Forecasting construction industry demand,price and productivity in Singapore: the BoxJenkins approach

In academic research, the traditional Box-Jenkins approach is widely acknowledged as a benchmark technique for univariate methods because of its structured modelling basis and acceptable forecasting performance. This study examines the versatility of this approach by applying it to analyse and forecast three distinct variables of the construction industry, namely, tender price, construction demand and productivity, based on case studies of Singapore. In order to assess the adequacy of the Box-Jenkins approach to construction industry forecasting, the models derived are evaluated on their predictive accuracy based on out-of-sample forecasts. Two measures of accuracy are adopted, the root mean-square-error (RMSE) and the mean absolute percentage error (MAPE). The conclusive findings of the study include: (1) the prediction RMSE of all three models is consistently smaller than the model's standard error, implying the models' good predictive performance; (2) the prediction MAPE of all three models consistently falls within the general acceptable limit of 10%; and (3) among the three models, the most accurate is the demand model which has the lowest MAPE, followed by the price model and the productivity model.     

Forecasting construction demand: a vector error correction model with dummy variables

Modelling the level of demand for construction is vital in policy formulation and implementation as the construction industry plays an important role in a country’s economic development process. In construction economics, research efforts on construction demand modelling and forecasting are various, but few researchers have considered the impact of global economy events in construction demand modelling. An advanced multivariate modelling technique, namely the vector error correction (VEC) model with dummy variables, was adopted to predict demand in the Australian construction market. The results of prediction accuracy tests suggest that the general VEC model and the VEC model with dummy variables are both acceptable for forecasting construction economic indicators. However, the VEC model that considers external impacts achieves higher prediction accuracy than the general VEC model. The model estimates indicate that the growth in population, changes in national income, fluctuations in interest rates and changes in householder expenditure all play significant roles when explaining variations in construction demand. The VEC model with disturbances developed can serve as an experimentation using an advanced econometrical method which can be used to analyse the effect of specific events or factors on the construction market growth.     

Forecasting state retail sales: Econometric vs. time series models

The volume of retail sales is an important indicator of state economic activity and forms the base of the percentage sales tax. Accurate forecasting of the variable is of interest to fiscal authorities and private analysts as well. This paper compares the performance of two techniques in forecasting net taxable retail sales: the ARIMA time series model and a structural model which is representative of the econometric approach. Four measures of forecast accuracy are calculated and the relative merits of the techniques are discussed. The ARIMA model was found to perform better than the structural model during the evaluation period used in the analysis. However, it is also shown that a predictor formed from combining the ARIMA and structural model predictors may be superior to exclusive use of the ARIMA.     

Evaluating the performance of combining neural networks and genetic algorithms to forecast construction demand: the case of the Singapore residential sector

In recent years, forecasting demand for residential construction in Singapore has become more vital, since it is widely perceived that the next trough of the real estate cycle is approaching. This paper evaluates the use of a combination of neural networks (NNs) and genetic algorithms (GAs) to forecast residential construction demand in Singapore. Successful applications of NNs, especially in solving complex non-linear problems, have since stimulated interest in exploring the capabilities of other biological-based methods such as GAs, and in exploiting the synergy of these two techniques to create more problem-solving power. In the study, a basic NN model is used as a benchmark to gauge the performance of the combined NN-GA model. A relative measure of forecasting accuracy, known as the mean absolute percentage error (MAPE), is used for the comparison. The models are checked also for internal validity by allowing each to be trained twice and having a set of forecasts generated after each training. Both models are found to produce accurate forecasts, because their MAPE values consistently fall within the acceptable limit of 10%. However, the combined model out-performs the basis model remarkably by reducing the average MAPE from about 6% to a mere 1%. For each model, the marginal difference in the MAPE values (i.e., 0.5% for the NN model and 0.06% for the NN-GA model) of its two forecasts indicates consistency in performance, hence establishing internal validity as well. The findings reinforce the reliability of using NNs to model construction demand and reveal the benefit of combining NNs and GAs to produce more accurate models.     

