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.     

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 construction tender price index in Hong Kong using vector error correction model

Reliable short‐ to medium‐term prediction of the tender price index (TPI) is crucial to construction stakeholders, and this has stimulated the interest of the research community to seek a more analytical method for TPI forecast. The purpose of this study is to establish an econometric model for accurately predicting the tender price movements based on a group of associated financial and macroeconomic variables. Applying Johansen’s method for multivariate cointegration analysis, the tender price was found to be cointegrated with the gross domestic product, construction output and building cost. A vector error correction (VEC) model imposing the cointegration restriction was then developed for the purpose of forecasting. The model was verified against various diagnostic statistical criteria and compared with the Box‐Jenkins and regression models. With a mean absolute percentage error for a three‐year ahead forecast at 2.9% level, the developed VEC model outperforms the Box‐Jenkins and regression models, and is proven to be efficient and reliable in forecasting the short‐ to medium‐term tender price movements. The model can assist estimators to predict the TPI pattern in advance, and it can also help the public sector in planning for the construction workload to improve the stability of the construction market. Although the VEC model developed focuses on the Hong Kong construction market, the econometric technique can be applied to modelling other economic variables.     

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 evolutionary algorithms in predicting non-deposition sediment transport

In this article, the densimetric Froude number of the flow is estimated using the parameters of volumetric sediment concentration (C_V), the relative depth of flow (d/R), dimensionless particle number (D_gr) and the overall sediment friction factor (λ_s). The particle swarm optimization (PSO) and imperialist competitive algorithms (ICA) were used to estimate the densimetric Froude number. To study the effects of sediment transport parameters on the densimetric Froude number, six different models are presented. The PSO algorithm with root mean square error (RMSE) = 0.014 and mean absolute percentage error (MAPE) = 5.1% present the results with a relatively good accuracy. The accuracy of the results presented for the selected model by the ICA algorithm is also in the form of RMSE = 0.007 and MAPE = 5.6%. Although both algorithms return good results in estimating the densimetric Froude number for the selected model, it should be mentioned that for all the six presented models ICA returns better results than PSO.     

Monthly water demand prediction using wavelet transform,first-order differencing and linear detrending techniques based on multilayer perceptron models

In this study, combined Discrete Wavelet Transform-Multilayer Perceptron (DWT-MP), combined First-Order Differencing-Multilayer Perceptron (FOD-MP) and combined Linear Detrending-Multilayer Perceptron (LD-MP) were developed and compared with stand-alone Multilayer Perceptron (MP) model for predicting monthly water consumption of Istanbul. The performance of these models were assessed by using coefficient of determination (R²), root mean square error (RMSE) and the Nash-Sutcliffe coefficient of efficiency (CE) as evaluation criteria. The study showed that DWT-MP could be used for forecasting the monthly water demand of Istanbul for only up to prediction lead-time of 3 months. However, FOD-MP was found to perform very well up to 12 months. It can be concluded from the results of the study that First-Order Differencing (FOD) is a reliable pre-processing technique for monthly water demand prediction.     

Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs

Prediction of machine performance is an essential step for planning, cost estimation and selection of excavation method to assure success of tunneling operation by hard rock TBMs. Penetration rate is a principal measure of TBM performance and is used to evaluate the feasibility of using a machine in a given ground condition and to predict TBM advance rate. In this study, a database of TBM field performance from two hard rock tunneling projects in Iran including Zagros lot 1B and 2 for a total length of 14.3 km has been used to assess applicability of various analysis methods for developing reliable predictive models. The first method used for this purpose was principal component analysis (PCA) which resulted in development of a set of new empirical equations. Also, two Soft computing techniques including adaptive neuro-fuzzy inference system (ANFIS) and support vector regression (SVR) have been employed for this purpose. As statistical indices, root mean square error (RMSE), correlation coefficient (R²), variance account for (VAF), and mean absolute percentage error (MAPE) were used to evaluate the efficiency of the developed artificial intelligence models for TBM performance prediction. The results of the analysis show that AI based methods can effectively be implemented for prediction of TBM performance. Moreover, it was concluded that performance of the SVR model is better than the ANFIS model. A high correlation was observed between predicted and measured TBM performance for the SVR model. This study shows the feasibility of using these systems and subsequent work is underway to expand the database of TBM field performance and use the aforementioned methods to develop a more comprehensive TBM performance prediction model.     

PSO优化ELM在火灾探测中的应用

为提高火灾探测精度,避免标准ELM陷入局部最优,本文基于火灾特征值CO浓度、烟雾浓度、温度建构了一种基于粒子群(PSO)优化极限学习机(ELM)的火灾探测模型,通过PSO优化ELM输入层与隐含层权值以及偏置,利用最优值进行极限学习机网络训练,将训练好的网络对测试样本进行预测并验证方法有效性.研究显示,PSO-ELM的均...     

