Performance of public private partnerships : an evolutionary perspective

PPPs are held to be a powerful way of mobilising private finance and resources to deliver public infrastructure. Theoretically, research into procurement has begun to acknowledge difficulties with the classification and assessment of different types of procurement, particularly those which do not sufficiently acknowledge variety within specific types of procurement methods. This paper advances a theoretical framework based on an evolutionary economic conceptualisation of a routine, which can accommodate the variety evident in procurement projects, in particular PPPs. The paper tests how the various elements of a PPP, as advanced in the theoretical framework, affect performance across 10 case studies. It concludes, that a limited number of elements of a PPP affect their performance, and provides strong evidence for the theoretical model advanced in this paper.

Monitoring the Performance of Complex Projects from Multiple Perspectives over Multiple Time Frames

When complex projects go wrong they can go horribly wrong with severe financial consequences. We are undertaking research to develop leading performance indicators for complex projects, metrics to provide early warning of potential difficulties. The assessment of success of complex projects can be made by a range of stakeholders over different time scales, against different levels of project results: the project’s outputs at the end of the project; the project’s outcomes in the months following project completion; and the project’s impact in the years following completion. We aim to identify leading performance indicators, which may include both success criteria and success factors, and which can be measured by the project team during project delivery to forecast success as assessed by key stakeholders in the days, months and years following the project. The hope is the leading performance indicators will act as alarm bells to show if a project is diverting from plan so early corrective action can be taken. It may be that different combinations of the leading performance indicators will be appropriate depending on the nature of project complexity. In this paper we develop a new model of project success, whereby success is assessed by different stakeholders over different time frames against different levels of project results. We then relate this to measurements that can be taken during project delivery. A methodology is described to evaluate the early parts of this model. Its implications and limitations are described. This paper describes work in progress.

Structural health monitoring of bridges using acoustic emission technology and signal processing techniques

Structural health monitoring (SHM) is the term applied to the procedure of monitoring a structure’s performance, assessing its condition and carrying out appropriate retrofitting so that it performs reliably, safely and efficiently. Bridges form an important part of a nation’s infrastructure. They deteriorate due to age and changing load patterns and hence early detection of damage helps in prolonging the lives and preventing catastrophic failures. Monitoring of bridges has been traditionally done by means of visual inspection. With recent developments in sensor technology and availability of advanced computing resources, newer techniques have emerged for SHM. Acoustic emission (AE) is one such technology that is attracting attention of engineers and researchers all around the world. This paper discusses the use of AE technology in health monitoring of bridge structures, with a special focus on analysis of recorded data. AE waves are stress waves generated by mechanical deformation of material and can be recorded by means of sensors attached to the surface of the structure. Analysis of the AE signals provides vital information regarding the nature of the source of emission. Signal processing of the AE waveform data can be carried out in several ways and is predominantly based on time and frequency domains. Short time Fourier transform and wavelet analysis have proved to be superior alternatives to traditional frequency based analysis in extracting information from recorded waveform. Some of the preliminary results of the application of these analysis tools in signal processing of recorded AE data will be presented in this paper.

Does chemometrics enhance the performance of electroanalysis?

This review explores the question whether chemometrics methods enhance the performance of electroanalytical methods. Electroanalysis has long benefited from the well-established techniques such as potentiometric titrations, polarography and voltammetry, and the more novel ones such as electronic tongues and noses, which have enlarged the scope of applications. The electroanalytical methods have been improved with the application of chemometrics for simultaneous quantitative prediction of analytes or qualitative resolution of complex overlapping responses. Typical methods include partial least squares (PLS), artificial neural networks (ANNs), and multiple curve resolution methods (MCR-ALS, N-PLS and PARAFAC). This review aims to provide the practising analyst with a broad guide to electroanalytical applications supported by chemometrics. In this context, after a general consideration of the use of a number of electroanalytical techniques with the aid of chemometrics methods, several overviews follow with each one focusing on an important field of application such as food, pharmaceuticals, pesticides and the environment. The growth of chemometrics in conjunction with electronic tongue and nose sensors is highlighted, and this is followed by an overview of the use of chemometrics for the resolution of complicated profiles for qualitative identification of analytes, especially with the use of the MCR-ALS methodology. Finally, the performance of electroanalytical methods is compared with that of some spectrophotometric procedures on the basis of figures-of-merit. This showed that electroanalytical methods can perform as well as the spectrophotometric ones. PLS-1 appears to be the method of practical choice if the %relative prediction error of not, vert, similar±10% is acceptable.