Beam tests of composite steel-concrete members: A three-dimensional finite element model

Modern construction makes frequent use of composite steel-concrete beams for bridge and building applications. This paper describes a three-dimensional finite element model in which all components forming the composite member are modelled by means of solid elements. The proposed approach is developed using the commercial software Abaqus and is able to model the composite response without requiring information from push-out tests commonly performed to define the constitutive relationship for the shear connectors. All materials are assumed to behave in a nonlinear fashion. Contact between the elements is simulated using surface-to-surface and embedment techniques. The adequacy and accuracy of the proposed modelling approach are validated against experimental results available in the literature on simply-supported and continuous beam tests with both solid and composite slabs.     

Modelling the spatial distribution of a threatened butterfly: Impacts of scale and statistical technique

This paper compares the performance of five modelling methods in the prediction of a species distribution, using a data set describing the distribution of the threatened clouded apollo butterfly (Parnassius mnemosyne) in south-west Finland. The five statistical techniques included were: generalized linear models (GLM), generalized additive models (GAM), classification tree analysis (CTA), neural networks (ANN) and multiple adaptive regression splines (MARS). The accuracy of the models was examined at three spatial resolutions (1, 25 and 100 ha) by area under the curve (AUC) and kappa statistics. All five modelling techniques had a relatively high discrimination capacity for the occurrence of clouded apollo. Classification tree analysis provided the least robust model performance. The differences between the other methods were small, although GAM and MARS provided marginally the best stability and performance. The most accurate models were developed for the resolutions of 1 ha (highest AUC values) and 25 ha (highest kappa values) and the least accurate models for the resolution of 100 ha. Our work shows that modern modelling techniques can provide useful forecasts of species distributions in unsurveyed parts of landscapes and provide valuable contributions to conservation and management planning. However, the success of applying the new modelling tools can be influenced by the choice of statistical technique and especially of spatial resolution. In conclusion, small changes in the spatial scale may result in a clear decrease in the model performance and thus caution should be exercised when implementing the models and their predictions in practice.     

Forecasting construction manpower demand: A vector error correction model

Manpower demand forecast is an essential component to facilitate manpower planning. The purpose of this paper is to establish a long-run relationship between the aggregate demand for construction manpower and a group of inter-related economic variables including construction output, wage, material price, bank rate and productivity, based on dynamic econometric modelling techniques. The Johansen co-integration procedure and the likelihood ratio tests indicate the existence of a long-run and stable relationship among the variables. A vector error correction (VEC) model is then developed for forecasting purposes and is verified against various diagnostic statistical criteria. The construction output and labour productivity are found to be the most significant and sensitive factors determining the demand of construction manpower. The model and the factors identified may assist in predicting manpower demand trend and formulating policies, training and retraining programmes tailored to deal effectively with the industry's labour resource requirements in this critical sector of economy.     

Assessment of predictive uncertainty within the framework of water demand forecasting using the Model Conditional Processor (MCP)

This paper presents an application of the Model Conditional Processor (MCP), originally proposed by Todini (2008) within the hydrological framework, to assess the predictive uncertainty in water demand forecasting related to water distribution systems. The MCP enables us to assess the probability distribution of the future water demand conditional on the forecasts provided by two or more deterministic forecasting models. In the numerical application described here, where two years of hourly water demand data for a town in northern Italy are considered, two forecasting models are applied in order to forecast hourly water demands from 1 to 24 hours ahead: the first model has a modular structure comprising a periodic component which reflects the long-term effects and a persistence component which represents the short-term memory of the process; the latter is based on neural networks. The results highlight the effectiveness of the approach, provided that the data set used for the MCP parameterization is properly selected so as to be actually representative of the accuracy of the real-time water demand forecasting models.     

Forecasting the sales of precast concrete building products

Sales of precast concrete building products are influenced by the general demand for construction. This demand is subject to substantial fluctuations, caused by such diverse factors as capital spending by Government, the general strength of the economy, the demand for housing — which in turn reflects mortgage interest rates -and also by seasonal factors and weather. These are some of the difficulties associated with sales forecasting in the precast concrete industry. Sales forecasting is crucial managerial practice and its accuracy is vital for any company's business survival. A survey of the current forecasting and planning practices in the industry concluded that forecasting, especially for major product groups, is fairly basic and not reliable. Against this background, a forecasting model has been developed to analyse historical data and forecast demand for 12 months ahead. Two forecasting methods were applied to historical data of 12 groups of products of a major manufacturer. The results of the forecasting model were encouraging and more accurate than the manufacturer's existing forecasting system. The authors interviewed the firm's marketing and sales staff to identify the advantages and disadvantages of the forecasting system and identify the factors which affect sales and forecasting in general. Some tangible indication of the practical use of this work is the support given to this research project by staff of this company, at all levels. The work described in this paper is part of a more general computerized capacity planning system for the precast industry. This would be suitable for major companies, most of whom produce a large number of different products in a number of different manufacturing plants dispersed throughout the UK.     