Simulation-based construction productivity forecast using Neural-Network-Driven Fuzzy Reasoning

Fuzzy models and Artificial Neural Network (ANN) systems are two well-known areas of soft-computing that have significantly helped researchers with decision-making under uncertainties. Uncertainty, an ever-present factor in construction projects, has made such intelligent systems very attractive to the construction industry. Estimating the productivity of construction operations, as a basic element of project planning and control, has become a remarkable target for forecasting models. A glimpse into this interdisciplinary field of research exposes the need for a system, that (1) models the effect of qualitative and quantitative variables on construction productivity with an improved accuracy of estimation and (2) has the ability to deal with both crisp and fuzzy input variables in one single framework. Neural-Network-Driven Fuzzy Reasoning (NNDFR), as one of the hybrid intelligent structures, displays a great potential for modeling datasets among which clear clusters are recognizable. The weakness of NNDFR in auto-tuning the design of fuzzy membership functions along with this model's insufficient attention to the optimization of number of clusters has created an area for further research. In this paper, the parameters (fuzzifier and number of clusters) of the proposed system are optimized by using Genetic Algorithm (GA) to fine-tune the system for the highest possible level of accuracy that can be exploited for productivity estimation. The proposed model is also capable of dealing with a combination of crisp and fuzzy input variables by using a hybrid modeling approach based on the application of the alpha-cut technique. The developed model helps researchers and practitioners use historical data to forecast the productivity of construction operations with a level of accuracy greater than what could be offered by traditional techniques.     

An evaluation of the accuracy of the multiple regression approach in forecasting sectoral construction demand in Singapore

In the current state of research in construction demand modelling and forecasting there is a predominant use of the multiple regression approach, particularly the linear technique. Because of the popularity, it may be useful at this stage to gain an insight into the accuracy of the approach by comparing the forecasting performance of different forms of regression analysis. It is only through such formal means that the relative accuracy of different regression techniques can be assessed. In a case-study of modelling Singapore's residential, industrial and commercial construction demand, both linear and nonlinear regression techniques are applied. The techniques used include multiple linear regression (MLR), multiple log-linear regression (MLGR) and autoregressive nonlinear regression (ANLR). Quarterly time-series data over the period 1975–1994 are used. The objective is to evaluate the reliability of these techniques in modelling sectoral demand based on ex-post forecasting accuracy. Relative measures of forecasting accuracy dealing with percentage errors are used. It is found that the MLGR outperforms the other two methods in two of the three sectors examined by achieving the lowest mean absolute percentage error. The general conclusion is that nonlinear techniques are more accurate in representing the complex relationship between demand for construction and its various associated indicators. In addition to improved accuracy, the use of nonlinear forms also expands the scope of regression analysis.     

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.     

Prediction of rockhead using a hybrid N-XGBoost machine learning framework

The spatial information of rockhead is crucial for the design and construction of tunneling or underground excavation. Although the conventional site investigation methods (i.e. borehole drilling) could provide local engineering geological information, the accurate prediction of the rockhead position with limited borehole data is still challenging due to its spatial variation and great uncertainties involved. With the development of computer science, machine learning (ML) has been proved to be a promising way to avoid subjective judgments by human beings and to establish complex relationships with mega data automatically. However, few studies have been reported on the adoption of ML models for the prediction of the rockhead position. In this paper, we proposed a robust probabilistic ML model for predicting the rockhead distribution using the spatial geographic information. The framework of the natural gradient boosting (NGBoost) algorithm combined with the extreme gradient boosting (XGBoost) is used as the basic learner. The XGBoost model was also compared with some other ML models such as the gradient boosting regression tree (GBRT), the light gradient boosting machine (LightGBM), the multivariate linear regression (MLR), the artificial neural network (ANN), and the support vector machine (SVM). The results demonstrate that the XGBoost algorithm, the core algorithm of the probabilistic N-XGBoost model, outperformed the other conventional ML models with a coefficient of determination (R²) of 0.89 and a root mean squared error (RMSE) of 5.8 m for the prediction of rockhead position based on limited borehole data. The probabilistic N-XGBoost model not only achieved a higher prediction accuracy, but also provided a predictive estimation of the uncertainty. Thus, the proposed N-XGBoost probabilistic model has the potential to be used as a reliable and effective ML algorithm for the prediction of rockhead position in rock and geotechnical engineering.     

城市供水管网漏损时间的预测模型研究

城市供水企业迫切需要加强给水管网的漏损管理，以减少漏损水量和提高经济效益。在对华北某市供水管网漏损数据进行统计和分析的基础上，按照管段实际发生漏损次数分两种情况建立了供水管网漏损时间的预测模型，对漏损次数≤4次的管段采用基于SAS系统的多元线性回归方法，对漏损次数〉4次的管段则采用灰色预测方法。经实例验证，多元线性回归方法预测的平均相对误差为21％，灰色预测方法预测的平均相对误差〈6％，整套模型的精度可满足城市供水管网漏损宏观管理的需要，能够提高管网漏损防治的效率。     

Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network

In mining or construction projects, for exploitation of hard rock with high strength properties, blasting is frequently applied to breaking or moving them using high explosive energy. However, use of explosives may lead to the flyrock phenomenon. Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans, especially workers in the working sites. Thus, prediction of flyrock is of high importance. In this investigation, examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out. One hundred and fifty-two blasting events in three open-pit granite mines in Johor, Malaysia, were monitored to collect field data. The collected data include blasting parameters and rock mass properties. Site-specific weathering index (WI), geological strength index (GSI) and rock quality designation (RQD) are rock mass properties. Multi-layer perceptron (MLP), random forest (RF), support vector machine (SVM), and hybrid models including Harris Hawks optimization-based MLP (known as HHO-MLP) and whale optimization algorithm-based MLP (known as WOA-MLP) were developed. The performance of various models was assessed through various performance indices, including a10-index, coefficient of determination (R²), root mean squared error (RMSE), mean absolute percentage error (MAPE), variance accounted for (VAF), and root squared error (RSE). The a10-index values for MLP, RF, SVM, HHO-MLP and WOA-MLP are 0.953, 0.933, 0.937, 0.991 and 0.972, respectively. R² of HHO-MLP is 0.998, which achieved the best performance among all five machine learning (ML) models.     

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.     

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。     

Large metropolitan water demand forecasting using DAN2, FTDNN,and KNN models: A case study of the city of Tehran,Iran

Efficient operation of urban water systems necessitates accurate water demand forecasting. We present daily, weekly, and monthly water demand forecasting using dynamic artificial neural network (DAN2), focused time-delay neural network (FTDNN), and K-nearest neighbor (KNN) models for the city of Tehran. The daily model investigates whether partitioning weekdays into weekends and non-weekends can improve forecast results; it did not. The weekly model yielded good results by using the summation of the daily forecast values into their corresponding weeks. The monthly results showed that partitioning the year into high and low seasons can improve forecast accuracy. All three models offer very good results for water demand forecasting. DAN2, the best model, yielded forecasting accuracies of 96%, 99%, and 98%, for daily, weekly, and monthly models respectively.     

Forecasting short-term air passenger demand using big data from search engine queries

Forecasting air passenger demand is a critical aspect of formulating appropriate operation plans in airport operation. Airport operation not only requires long-term demand forecasting to establish long-term plans, but also short-term demand forecasting for more immediate concerns. Most airports forecast their short-term passenger demand based on experience, which provides limited forecasting accuracy, depending on the level of expertise. For accurate short-term forecasting independent of the level of expertise, it is necessary to create reliable short-term forecasting models and to reflect short-term fluctuations in air passenger demand. This study aims to develop a forecasting model of short-term air passenger demand using big data from search queries to identify these short-term fluctuations. The suggested forecasting model presents an average forecast error of 5.3% and indicates that an increase of approximately 195,000 air passengers is to be expected 8 months later, as the key query frequencies increase by 0.1%.     

Estimation of convergence of a high-speed railway tunnel in weak rocks using an adaptive neuro-fuzzy inference system(ANFIS) approach

Estimation of tunnel diameter convergence is a very important issue for tunneling construction,especially when the new Austrian tunneling method(NATM) is adopted.For this purpose,a systematic convergence measurement is usually implemented to adjust the design during the whole construction,and consequently deadly hazards can be prevented.In this study,a new fuzzy model capable of predicting the diameter convergences of a high-speed railway tunnel was developed on the basis of adaptive neuro-fuzzy inference system(ANFIS) approach.The proposed model used more than 1 000 datasets collected from two different tunnels,i.e.Daguan tunnel No.2 and Yaojia tunnel No.1,which are part of a tunnel located in Hunan Province,China.Six Takagi-Sugeno fuzzy inference systems were constructed by using subtractive clustering method.The data obtained from Daguan tunnel No.2 were used for model training,while the data from Yaojia tunnel No.1 were employed to evaluate the performance of the model.The input parameters include surrounding rock masses(SRM) rating index,ground engineering conditions(GEC) rating index,tunnel overburden(H),rock density(?),distance between monitoring station and working face(D),and elapsed time(T).The model’s performance was assessed by the variance account for(VAF),root mean square error(RMSE),mean absolute percentage error(MAPE) as well as the coefficient of determination(R2) between measured and predicted data as recommended by many researchers.The results showed excellent prediction accuracy and it was suggested that the proposed model can be used to estimate the tunnel convergence and convergence velocity.     

Development of room temperature and relative humidity linear parametric models for an open office using BMS data

This study investigates Box-Jenkins (BJ), autoregressive with external inputs (ARX), autoregressive moving average with external inputs (ARMAX) and output error (OE) models to identify the thermal behaviour of an office positioned in a modern commercial building in London. These models can all be potentially used for improving the performance of the thermal environment control system. External and internal climate data, recorded over the summer, autumn and winter seasons, were used to build and validate the models. The paper demonstrates the potential of using linear parametric models to predict room temperature and relative humidity for different time scales (30 min or 2 h ahead). The prediction performance is evaluated using the criteria of goodness of fit, coefficient of determination, mean absolute error and mean squared error between predicted model output and real measurements. The results demonstrate that all models provide reasonably good predictions but the BJ model outperforms the ARMAX and ARX models.