Fingerprinting of complex mixtures with the use of high performance liquid chromatography, inductively coupled plasma atomic emission spectroscopy and chemometrics

The molecular and metal profile fingerprints were obtained from a complex substance, Atractylis chinensis DC—a traditional Chinese medicine (TCM), with the use of the high performance liquid chromatography (HPLC) and inductively coupled plasma atomic emission spectroscopy (ICP-AES) techniques. This substance was used in this work as an example of a complex biological material, which has found application as a TCM. Such TCM samples are traditionally processed by the Bran, Cut, Fried and Swill methods, and were collected from five provinces in China. The data matrices obtained from the two types of analysis produced two principal component biplots, which showed that the HPLC fingerprint data were discriminated on the basis of the methods for processing the raw TCM, while the metal analysis grouped according to the geographical origin. When the two data matrices were combined into a one two-way matrix, the resulting biplot showed a clear separation on the basis of the HPLC fingerprints. Importantly, within each different grouping the objects separated according to their geographical origin, and they ranked approximately in the same order in each group. This result suggested that by using such an approach, it is possible to derive improved characterisation of the complex TCM materials on the basis of the two kinds of analytical data. In addition, two supervised pattern recognition methods, K-nearest neighbors (KNNs) method, and linear discriminant analysis (LDA), were successfully applied to the individual data matrices—thus, supporting the PCA approach.

Internal audit quality, audit committee independence, growth opportunities and firm performance

This study explores whether the relation between internal audit quality and firm performance is associated with firm characteristics of information asymmetry and uncertainty (growth opportunities) and certain governance controls (audit committee effectiveness). The results from this preliminary study of 60 Malaysian companies show that the association between internal audit quality and firm performance is stronger for firms with high growth opportunities and that this positive association is weakened by increasing audit committee independence. These findings demonstrate the internal auditors conflicting roles and question the governance recommendations that require all members of the audit committee to be non-executive directors.

Examining the use of bid information in predicting the contractor’s performance

Purpose – The purpose of this paper is to examine the use of bid information, including both price and non-price factors in predicting the bidder’s performance. Design/methodology/approach – The practice of the industry was first reviewed. Data on bid evaluation and performance records of the successful bids were then obtained from the Hong Kong Housing Department, the largest housing provider in Hong Kong. This was followed by the development of a radial basis function (RBF) neural network based performance prediction model. Findings – It is found that public clients are more conscientious and include non-price factors in their bid evaluation equations. With the input variables used the information is available at the time of the bid and the output variable is the project performance score recorded during work in progress achieved by the successful bidder. It was found that past project performance score is the most sensitive input variable in predicting future performance. Research limitations/implications – The paper shows the inadequacy of using price alone for bid award criterion. The need for a systemic performance evaluation is also highlighted, as this information is highly instrumental for subsequent bid evaluations. The caveat for this study is that the prediction model was developed based on data obtained from one single source. Originality/value – The value of the paper is in the use of an RBF neural network as the prediction tool because it can model non-linear function. This capability avoids tedious ‘‘trial and error’’ in deciding the number of hidden layers to be used in the network model. Keywords Hong Kong, Construction industry, Neural nets, Modelling, Bid offer spreads Paper type Research paper

Measuring quality : how does this improve construction performance?

The paper charts the history and development of the Hong Kong Housing Department (HKHD) Performance Assessment Scoring System (PASS) from 1990 to the present day and examines its effect on facilitating change to the quality of construction work of building contractors engaged in the production of public sector housing projects Hong Kong. The paper builds partly on empirical research carried out by the author as part of a doctoral thesis from 2000 to 2005, on experiential knowledge and also on some relevant case studies. The outcomes from this earlier research and validation of PASS based on results derived from the system since the research was originally undertaken are of benefit to practitioners and academics working and studying in the areas of performance assessment and organisational culture and change. The conclusions presented in the paper further underpin the connection established in previous research between strong organisational culture and project and corporate success. Organisational culture was measured using a survey instrument originally developed by Denison and Neale (1994), adapted for the environment of the study, and corporate success was measured by the PASS system mentioned above. The major results of the original study indicate that there is significant linkage between strong organisational cultures and business success and the detailed findings were that, (1) strong organisational culture was positively associated a high level of company effectiveness, (2) a high level of company effectiveness was positively associated with the cultural traits of ‘consistency’, ‘adaptability’ and ‘mission’, and (3) a high level of company effectiveness was positively associated with the combined cultural traits represented by the dimensions of ‘external focus’ and ‘stable culture’. Several opportunities to take forward this research have been identified, including extending the study to other countries and also longitudinally re-evaluating some of the original case studies to ascertain how organisational cultures have changed or further developed in relation to the changing construction climate in Hong Kong.

