The past several years have witnessed an increase in research on the nonlinear analysis of the structures made from reinforced concrete. Several mathematical models were created to analyze the behavior of concrete and the reinforcements. Factors including inelasticity, time dependence, cracking and the interactive effects between reinforcement and concrete were considered. The crushing of the concrete in compression and the cracking of the concrete in tension are the two common failure modes of concrete. Material models were introduced for analyzing the behavior of unconfined concrete, and a possible constitutive model was the concrete damage plasticity (CDP) model. Due to the complexity of the CDP theory, the procedure was simplified and a simplified concrete damage plasticity (SCDP) model was developed in this paper. The SCDP model was further characterized in tabular forms to simulate the behavior of unconfined concrete. The parameters of the concrete damage plasticity model, including a damage parameter, strain hardening/softening rules, and certain other elements, were presented through the tables shown in the paper for concrete grades B20, B30, B40 and B50. All the aspects were discussed in relation to the effective application of a finite element method in the analysis. Finally, a simply supported prestressed beam was analyzed with respect to four different concrete grades through the finite element program. The results showed that the proposed model had good correlation with prior arts and empirical formulations.

PurposeConstruction labour productivity is of great interest to practitioners and researchers because it affects project cost and time overrun. This paper evaluates and ranks the importance, frequency and severity of project delay factors that affect the construction labour productivity for Malaysian residential projects.Design/methodology/approachA total of 100 respondents consisting of 70 contractors, 11 developers and 19 consultants participated in this study. The respondents were asked to indicate how important each item of a list of 50 project related factors was to construction labour productivity. The data were then subjected to the calculation of importat indices which enabled the factors to be ranked.FindingsThe five most important factors identified by them were: material shortage at site; non‐payment to suppliers causing the stoppage of material delivery to site; change order by consultants; late issuance of construction drawing by consultants; and incapability of contractors' site management to organise site activities. On the other hand, the five most frequent factors were: material shortage at project site; non‐payment to suppliers causing the stoppage of material delivery to site; late issuance of progress payment by the client to main contractor; lack of foreign and local workers in the market; and coordination problem between the main contractor and subcontractor.Originality/valueThe inferences drawn from this study could be used by the project managers to take account of these factors at an early stage, hence minimising the time and cost overrun.

The major compounds in honey are carbohydrates such as monosaccharides and disaccharides. The same compounds are found in cane-sugar concentrates. Unfortunately when sugar concentrate is added to honey, laboratory assessments are found to be ineffective in detecting this adulteration. Unlike tracing heavy metals in honey, sugar adulterated honey is much trickier and harder to detect, and traditionally it has been very challenging to come up with a suitable method to prove the presence of adulterants in honey products. This paper proposes a combination of array sensing and multi-modality sensor fusion that can effectively discriminate the samples not only based on the compounds present in the sample but also mimic the way humans perceive flavours and aromas. Conversely, analytical instruments are based on chemical separations which may alter the properties of the volatiles or flavours of a particular honey. The present work is focused on classifying 18 samples of different honeys, sugar syrups and adulterated samples using data fusion of electronic nose (e-nose) and electronic tongue (e-tongue) measurements. Each group of samples was evaluated separately by the e-nose and e-tongue. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to separately discriminate monofloral honey from sugar syrup, and polyfloral honey from sugar and adulterated samples using the e-nose and e-tongue. The e-nose was observed to give better separation compared to e-tongue assessment, particularly when LDA was applied. However, when all samples were combined in one classification analysis, neither PCA nor LDA were able to discriminate between honeys of different floral origins, sugar syrup and adulterated samples. By applying a sensor fusion technique, the classification for the 18 different samples was improved. Significant improvement was observed using PCA, while LDA not only improved the discrimination but also gave better classification. An improvement in performance was also observed using a Probabilistic Neural Network classifier when the e-nose and e-tongue data were fused.

The thermal analysis of roller compacted concrete dams (RCC) plays an important role in their design and construction. This paper focuses on the application and verification of a twodimensional finite element code developed for the thermal and structural analysis of RCC dams. The Kinta RCC gravity dam, which is the first RCC dam in Malaysia, has been taken for the purpose of verification of the finite element code. The dam is 78 m in height and still under construction. The actual climatic conditions and thermal properties of the materials were considered in the analysis. The predicted temperatures obtained from the finite element code that was developed are found to be in good agreement with actual temperatures measured in the field using thermocouples installed within the dam body.

that stored soil water per unit depth is worth four to five times as much as warm-season rainfall. Rooting front advance and rooting depth are factors to considerSunflower is not highly drought tolerant but comin selecting a crop for effective use of stored soil water. Our objective monly is grown as a dryland crop and often produces was to compare rooting front development (advance rate and maxisatisfactorily when other crops are damaged seriously mum depth) and water depletion depths of grain sorghum [Sorghum (Robinson, 1978). The drought-tolerant nature of sunbicolor (L.) Moench] and sunflower (Helianthus annuus L.) in a field experiment. The study was near Manhattan, KS on Eudora silt loam flower has been attributed to its extensive root system, soil (coarse-silty, mixed, superactive, mesic Fluventic Hapludolls).which can extract water and nutrients to soil depths of Root development was quantified through multiple field samplings 3 m (Jones and Johnson, 1983). Sunflower's ability to and the core-break method. Water content was measured to 3.05 m extract more water from deep soil layers than most other by neutron attenuation. Rooting front depths of the two crops were crops plays an important role in productivity and in similar from emergence to mid-June [0 to 20 d after emergence survival under low rainfall (Connor and Hall, 1997). (DAE)], but from late June, sunflower roots were deeper than sor-Sunflower's ability to produce under low rainfall also ghum roots. Maximum rooting depths were 1.85 m in sorghum and is aided by a crop water use requirement that is relatively 2.49 m in sunflower (means of 2 yr). From 20 to 60 DAE, rooting low for initial seed yield, reported to be 128 mm by front depth increased 25 and 41 mm d Ϫ1 in sorghum and sunflower, Nielsen (1998). respectively. From 60 to 90 DAE, rooting front depth increased 8Central to the concept of crop selection to more effecand 6 mm d Ϫ1 in sorghum and sunflower, respectively. Net seasonal water depletion was greater in sunflower plots than in sorghum plots

Wireless sensor network technology holds great promise for application in a wide range of areas, both to monitor and control a variety of systems. Whilst the use of sensors has found natural applications within the manufacturing sector, application in agriculture is still in its infancy and has been used largely to only monitor the environment. The use of technology in the agricultural sector to improve crop yield, quality and to foster sustainable agriculture can be regarded as one of the areas that will provide food security to the expanding global population and to mitigate food shortage precipitated by unpredictable weather patterns. This paper presents a Wireless Sensor Network coverage measurements in a mixed crop farming, modeling and deployment architecture taking into account the different signal propagation scenarios and attenuation factor of different crops. Most importantly, the paper presents wireless sensor network deployment architecture for a mixed crop trial field over an area of 54,432m 2 , which is 4% of the total area to be covered by the final network.

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In recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis) maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. However, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both PCA and LDA were unable to discriminate the age difference of the Harumanis mangoes. Instead of six different groups, only four were observed using the LDA, while PCA showed only two distinct groups. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved. However, no significant improvement was observed using PCA with data fusion technique. Further work using a hybrid LDA-Competitive Learning Neural Network was performed to validate the fusion technique and classify the samples. It was found that the LDA-CLNN was also improved significantly when data fusion was applied.