Seismic vulnerability assessment of tilt-up concrete structures

This paper focuses on seismic vulnerability assessment for one-story tilt-up concrete structures. To capture the potential failure mechanisms, an analytical modelling approach using nonlinear properties is developed and verified with measured data from a shake table test documented in the literature. Nonlinear dynamic analyses using synthetic ground motions for Memphis, Tennessee, are performed to assess dynamic behaviour of the buildings. Then, probabilistic demand models for multiple limit states that represent potential failure mechanisms are developed with a Bayesian updating approach. These demand models are used in conjunction with appropriate capacity limits to develop fragility curves that provide a probabilistic measure of the seismic vulnerability of typical tilt-up concrete buildings. This study shows that the vulnerability of typical tilt-up structures in Mid-America is significant when seismic hazards are high. In addition, it is found that the aspect ratio of building geometry has a significant impact on the seismic performance and fragility estimates of tilt-up buildings.     

装配式建筑技能人才需求分析研究

在利用典型工程测算的人工消耗量下降幅度数据的基础上，选用线性回归模型与灰色 GM（1，1）模型分别进行建筑业从业人员数量预测，但鉴于两种模型的局限性，引入线性回归与灰色预测组合模型进行预测，并通过 3 种预测方法结果的对比，论证了组合模型预测结果的合理性。选取 2020 年与 2025 年两个典型时间点，预测建筑业从业人员需求量。考虑装配式建筑比例和现场作业人员比例，计算因发展装配式建筑减少现场作业人员的用工量，结合预测数据得出装配式建筑技能人才需求量，并根据测算结果提出了相应的建议。     

城市污水排放量预测模型研究

采用多元线性回归和BP人工神经网络两种方法分别建立城市污水排放量的预测模型，并进行实例计算验证。通过比较分析，发现BP神经网络的非线性映射能力能够更好地反映城市污水量与多个自变量间的复杂关系，具有较高的模拟精度且应用简便。     

基于混沌SVM与ARIMA的工程造价组合预测

为了解决工程造价预测的时效性问题,针对传统线性时间序列预测模型可靠性不高的缺点,引入混沌相空间重构和支持向量机技术,并将两者耦合组成一种非线性预测模型,再利用ARIMA在整体线性趋势预测方面的优越性,对非线性模型进行修正。混沌SVM和ARIMA预测构成组合模型的两个子过程,将两个子过程的预测结果综合平均即可得到最终预测结果。经实例计算,组合模型比最大Lyapunov指数、ARIMA和只将相空间重构与SVM进行耦合的方法拟合效果好,预测精度高,证明其的确具有线性趋势拟合和非线性波动拟合的双优势。     

'Rev Thomas Bayes: Get Ready to Wiggle' — Bayesian Modelling,Radiocarbon Wiggle-Matching,and the North Wing of Baguley Hall

Abstract

The principles of the Bayesian approach for modelling chronologies has been outlined in Bayliss (this volume). This paper applies these methods to a 'real world' problem of dating a phase of construction where the potential for treering dating is limited. The accuracy and precision that these new techniques can deliver is demonstrated. Bayesian modelling of radiocarbon dates and tree-ring dating independently estimate construction of the north wing in the third quarter of the fifteenth century.     

On the value of economic-demographic forecasts to local government

Both the relative cost and accuracy of alternative forecasting techniques should be considered by local decision makers. The contention of this paper is that estimates of the benefits of increased accuracy from more expensive economic-demographic forecasting models are necessary prior to making a rational choice over how much to spend on model construction. A case study using western North Dakota coal development and its economic-demographic impacts is used to illustrate a method for evaluating these benefits in the public sector.     

