions of the German DLR Falcon aircraft. We find good quantitative agreement, with an error similar to the spread in the observations (however depending on the method used to estimate mass eruption rate) for both airborne and ground mass concentration. Such verification results help us understand and constrain the accuracy and reliability of ash transport models and it is of enormous relevance for designing future operational mitigation strategies at Volcanic Ash Advisory Centers.

A semi-distributed ntegrated itrogen model for multiple source assessment in tchments (INCA): Part I — model structure and process equations

A new model has been developed for assessing multiple sources of nitrogen in catchments. The model (INCA) is process based and uses reaction kinetic equations to simulate the principal mechanisms operating. The model allows for plant uptake, surface and sub-surface pathways and can simulate up to six land uses simultaneously. The model can be applied to catchment as a semi-distributed simulation and has an inbuilt multi-reach structure for river systems. Sources of nitrogen can be from atmospheric deposition, from the terrestrial environment (e.g. agriculture, leakage from forest systems etc.), from urban areas or from direct discharges via sewage or intensive farm units. The model is a daily simulation model and can provide information in the form of time series at key sites, or as profiles down river systems or as statistical distributions. The process model is described and in a companion paper the model is applied to the River Tywi catchment in South Wales and the Great Ouse in Bedfordshire.

Arable crop rotations under abiotic stress: a dynamic economic model

Break crops and multi-crop rotations are common in arable farm management, and the soil quality inherited from a previous crop is one of the parameters that determine the gross margin that is achieved with a given crop from a given parcel of land. In previous work we developed a dynamic economic model to calculate the potential yield and gross margin of a set of crops grown in a selection of typical rotation scenarios, and we reported use of the model to calculate coexistence costs for GM maize grown in a crop rotation. The model predicts economic effects of pest and weed pressures in monthly time steps. Validation of the model in respect of specific traits is proceeding as data from trials with novel crop varieties is published. Alongside this aspect of the validation process, we are able to incorporate data representing the economic impact of abiotic stresses on conventional crops, and then use the model to predict the cumulative gross margin achievable from a sequence of conventional crops grown at varying levels of abiotic stress. We report new progress with this aspect of model validation. In this paper, we report the further development of the model to take account of abiotic stress arising from drought, flood, heat or frost; such stresses being introduced in addition to variable pest and weed pressure. The main purpose is to assess the economic incentive for arable farmers to adopt novel crop varieties having multiple ‘stacked’ traits introduced by means of various biotechnological tools available to crop breeders.