Predicting project performance through neural networks

Successful project delivery of construction projects depends on many factors. With regard to the construction of a facility, selecting a competent contractor for the job is paramount. As such, various approaches have been advanced to facilitate tender award decisions. Essentially, this type of decision involves the prediction of a bidderÕs performance based on information available at the tender stage. A neural network based prediction model was developed and presented in this paper. Project data for the study were obtained from the Hong Kong Housing Department. Information from the tender reports was used as input variables and performance records of the successful bidder during construction were used as output variables. It was found that the networks for the prediction of performance scores for Works gave the highest hit rate. In addition, the two most sensitive input variables toward such prediction are ‘‘Difference between Estimate’’ and ‘‘Difference between the next closest bid’’. Both input variables are price related, thus suggesting the importance of tender sufficiency for the assurance of quality production.

The self-Googling phenomenon : investigating the performance of personalized information resources

This paper investigates self–Googling through the monitoring of search engine activities of users and adds to the few quantitative studies on this topic already in existence. We explore this phenomenon by answering the following questions: To what extent is the self–Googling visible in the usage of search engines; is any significant difference measurable between queries related to self–Googling and generic search queries; to what extent do self–Googling search requests match the selected personalised Web pages? To address these questions we explore the theory of narcissism in order to help define self–Googling and present the results from a 14–month online experiment using Google search engine usage data.

Theater 38.3 : Censorship and Performance

Censorship and Performance, edited by Tom Sellar, examines the politics of censorship, and continuing contests over the ‘right’ to claim theatrical and cultural stages for controversial forms of social and self representation, at the start of the twenty-first century. In bringing this collection together, Sellar has taken a broad-based approach to the concept of censorship in theatrical performance—and, indeed, to the concept of theatrical performance itself. Sellar and his contributors clearly accept that surveillance, suppression and restriction of specific forms of representation is a complex, culturally specific phenomenon. In this sense, Censorship and Performance addresses direct political control over content, as well as thornier arguments about media controversy, moral panic, and the politics of self-censorship amongst artists and arts organisations.

Interaction between visual status, driver age and distracters on daytime driving performance

This study investigated the effects of visual status, driver age and the presence of secondary distracter tasks on driving performance. Twenty young (M = 26.8 years) and 19 old (M = 70.2 years) participants drove around a closed-road circuit under three visual (normal, simulated cataracts, blur) and three distracter conditions (none, visual, auditory). Simulated visual impairment, increased driver age and the presence of a distracter task detrimentally affected all measures of driving performance except gap judgments and lane keeping. Significant interaction effects were evident between visual status, age and distracters; simulated cataracts had the most negative impact on performance in the presence of visual distracters and a more negative impact for older drivers. The implications of these findings for driving behaviour and acquisition of driving-related information for people with common visual impairments are discussed

Seismic performance of brick infilled RC frame structures in low and medium rise buildings in Bhutan

The construction of reinforced concrete buildings with unreinforced infill is common practice even in seismically active country such as Bhutan, which is located in high seismic region of Eastern Himalaya. All buildings constructed prior 1998 were constructed without seismic provisions while those constructed after this period adopted seismic codes of neighbouring country, India. However, the codes have limited information on the design of infilled structures besides having differences in architectural requirements which may compound the structural problems. Although the influence of infill on the reinforced concrete framed structures is known, the present seismic codes do not consider it due to the lack of sufficient information. Time history analyses were performed to study the influence of infill on the performance of concrete framed structures. Important parameters were considered and the results presented in a manner that can be used by practitioners. The results show that the influence of infill on the structural performance is significant. The structural responses such as fundamental period, roof displacement, inter-storey drift ratio, stresses in infill wall and structural member forces of beams and column generally reduce, with incorporation of infill wall. The structures designed and constructed with or without seismic provision perform in a similar manner if the infills of high strength are used.

The X-Factor and its relationship to golfing performance

It is often postulated that an increased hip to shoulder differential angle (`X-Factor') during the early downswing better utilises the stretch-shorten cycle and improves golf performance. The current study aims to examine the potential relationship between the X-Factor and performance during the tee-shot. Seven golfers with handicaps between 0 and 10 strokes comprised the low-handicap group, whilst the high-handicap group consisted of eight golfers with handicaps between 11 and 20 strokes. The golfers performed 20 drives and three-dimensional kinematic data were used to quantify hip and shoulder rotation and the subsequent X-Factor. Compared with the low-handicap group, the high-handicap golfers tended to demonstrate greater hip rotation at the top of the backswing and recorded reduced maximum X-Factor values. The inconsistencies evident in the literature may suggest that a universal method of measuring rotational angles during the golf swing would be beneficial for future studies, particularly when considering potential injury.

Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.