Applicability of the transfer function approach to forecasting employment levels in small regions

The problem investigated in this paper is that of forecasting employment levels (both raw and seasonally adjusted) in small regions, regions typically lacking data sufficient for the construction of simultaneous equation econometric models. The method employed is transfer function analysis using readily available national variables as drivers. The results suggest that the transfer function approach is capable of providing accurate forecasts of employment levels in small regions, forecast accuracy being measured in terms of mean absolute percentage errors and root mean squared errors. We conclude on the basis of this demonstrated accuracy that this approach is a viable method for forecasting selected variables in a small region context.Support for this research was provided in part by a grant from the Manufacturers Association of Erie. Special thanks to Mrs. Dana Moreira for data entry, and miscellaneous statistical tasks. Any errors remain the responsibility of the authors.     

Using extreme learning machines for short-term urban water demand forecasting

This study explores the ability of various machine learning methods to improve the accuracy of urban water demand forecasting for the city of Montreal (Canada). Artificial Neural Network (ANN), Support Vector Regression (SVR) and Extreme Learning Machine (ELM) models, in addition to a traditional model (Multiple linear regression, MLR) were developed to forecast urban water demand at lead times of 1 and 3 days. The use of models based on ELM in water demand forecasting has not previously been explored in much detail. Models were based on different combinations of the main input variables (e.g., daily maximum temperature, daily total precipitation and daily water demand), for which data were available for Montreal, Canada between 1999 and 2010. Based on the squared coefficient of determination, the root mean square error and an examination of the residuals, ELM models provided greater accuracy than MLR, ANN or SVR models in forecasting Montreal urban water demand for 1 day and 3 days ahead, and can be considered a promising method for short-term urban water demand forecasting.     

基于箱形图和隔离森林的施工人次数据处理与预测研究

针对工程项目进度和施工人员进场记录数据存在异常值的问题,提出一种基于箱形图和隔离森林的施工人次数据处理方法。将历史工程项目进行分类并计算各工程项目每人次的效率值,采用箱形图法剔除效率值异常的样本;结合各类型工程项目投资和投入施工人次的数据可视化情况,通过隔离森林隔离出属性值异常的数据点;采用剔除异常值后的样本作为线性回归预测模型的学习样本,使用测试集进行预测精度测试。并以某施工单位的实测数据为具体对象,根据所提算例结果显示,该方法能实现异常值的定位与分离,有效提高施工人次预测的精确度。     

An ARIMA-NARX hybrid model for forecasting urban water consumption (case study: Tehran metropolis)

ABSTRACT

In this research, an ARIMA-NARX (Autoregressive Integrated Moving Average-Nonlinear Auto-Regressive eXogenous) hybrid model is proposed to forecast daily Urban Water Consumption (UWC) for Tehran Metropolis. The linear and nonlinear component of the UWC was forecast by ARIMA as a linear forecasting model and the artificial neural network as a nonlinear forecasting model, respectively. An alternative hybrid model including sunshine hour in addition to the previous studies’ predictors (the minimum, maximum and average temperature, relative humidity and precipitation) was selected as the superior alternative model. Then, the performance of proposed model was compared with ARIMA and NARX models. The results showed that the hybrid model, which benefits from capability of both linear and nonlinear models, has a higher accuracy than the other two models in forecasting UWC. Therefore, the proposed hybrid model has better results in UWC forecasting and, as a consequence, better urban water reservoir management will be provided.     

Preservation initiatives for the truncated pyramid-shaped traditional housesof Siirt,Turkey

The traditional houses of the residential areas in and around Siirt in the southeastern region of Turkey are notable because of their interesting forms. The most successful examples are the "truncated pyramidal-shaped houses" that have existed for centuries and are unique to the locality; however, these forms are demolished rapidly. In this study, the structures of small- scale settlements in Siirt province and its environs have been evaluated to highlight the cultural aspects of the region. The subject of this research is to investigate the design principles of the rural houses constructed in vernacular style to raise international awareness of the need to preserve vernacular architecture. Surviving examples have been examined in terms of multiple case approach by their forms, spatial compositions, changes in their forms over time, their layouts in urban and rural areas, the construction techniques used to produce them from material production through implementation, the composition of the living space and its uses, their differences or similarities to other houses in the region and their aesthetic details. The research reveals that the design principles of traditional rural architecture offer the use of local material and techniques in a unique way that promote highlights to the future.